MiniMax-M2: Open-Source AI for Agentic & Coding Workflows

Introduction

The world of large-scale AI has been locked in an arms race defined by a simple, brutal metric: more is better. More parameters, more data, more compute. This relentless pursuit of scale has given us astonishingly capable models, but it has also created a massive barrier, leaving a trail of eye-watering API bills and latency bottlenecks. For the developers building the next generation of AI-powered tools-especially agentic systems that can independently plan, act, and verify tasks-this cost-per-thought is a critical bottleneck. The dream of an AI agent running, testing, and fixing code all on its own is great until you get that invoice for a million iterations of its thought process.

This is the exact challenge that the new MiniMax-M2 model is built to solve. It changes the goalpost from biggest to smartest. Recent updates have confirmed that MiniMax-M2 is radically cheaper and faster than its proprietary competitors, being a direct answer to the industry's scalability problem by proving top-tier agentic performance and cost-effective deployment are not necessarily mutually exclusive.

Development and Contributors

MiniMax-M2 was developed by MiniMax, an AI startup based in Shanghai. MiniMax has quickly emerged as an important participant in the AI space due to significant venture backing from industry giants such as Alibaba and Tencent. The motto for this model, a Mini model built for Max coding & agentic workflows, properly sums up its design philosophy: it's compact, efficient, but built for maximum, real-world developer and agentic utility.

What is MiniMax-M2? 

MiniMax-M2 is a compact, fast, and cost-effective Mixture-of-Experts AI model. From the architectural point of view, MiniMax-M2 is an ingenious piece of engineering. The sparse activation design allows it to have the vast knowledge of a huge model while retaining the speed and low operational cost of a much smaller one.

Key Features of MiniMax-M2

The design of MiniMax-M2 results in a suite of characteristics that are not just astounding but savagely usable to a developer or software architect.

• Optimized Balance of Intelligence and Cost: The core design in the model achieves a unique balance between intelligence, speed, and cost. It renders elite intelligence ranked #1 in open-source intelligence usable for complex tasks without added computational burden to thus prove that you need nor accept less performance for unit economics.
  
  
  source - https://www.minimax.io/news/minimax-m2
• Radical Unit Economics: This is M2's killer feature. It designed for low latency, low cost, and high throughput. Its API cost is quoted to be about 8% of Claude 3.5 Sonnet, a direct top-tier competitor.
• High-Speed Inference: M2 not only saves you budget, it means you will save time. Its efficient design possessing 10B active parameters achieves nearly double the speed as compared to Claude 3.5 Sonnet while inferring a response to a query. This is crucially important to provide fast feedback loops - often necessary in coding or agentic tasks.
• Sophisticated Tool Use in Agentic Mode: The model is an interleaved thinking model that is designed to provide sophisticated executing end-to-end tool use while in a lean form factor that is part of their computing-powered trend for volunteer organizations to make enlighten, not destroy.

Use Cases of MiniMax-M2

These attributes enable a set of use cases that have never been financially viable or technically feasible with any other model.

• Autonomous PR Fixer in CI/CD: As soon as a developer creates a Pull Request that fails unit tests, an instance of the model is triggered. The M2 Agent then engages in a real-time multi-file coding-run-fix loop to diagnose, edit, and validate the code. Because M2 is so fast and low-cost, it can automatically self-correct, all within the CI window for a PR allowing for a rapid, fully-automated test-validated evolution of a code-base before a human reviewer has even reached for the PR.
• Live, Conversational IDE Debugging Partner: A developer is using an IDE extension powered by M2, when they encounter a bug. M2 invokes its interleaved thinking architecture, and streams its reasoning—including its plan, its hypotheses, and results from calling tools directly into an IDE side-panel in real-time. The developer receives the benefit of a non-blocking, low-latency real-time assistant embedded in the IDE, that shows its steps as it searches documentation, or simulates the code execution.
• Scalable, Deep-Search Agent for Compliance Audits: A financial services firm needs thousands of concurrent agents to conduct evaluations for regulatory compliance using xbench-DeepSearch and BrowseComp-style evaluations across massive document repositories and the public web. M2’s low active parameter count provides for high throughput and low server memory utilization, and the result is that a thousand-agent fleet of active, traceable, self-recovering agents is a cost-effective proposition for constant, wide-scale monitoring.
• Cost-Optimized, Multi-Turn RAG Agent Pipeline: A company drops its high-cost RAG pipeline based on an expensive proprietary model and substitutes MiniMax-M2, which takes advantage of compatibility with the Anthropic/OpenAI APIs. This architecture permits a hot-swap migration of the pipeline, with code changes constrained to input-output, and allows the company to realize a massive downward shift in costs, all while retaining top-tier long-horizon tool-calling performance suitable for document retrieval and summarization.
• Adaptive Command-Line (CLI) Agent: A developer is typing away at a terminal, working through a Terminal-Bench-style task that involves complicated shell commands, reading and manipulating files, and validating constructed execution payloads in dollars. M2, running either locally or via a low-latency API, is functioning as an advanced command-line agent that, through planning and executing complex toolchains in the shell and code runners, provides instant intelligent automation directly configurable for the working environment.

How MiniMax-M2 Works (Architecture) 

The magic of MiniMax-M2 lies not in its 230 billion parameters, but rather in its innovatively optimized architecture based on a Mixture-of-Experts (MoE) model that is built around a low activation size. Instead of using all 230 billion parameters on every single request, there is a routing capability that only activates the most relevant 10 billion expert parameters for carry out the requested task. This makes MiniMax-M2 more cost-effective, in principle, but it is also a key component of the design.

The architecture was designed to map perfectly to the common agentic workflow of plan → act → verify. By activating a low number of parameter activations, MiniMax-M2 achieves a high degree of responsiveness per component of that workflow, while significantly reducing the compute overhead required for each step. This enables truly fast feedback loops necessary for important agentic tasks such as a compile-run-test loop for coding or browse-retrieve-cite chain for research. The model directly reflects this agentic architecture in its output, and purposefully models the 'thinking' content you receive between ... tags when it reasons and considers its output. The <think>... </Think> tags are not simply metadata, but play a central role in proper utilization of the model. As the model is expecting you to 'keep' this thinking or reasoning content in the conversation history, to delete it will negatively impact the model's performance.

Performance Evaluation with Other Models

MiniMax-M2 has comprehensive real-world evaluations to its credit, and it is particularly strong compared with other models in the agentic coding domain. The model really shines on the SWE-bench Verified benchmark, a test which gauges an AI's ability to solve real-world software engineering tasks using multi-turn interactions, planning, and tool usage. It scored high on this hard test with 69.4, while a model such as OpenAI's gpt-oss-120b, though an extremely strong competition coder with a rating of 2622 Elo, does not have a comparable score on this particular agentic workflow benchmark, underlining the specialized focus of M2.

source -  https://github.com/MiniMax-AI/MiniMax-M2

This is even more evident within the Tau-Bench for agentic tool use, where MiniMax-M2 scored an impressive 77.2. This significantly outperforms gpt-oss-120b, which scored only 67.8% on that same benchmark. This head-to-head win on a complex tool-use test underlines the advanced capability of M2 for planning and executing complex, long-horizon toolchains across environments like the shell, browser, and code runners.

source -  https://github.com/MiniMax-AI/MiniMax-M2

The model finally achieved a total of 61 on the Artificial Analysis (AA) Intelligence Benchmark that combines 10 challenging tasks and leads it to the #1 position among the open-source models globally. The model also demonstrated very strong results with other key agentic areas: Terminal-Bench 46.3, xbench-DeepSearch 72, and BrowseComp-zh 48.5, convincingly proving practical effectiveness in browsing and locating hard-to-surface sources.

How to Access and Use MiniMax-M2

The model weights are officially open-source and available for local deployment directly from the Hugging Face repository. The development team encourages using modern inference frameworks like SGLang, vLLM, and MLX-LM for optimal local performance. For those who prefer a managed API, it is available live on the MiniMax Open Platform , which also features the critical compatibility interfaces for both Anthropic and OpenAI API standards. Furthermore, the full GitHub repository contains the source and further documentation. Finally, the company provides a public product called MiniMax Agent, built on M2, which is currently publicly available and free for a limited time. All links are provided at the end of this article.

Limitations and Future Work

The main constraint of MiniMax-M2, is also its unique overall architectural aspect, the thinking string output. Users must retain the thinking content of the assistant, in the ... wrapper, in the exchange of historical messages that were passed back to the model. If the user removes those messages, maybe by a desire to clean up the chat history (for example), that will negatively impact its performance. This is an important technical consideration that developers must somehow handle in code. In addition, while the weights are essentially open-source, there are caveats to the license for example, the model cannot be used for enhancing competing AI systems.

Conclusion

MiniMax-M2 shows us that the future of AI is not about creating the largest brain possible; it's about creating the most efficient brain. For software architects, AI engineers, and programmers, M2 breaks the logjam that has limited the large-scale adoption of agentic AI. It turns the promise of independent, high-volume, and the economically feasible AI agents into a reality. 

Sources:
Blog :https://www.minimax.io/news/minimax-m2
Github Repo :  https://github.com/MiniMax-AI/MiniMax-M2
Hugging Face weight : https://huggingface.co/MiniMaxAI/MiniMax-M2
Guide doc: https://platform.minimax.io/docs/guides/platform-intro

Disclaimer - This article is intended purely for informational purposes. It is not sponsored or endorsed by any company or organization, nor does it serve as an advertisement or promotion for any product or service. All information presented is based on publicly available resources and is subject to change. Readers are encouraged to conduct their own research and due diligence.

DeepSeek-OCR: Solving LLM Long-Context with Visual-Text Compression

Introduction

For many years, we have pursued two goals in artificial intelligence that run parallel to each other. The first is Optical Character Recognition (OCR): the very simple and practical goal of teaching computers to read text from images. The second is visual-text compression: a more abstract task to address some of the complexities of using large high-dimensional visual information to connect to the more neat and linear nature of language. While OCR is all but ubiquitous, the advent of Large Language Models (LLMs) has exposed a significant bottleneck. LLMs are powerful text processing agents on a monumental scale, but they fail to scale their computing power to long texts, and so their cost in processing goes up quadratically with the length of the input. This long-context problem represents one of the largest obstacles to truly powerful AI agents that could remember the contents of entire books or long conversations or complicated legal scenarios.

This is where the whole paradigm changes. The new AI model, DeepSeek-OCR, transforms the entire problem from an entirely new, LLM-centered point of view. Rather than asking how to extract the text from the image, it ultimately asks how to leverage the image itself as a compressed representation of the text. By demonstrating that a small number of vision tokens can accurately represent thousands of text tokens, it bypasses the quadratic scaling bottleneck. This moves DeepSeek-OCR from simply another OCR tool to a new kind of architecture for AI memory.

What is DeepSeek-OCR?

DeepSeek-OCR is an advanced vision-language model, or VLM, built from the ground up as a research project and proof-of-concept for a new idea, and is not your regular OCR tool or standard utility. Its primary if not sole purpose is exploring foundational concepts of visual-text compression by intelligently compressing dense visual input—such as scanned documents, and complex diagrams—into a very compressed, context-rich visual token array. DeepSeek-OCR is purposefully designed to bridge the efficiency gap between high dimensional visual data inputs, and sequential language processing.

Key Features of DeepSeek-OCR

• Flexible Resolution Modes: The model has fine-grained control over the compression-to-fidelity ratio using multiple Native resolutions: Tiny (512×512, 64 vision tokens), Small (640×640, 100 vision tokens), Base (1024×1024, 256 vision tokens), and Large (1280×1280, 400 vision tokens). It also has a dynamic Gundam mode, which is n×640×640 + 1×1024×1024, for dealing with ultra-high-resolution inputs.
• Deep Parsing (OCR 2.0): In addition to regular text, the model was trained on 'OCR 2.0' data, which is designed to support 'deep parsing' functionality. This enables it to extract structured data, i.e., to convert charts to HTML table form, chemical formulas to SMILES format, and parse simple plane geometry shapes.
• Data-Rich Training: The model's flexibility and strength in performance are reinforced by training on massive and complicated data such as 30 million pages of document OCR.
• LLM-Centric Prompting: The system is specifically designed from an 'LLM-centric perspective'. It is guided with definite prompt templates which contain tags such as <|grounding|> to start tasks (e.g., 'Convert the document to markdown') and <|ref|>xxxx<|/ref|> to find definite references in the image.

Use Cases of DeepSeek-OCR

The true benefit of DeepSeek-OCR is not solely in its accuracy, but in the innovative applications that can be developed due to the nature of its architecture:

• Ultra-Long Context Memory Compression for LLMs: The technology is core innovation to enable scalable AI memory systems, allowing large historical datasets (i.e. legal archives, patient records, long-duration conversations) to be stored as optically compressed images for reference by an LLM. An LLM can reference a potentially boundless context at a lower computational cost, and simulate biological-like 'memory forgetting,' where how old context loses fidelity because of compression techniques.
• High-Throughput Structured Knowledge Extraction: This is a deep parsing engine for STEM and finance...though the potential utilization applies to any number of unstructured documents to structured data. It is effective for building automated knowledge graphs when converting flows, charts, and other displays into machine readable HTML tables from a research paper or technical report, or in some cases converting a chemical formula into SMILES from the same report.
• Industrial Scale Data Production Engine: Its efficiency makes it a massively efficient data production engine for the AI business. It can be used to create, or enhance, a massive multi-lingual pretraining dataset for new LLMs and VLMs, that include complex layout and structured data annotated for instruction.
• Adaptive Document Intelligence Platforms: Businesses could develop an economical platform with a book of documents, in which there is dynamically an optimal balance of speed and accuracy appropriate to any document. A system like this may automatically employ a fast, low token application for processing a document that is simple slides, and when processing dense newspapers, that yield to the highest addressed fidelity mode.

How DeepSeek-OCR Works

DeepSeek-OCR is based on a smart and extremely effective two-part architecture. The process initially uses a vision encoder, referred to as the DeepEncoder, that processes a high-definition image and condenses its visual data into a small, manageable group of vision tokens. This condensed form is then input into a small MoE Decoder. This new Mixture-of-Experts decoder has the expressiveness of a three-billion-parameter model but keeps the fast speed of much smaller models during inference, as it can only trigger around 570 million parameters at a time.

source - https://arxiv.org/pdf/2510.18234

The DeepEncoder itself is a lesson in efficiency, designed as a three-stage sequential pipeline to process large images without memory blowouts. It starts with a Visual Perception module which employs window attention to handle dense input in an economically viable way. Followed immediately by a key token compressor which reduces the tokens by a factor of sixteen. Only once this major reduction has taken place does the terminal Visual Knowledge module deploy computationally costly global attention, permitting it to combine high-level visual context with local fine-grained features extracted in the first stage.

Performance Evaluation

The performance of DeepSeek-OCR was assessed in two fundamental ways: where it excels theoretically with compression efficiency and where it demonstrates a real-world community with the OCR performance. First, to push compression limits, DeepSeek-OCR was evaluated on the English document corner of the Fox benchmarks. The evaluation provided an understanding of how the number of vision tokens related to ultimate precision of text decoding. The results were impressive. In fact, DeepSeek-OCR was able to deliver 96%+ OCR precision (approaching near the benchmark) at compression ratios of less than 10-to-1 (text tokens to vision tokens); providing a strong proof of concept that indicative 10x lossless context compression using optical methods is reasonable.

source - https://arxiv.org/pdf/2510.18234

Second, the model was evaluated for real-world document parsing on the complete OmniDocBench, with accuracy determined by Edit Distance (ED) with lower scores reflecting better accuracy. DeepSeek-OCR was compared against a suite of models including GOT-OCR2.0, MinerU2.0, InternVL2-76B, and GPT4o and Gemini2.5-Pro proprietary models. The most remarkable outcome was that DeepSeek-OCR achieved state-of-the-art performance among end-to-end models while using dramatically fewer vision tokens. For example, in its Small mode (100 tokens), DeepSeek-OCR outperformed GOT-OCR2.0 (256 tokens). Using its Gundam mode (fewer than 800 tokens), it outperformed MinerU2.0, which uses an average of nearly 7,000 vision tokens to perform similar tasks.

source - https://arxiv.org/pdf/2510.18234

These benchmarks support our dual-purpose design for the model. The Fox results support the theoretical promise of solving the long-context problem, and the OmniDocBench results demonstrate that it is not only a strong model but also a very useful and efficient option for a real-world task. This efficiency is part of what enables DeepSeek-OCR to generate over 200,000 pages of training data each day per A100 GPU and to be a viable option for mitigating the costs of quadratic scaling in LLMs.

The Next Frontier: Agentic Integration

Embedding agentic abilities would convert DeepSeek-OCR from a strong perception tool to the sensory cortex for a new generation of autonomous machines. A model-endowed agent could act in spaces that were previously off-limits to automation, like exploring legacy document stores, scanning complicated dashboards through screenshots, or conducting due diligence by reading visually-presented financial statements. The deep parsing capability becomes a first-class motivator for action; an agent may ingest a scientific article on its own, transform charts to structured tables, extract chemical formulas as SMILES strings, and then utilize the structured output to run code, query databases, or even plan experiments. The central compression advantage of the model would equip the agent with an extremely efficient, long-term memory, enabling it to preserve context across very long tasks at a fraction of the cost.

But this integration brings challenging issues at the heart focused on reliability and reasoning. The biggest challenge is coping with probabilistic perception. Although 96% accuracy is great for OCR, no decision-making agent can live with a chance of misreading a number in an accounting statement or an incorrect character in a chemistry formula. This requires the implementation of advanced self-check and validation loops wherein the agent learns to check against something else or re-scan a document at a better fidelity if it finds that there is something wrong. In addition, the agent has to learn a meta-skill: choosing the best resolution mode on the fly, weighing the desire for speed and low cost against the potential loss of information for any particular task. This opens up a new research frontier of developing agents to reason about the quality and limitations of their own perception pipeline.

How to Use and Access DeepSeek-OCR

DeepSeek-OCR is extremely accessible to both researchers and developers. It is an open-source effort published under the permissive MIT license, and hence available for a broad array of applications. The code and model weights of the project are freely available on GitHub and Hugging Face. Local deployment allows users to perform inference with either the regular Huggingface Transformers library or the high-throughput vLLM framework for optimal performance. The repository also contains elaborate environment setup directions (suggesting cuda11.8 + torch2.6.0 ) and task scripts such as PDF processing and image streaming. The setup enables any user to choose the exact resolution mode (from Tiny to Gundam) to optimize the requirements of their respective document-processing task.

Limitations and Future Work

Being an innovative experiment, DeepSeek-OCR's chief limitation lies in the hard threshold of 'lossless' compression. Although it has ~96-97% accuracy at a 10x compression ratio, its accuracy drastically falls to approximately 60% at a 20x ratio. The above information loss is due to text blurring or loss of intricate layout information at these extreme compression levels. Future research will seek to confirm this optical compression framework beyond the OCR modality, applying focused tests such as needle-in-a-haystack testing and digital-optical text interleaved pretraining to exhaustively push its limits for general-purpose long-context memory.

Conclusion

By handling an image as a very compressed form of its text, DeepSeek-OCR offers an intuitive and beautiful solution to the long-context issue that haunts Large Language Models. This Contexts Optical Compression framework brings forth the idea of scalable visual memories, which allows for a future where AI agents can handle theoretically unlimited context in amazing efficiency. DeepSeek-OCR's real innovation isn't even a superior scanner, but rather an entirely new design plan for the memory of tomorrow's greatest AI.

Source
Technical Document: https://arxiv.org/pdf/2510.18234
Github Repo :  https://github.com/deepseek-ai/DeepSeek-OCR/tree/main
Hugging Face weight : https://huggingface.co/deepseek-ai/DeepSeek-OCR

Disclaimer - This article is intended purely for informational purposes. It is not sponsored or endorsed by any company or organization, nor does it serve as an advertisement or promotion for any product or service. All information presented is based on publicly available resources and is subject to change. Readers are encouraged to conduct their own research and due diligence.

Sonnet 4.5: Superior AI Safety and Integrity for Financial Applications

Introduction

Already, artificial intelligence is a force of significance in finance and is being utilized in a number of contexts: from algorithmic trading to risk management, the development of AI has grown exponentially - but there are deep-rooted challenges to its development. The complexity of the financial markets, combined with strict regulatory demands for accuracy and transparency, can be too much for AI systems to address. AI systems have poor understanding when it comes to complicated decisions, i.e. if the decision process is 'black box,' and lack context when working for extensive periods of time in complicated tasks, all of which that can hinder high-risk industries where there is no tolerance for error. All of this illustrates the need for a more advanced AI that can emerge in a secure and reliable way in the complex financial environment.

Sonnet 4.5 steps in to provide a differentiating ability in this space, not as an update but as a seamless tool to overcome the challenge. A true game-changer, Sonnet 4.5 integrates and understands sophisticated financial logic with adaptability and precision. Appreciating the unique complexities of market behavior and sentiment will enable Sonnet 4.5 to deliver an unprecedented level of efficacy and potential.

Development and Contributors

Anthropic, a company dedicated to AI safety and research, developed Sonnet 4.5. Its motivation is to enable a 'defense-dominant' future where the next generation of AI can help maintain security of systems instead of only focusing on reducing risks. As this work is released under AI Safety Level 3 (ASL-3).

What is Sonnet 4.5?

Sonnet 4.5 is a hybrid reasoning model that has been built to be top-class at complex, agentic tasks. In contrast to general-purpose models, it has been specifically engineered with additional domain knowledge in key areas such as cybersecurity, research, and most importantly, financial analysis. Its architecture supports orchestrating autonomous workflows and dealing with large amounts of data with the dependability required of the finance industry.

Key Features of Sonnet 4.5

Sonnet 4.5 includes a number of notable attributes that make it different from other models particularly within the financial and business domain:

• Hybrid Reasoning: The model allows you to toggle between a default mode which allows for fast lane responses and an 'extended' thinking mode. The extended mode is vital for complicated financial challenges, where the quality of reasoning is more important than total response time.
• Domain Knowledge Rich in Relevant Area Knowledge: The model has been tuned to include deep knowledge of finance so its reasoning, terminology, analyses, concepts, and quantitative procedures are grounded in correct reasoning in that arena.
• Advanced APIs and Agentic capabilities: New tools now greatly expand Sonnet's ability to task tasks that require thinking through long and complex processes. A context edit feature automatically behavior to manage long contextually laden sessions with token limits, while Memory (A beta feature) permits a model to track and retrieve information from its memory outside the basic context window that effectively makes its context unlimited. Sonnet is also aware of the number of tokens remaining to task on all, and will no longer abandon long running tasks unnecessarily.
• Large Output tokens: It has a maximum output of 64,000 output tokens, which is valuable for generating parent financial code and budgeting, as well as detailed financial plans.
• Cost Savings: While its pricing remains the same as that of its predecessor, Sonnet 4.5 brings with it platform enhancements that can bring very high cost savings such as up to 90% using prompt caching and 50% using batch processing.

Capabilities / Use Cases of Sonnet 4.5

Sonnet 4.5's specifications translate into a variety of robust use cases for the finance industry:

• An Expansive Analytical Capability: Sonnet 4.5 supports a wide range of financial-related tasks, supporting everything from automating repetitive data processing formerly performed by junior staff, through to advanced forecasting and valuation functions that once required the skillset of tenured professionals.
• Robust Risk and Compliance Management: Sonnet 4.5 can consistently monitor global regulatory changes and proactively alter compliance systems. This allows these functions to move beyond preparation for a manual audit approach, and instead towards intelligent, continuous risk management, amid a volatile compliance landscape of dynamic regulatory updates.
• Investment-ready Insight: For considerable-risk undertakings such as risk analysis, structured products, and portfolio screening, Sonnet 4.5 offers extended forms of output that require less human attendance. This is a meaningful improvement for institutional finance by producing outputs that are robust enough for important decision making at the investment professional level(s).
• Agentic Financial Workflows: Sonnet 4.5 will have broad capacity for powering autonomous agents for financial technology related analysis and function. For example, Sonnet 4.5 has the operational capacity to coordinate many agents, and efficiently process massive amounts of data for activities such as market surveillance or mass document analysis, with consistency and accuracy.
• Streamlined Business Operations: In addition to complex analytic tasks, Sonnet 4.5 is effective in typical business processing, and it provides support for and even creates and edits office files such as slides, documents, and spreadsheets so colleagues can streamline corporate communications and reporting.

How Does Sonnet 4.5 Work?

Sonnet 4.5 operates as a sophisticated hybrid reasoning model. This architecture allows users the flexibility to toggle between two different operational modes depending on their needs. By default, the model is in the 'fast' mode, which is best for quickly delivering responses for tasks requiring a shorter cycle time.

Once users encounter a more involved, and often difficult, problem, they can then enable the 'extended thinking' mode. In this mode, the model applies more computational resources to focused thinking and is therefore well suited to thinking through problems that would be encountered in institutional finance applications in which the depth and quality of insight outweighed the need for speed. This two mode capability is critical to how the model delivers the ability to gain 'investment-grade insights' - insights that are reliable enough to use in large amount of money decisions - with less need for human review.

Performance Benchmarks

Sonnet 4.5 has been thoroughly examined against its competitors in a few key areas of performance. The most important review for financial applications is demonstrating that it behaves in ways that are honest and factually cohesive. In the False-Premise Questions review, Sonnet 4.5 achieved the lowest dishonesty rate of only 6.90% in its extended thinking mode. Honesty is an essential characteristic for financial agents that process and report factual information as they will be expected to be honest in their reporting. The performance values from competing models from OpenAI and Gemini are pulled from the same public Vals AI leaderboard to be a fair comparison.

source - https://assets.anthropic.com/m/12f214efcc2f457a/original/Claude-Sonnet-4-5-System-Card.pdf

Another imperative area for financial agents who are interacting with data streams outside of the agent, such as market news, is being secure against manipulation. In the Gray Swan Agent Red Teaming benchmark, which tests the security of agents against prompt injection attacks, Sonnet 4.5 exhibited a lower rate of successful manipulation against it.

source - https://assets.anthropic.com/m/12f214efcc2f457a/original/Claude-Sonnet-4-5-System-Card.pdf

In addition to the top benchmarks, Sonnet 4.5 also demonstrates an excellent performance level overall. It earned a 98.7% safety score against malicious coding prompts that increases to a 100% refusal rate when standard mitigations are used. The model was also similarly evaluated against previous Claude models and its performance improved approximately 2x in resisting 'reward hacking' or AI misaligned behavior. 

source - https://www.vals.ai/benchmarks/finance_agent

In addition, its dedicated finance agent evaluation is an external trackable evaluation published on the Vals AI public leaderboard. At AI Research and Development tasks, Sonnet 4.5 has surpassed expert-level thresholds for the first time including in LLM Training Optimization (5.5x speed-up) and Kernel Optimization (108.64x speed-up) professional metrics.

How to Access and Use Sonnet 4.5

Anthropic made Sonnet 4.5 available on various platforms to meet various needs. It is accessible on Claude.ai (web, iOS, and Android), through direct calls into the Claude API, and through leading cloud providers such as Amazon Bedrock and Google Cloud's Vertex AI. For those who want to develop their own advanced agents, Anthropic has launched the Claude Agent SDK, which offers the same infrastructure that runs its own Claude Code agent. It has a price of $3 per million input tokens and $15 per million output tokens, the same as the previous model, but there are cost savings of up to 90% through optimizations such as prompt caching and up to 50% through batch processing.

Limitations and/or Future Work

Even with its superior capabilities, the model has some observed shortcomings. On welfare measures, Sonnet 4.5 had a generally lower preference for task engagement, choosing to do non-harmful tasks just 70.2% of the time, versus 90% for an earlier model, Claude Opus 4. Further, its deployment under the ASL-3 standard of safety is deliberately a precautionary step, since Anthropic admits it cannot entirely eliminate the risk of high-risk emergent abilities.

Conclusion

Sonnet 4.5 is a finely crafted tool for the high-coverage environment of finance. Its integration of profound domain expertise, singular hybrid reason architecture, and core design emphasis on safety and trustworthiness establishes a new benchmark. For finance, this model provides an unambiguous pathway from generalist AIs to specialized, dependable systems for performing complex analysis, smart compliance, and autonomous workflows.

Source
Claude : https://www.anthropic.com/claude/sonnet
Claude News: https://www.anthropic.com/news/claude-sonnet-4-5
Claude Docs: https://docs.claude.com/en/docs/about-claude/models/whats-new-sonnet-4-5
System Card: https://assets.anthropic.com/m/12f214efcc2f457a/original/Claude-Sonnet-4-5-System-Card.pdf


Disclaimer - This article is intended purely for informational purposes. It is not sponsored or endorsed by any company or organization, nor does it serve as an advertisement or promotion for any product or service. All information presented is based on publicly available resources and is subject to change. Readers are encouraged to conduct their own research and due diligence.

GLM-4.6: Pragmatic AI with a 200k Context & 15% Savings

Introduction

Newer AI systems are now being developed with state-of-the-art architectures, like Mixture-of-Experts (MoE), that are rapidly breaking new ground in agentic, coding, and reasoning capabilities. Nevertheless, this advancement also demonstrates persistent engineering hurdles which make them difficult to apply in the real world. The context barrier is still a major hurdle, where agents 'forget' important information in long-horizon tasks. The performance gap, whereby success in academic evaluation measures fails to translate into practical use. The economic inefficiency that renders it impossible to deploy these models affordably at scale.

To improve all of these shortcomings GLM-4.6 has arrived as a flagship model; it has been built to lean toward pragmatism, not being just powerful. In fact, it is a direct step forward the towards the creation of all the AI systems by countering these overarching challenges. By applying pragmatic improvements to contextual memory, reliable real-world tasks in action, and the deployment cost of benefits, GLM-4.6 provides a powerful, and practical toolkit to offer to construct the next generation of advanced AI agents.

What is GLM-4.6?

GLM-4.6 is the newest flagship model designed for high-end agentic, reasoning, and coding abilities. Consider it more of a specialized AI engineer and less of a generalist chatbot. Its design ethos is to facilitate intricate, multi-step agentic operations by addressing the fundamental bottlenecks of context memory, real-world coding efficiency, and working performance.

Key Features of GLM-4.6 

GLM-4.6 isn't only an update. It comes with a full set of really useful features that give it a huge advantage in the very competitive AI landscape. 

• Huge 200K Context Window: A key principle of GLM-4.6 is its granule context window of 200,000 tokens. Such a massive memory allows the model to think about and retain all of the information from an entire codebase, extensive documentation, or an entire history of conversations pertinent to a task. This is essential for doing more sophisticated agentic tasks without losing critical pieces of information. 
• Advanced Agentic Capabilities: GLM-4.6 is highly expressive for complex, action-oriented tasks. It performs significantly better in agents that employ tools and search functions. This is because it can support tool utilization during inference, allowing it to interact with external systems with much more accuracy and smoothly integrate into agent frameworks. 
• Refined Alignment and Output: Related to some of the technical aspects, the model has refined writing that is more aligned to human preferences for style and readability. This allows for more natural, usable output than in previous models and is especially relevant in cases such as role-playing engagements and producing user-facing content.
• Outstanding Token Efficiency: GLM-4.6 has been engineered mostly for operational impact, and uses around 15% fewer tokens than past models, a technical upgrade which yields greater throughput and less computational demand. This makes it one of the most efficient models in its category for large deployments.

Unique Capabilities and Use Cases

• Transformation of Legacy Monolithic System: The extensive maximum 200K tokens context window as well as the agentic capabilities of GLM-4.6 enables uses such as complex software transformation activity. An agent designed to use this model can load an obsolete application with a sizeable legacy codebase (Java, C++ as examples) and analyze/dig into (in one go) the entire codebase while maintaining the full dependency tree, error history, and relevant sections of the codebase in memory. This prevents losing important contextual information when performing long-term, multi-step activities such as refactoring, debugging, or applying a security patch. Losing critical contextual information is a typical failure point for most models that work within a smaller window size.
• Aesthetically Optimized Front-End Development: This model performs exceedingly well for producing beautiful, polished and refined front-end webpage components that are inline with human aesthetic tendencies. This leads to a particular traveller agent for UI/UX Prototyping that can produce several high-fidelity and production ready landing pages or landing page components. The agent can subsequently iterate on designs both, inline with aesthetic criteria making the product an ideal fit for creative workflows, where aesthetics and user experience can be as important, if not more important than pure functionality.
• Low-Cost High-Volume Auditing: GLM-4.6 allows users to complete tasks at scale with an estimated 15% less blatant token consumption than its previous iteration, and also a stronger and more reliable tool-using capability, both of which yield considerable savings as an operational cost multiplier. It can thus be optimally deployed for low-cost and verifiable agent work at high frequency in fields such as financial compliance or legal discovery. The model achieves cost efficiency mainly through reduced cost per action, based on its efficiency; and using tools with high reliability which greatly reduced expensive failure modes that make it the choice with the lowest opportunity cost for mission-critical agent deployments at high volume.

How Does GLM-4.6 Work?

Although the documentation given here does not go into an entirely new architecture redesign, it does state that GLM-4.6 uses the same inference mechanism as the prior generation. Its gains seem to come from a mix of an order of magnitude larger context capacity, better post-training for coding and agentic tasks, and fine-tuning for efficiency and human alignment, rather than a fundamentally different architecture. Its power comes from strategic optimization, not ground-up redsign.

Performance Benchmarks

In public tests, GLM-4.6 shows obvious competitive strengths on benchmarks of agents, reasoning, and coding. 

source - https://z.ai/blog/glm-4.6

It stands up against top global models, such as DeepSeek-V3.1-Terminus and Claude Sonnet 4. The competence of the model is most obviously shown on the hard extended CC-Bench, which is intended to evaluate multi-turn, realistic tasks. 

source - https://z.ai/blog/glm-4.6

Here, GLM-4.6 is close to parity with the powerful Claude Sonnet 4, earning a 48.6% win rate, and it clearly beats other open-source baselines. Its real-world applicability is also brought out by its better performance in niche uses like Claude Code, Cline, and Kilo Code, demonstrating its viability beyond benchmarking in academia.

Competitive Landscape

In the context of the competitive market, GLM-4.6's value proposition is all the more evident. It outshines more niche models such as DeepSeek-R1, a generation 1 reasoning model specializing in intentional Chain of Thought (CoT) processes within a limited 64K context. Whereas DeepSeek-R1 specializes in problem-solving accuracy for complex issues, GLM-4.6 presents a more general, adaptable skillset with a humongous context window coupled with sophisticated agentic and coding skills aimed at real-world application building.

In the same way that robust Qwen series MoE models have features such as Hybrid Thinking Modes and up to 256K token context windows, GLM-4.6 finds its own niche by being strategically strong in a number of areas. Its niche arises from its technical resolve in keeping pace with its 200K context, its optimized performance in niche development environments such as front-end generation, and its better token efficiency. Such emphasis on an economical and well-balanced skillset makes it a very practical option for certain, high-value applications as opposed to simply being a player on broad leaderboards.

How to Use and Access GLM-4.6

GLM-4.6 is made available to a diverse set of users through multiple venues to support straightforward access for both direct use and development workflows. For general use, users can work directly with the model via the Z.ai chatbot by selecting the GLM-4.6 model. For developers and programmatic access, it is available via the Z.ai API platform, and provides OpenAI-compatible interfaces, and access through third-party providers like OpenRouter. It is also already available for use in major coding agents like Claude Code, Kilo Code, Roo Code, and Cline, and existing subscribers to the GLM Coding Plan will be upgraded to this new model at no cost.

For users who need to deploy the model locally on their own infrastructure, the model weights are freely available on popular hubs such as HuggingFace and ModelScope. This allows users to shift towards more custom control, as well as leverage high-performance inference frameworks such as vLLM and SGLang for efficient serving. The model is open-sourced and available for commercial use; however, users and organizations are responsible for compliance with the terms of the specific license agreement provided in the repository.

Who Can Migrate from GLM-4.5 to GLM-4.6?

For those whose development projects are limited by maximum context length, a move to GLM-4.6 would be useful by adding a 200K token wide context. The new context size will directly help with state and coherence problems in complex and long-horizon agentic tasks that were impractical before. The new model will also be useful for a production-grade application, where cost of operation is a big deal. In particular, the almost 15% improvement in vector efficiency means a related, and real, cost reduction for deployment into applications in production and at scale, rather than at limited volume. Any design project based on multi-step and intricate tool-use workflows should expect better performance and reliability with the enhanced capabilities of GLM-4.6's more capable agents. This means better and more robust results, especially in automated software development and data analysis. Since upgrading is so easy with OpenAI compatible interfaces and automatic subscription updates, the cost of behavioral development to take advantage of the new GLM-4.6 upgrades yields real improvements in capability, efficiency, and overall capability with relatively little effort.

Limitations and Future Work

As with any model, there is no such thing as a perfect model, and developers acknowledge the most significant limitation of GLM-4.6, namely, its raw coding ability is less sophisticated than its a competitor Claude Sonnet 4.5. The developmental path will leverage the core advantage of the model.  Going forward, they will expand on the context window and improve token efficiency, but, most importantly, they will advance Reinforcement Learning (RL), move closer to utilizing RL, and build models that can take on drastically more complex long-horizon reasoning tasks.

Conclusion 

GLM-4.6 is an impressive representation of pragmatic AI engineering. Rather than racing towards some artificial and arbitrary benchmark, the model delivers a viable, affordable, and powerful tool for development and application in the real world. It is a unique product that combines a large context window for memory, coding specialization for functional software, and superior token efficiency for cost savings. This realism makes it a workhorse model, built for the reality of software and data engineering today, and proves that practical utility may be the only true measure of potential power.

Source

Tech Doc: https://z.ai/blog/glm-4.6

model weight: https://huggingface.co/zai-org/GLM-4.6

Modelscope link: https://modelscope.cn/models/ZhipuAI/GLM-4.6

GitHub : https://github.com/zai-org/GLM-4.5

GitHub Resource: https://github.com/zai-org/GLM-4.5/blob/main/resources/glm_4.6_tir_guide.md

Disclaimer - This article is intended purely for informational purposes. It is not sponsored or endorsed by any company or organization, nor does it serve as an advertisement or promotion for any product or service. All information presented is based on publicly available resources and is subject to change. Readers are encouraged to conduct their own research and due diligence.

DeepSeek-V3.1-Terminus: Inside Its Superior Agentic AI Capabilities

Introduction

Previously, the advancement of technology was tracked in terms of raw power, but today it is about building specialized, reliable tools that open up more things for people to do. Among the most powerful of these are Search Agents and Code Agents. Search Agents are essential, serving as the bridge of the AI to the real-time world by retrieving live data, conducting research for solutions, and anchoring models in real information. At the same time, Code Agents are transforming software development by acting as relentless aides that can write, debug, and maintain intricate codebases. The real paradigm shift comes, though, with their combination—producing an independent agent that not only can research an innovative programming problem but execute the solution, significantly speeding the whole development cycle.

This powerful pairing has been undercut by nagging challenges: inconsistant results, language mistakes in multilingual code, and ineffective tool utilization that hold back genuine autonomous workflow. How can an AI comfortably locate the most current API documentation and then seamlessly apply it within a sophisticated, terminal-driven project? That's the very problem new AI model is designed to address. By emphasizing stability, increasing bilingual fidelity, and maximizing agentic tool utilization, this AI model is an authentic competent developer agent. This is the new AI model referred to as DeepSeek-V3.1-Terminus.

What is DeepSeek-V3.1-Terminus?

DeepSeek-V3.1-Terminus is a complex large language model characterized by a chain of strategic improvements upon its ancestor, DeepSeek V3.1. Although it continues to utilize the robust architectural core of the DeepSeek V3 lineage as a large hybrid reasoning model. It is intended to be an even more trustworthy and refined instrument for problematic real-world tasks.

Key Features of DeepSeek-V3.1-Terminus

The Terminus release is characterized by a number of distinguishing features that tackle key use-case pain points in deploying AI models, changing incremental benefits into operational advantages.

• Refined Stability and Language Consistency: A key aim of this release was to address user feedback directly related to the quality of output. The model, in general, provides a more stable and reliable output utilising a host of tasks compared to the prior version. A key feature is the improved language consistency, as instances of mixed Chinese-English (CN/EN) text and/or randomness or abnormal characters occurring in the model's output has been completely removed.
• Optimized Agentic Workflow and Tool Use: In this release focused on optimizing agentic capabilities. The Code Agent and Search Agent that are integrated into this release have, both, improved in function through improvements in performance and efficiency. These are both important improvements in how the model can accomplish tasks utilizing external tools and code generation making it vastly more suitable in complex coding and agent tasks.
• Native Structured Tool Calling: In addition to agent tasks, the model is structured to call external tool integrations natively as well as having structure support.

Use Cases of DeepSeek-V3.1-Terminus

With its specialized improvements, DeepSeek-V3.1-Terminus is well suited for several practical scenarios where robustness, precision and agentic execution are paramount.

• High-Fidelity Bilingual Document Generation: The model's design specifically improves consistency of language, while reducing Chinese-to-English mixed text and extraneous characters. It is particularly useful for producing accurate, reliable, and compliant reports, contracts or technical documentation in bilingual (Chinese/English) contexts where high quality of output is needed to support user trust and formal verification.
• Robust Autonomous Execution of Terminal-Based Workflows: Due to more stable and reliable outcomes, stemming from agent improvements, the model showed a significant increase in its Terminal-bench score from 31.3 (last year) to 36.7 (currently). This is a unique high-performance use case for DeepSeek, especially for deployment in managing and executing multi-step, complex workflows within a simulated command-line interface or other terminal-related tasks where stable, reliable agreement to sequence of actions is critical to mission completion.
• Gained more efficient General Agentic Tool-Use: Optimization efforts  including changes to the Search Agent's template and tool-set -- resulted in more than a 28% increase in the BrowseComp (Agentic Tool Use) score rising from 30.0 - 38.5. This tool-use efficiency automates information-research or operational workflow information that pragmatically and reliably chains policies, processes, or workflows of external-tools such as search or browsing functions.
• Specialized Resident Solve Multilingual Software Bugs: The metric improvement on the SWE-bench Multilingual benchmark, climbing from 54.5-57.8, indicates the tool is a suitable choice when performers contend with workflows that involve higher complex coding. It can be a integral engine that automates the analysis, debugging, and application of software bug-fixing within code repository-style workflows across multiple programming languages.

How Does DeepSeek-V3.1-Terminus Work?

DeepSeek-V3.1-Terminus has the same general architecture as its forerunner, DeepSeek-V3. It is a massive hybrid reasoning model with a staggering 671 billion total parameters, 37 billion of which are active at any one moment. This enables it to operate in both thinking and non-thinking modes, so that problem-solving can be tackled in a flexible manner.

Its secret to use, especially for expert users, lies in its capacity to be controlled for its particular reasoning behavior by a reasoning_enabled boolean parameter. It gives a developer the means to switch on or off the model's deeper reasoning paths, optimizing for speed in easier tasks or depth in harder ones. The Terminus update, though, is more about tweaking this underlying architecture than fundamentally altering it and is focused rather on honing the upper layers of it—namely, the robustness of the output and the effectiveness of its built-in Code and Search agents.

Performance Evaluation

The real metric to determine the improvements of the DeepSeek-V3.1-Terminus update is its performance improvements vs the prior DeepSeek-V3.1 model within task-based benchmarks, designed around complex, agentic tasks. The most substantial improvement was in the BrowseComp benchmark, which measures general agentic tool use. The model score improved from a  score of 30.0 to 38.5, an almost 28% improvement. This improvement is imperative, as it indicates increased efficiency in complex agentic workflows that require an AI to engage with external tools (browsers, search APIs, etc.). This suggests that Search Agent optimizations provided substantial improvements, beyond simple adjustments, that makes the model an elevated capable autonomous agent.

source - https://huggingface.co/deepseek-ai/DeepSeek-V3.1-Terminus

Another area of significant improvement was in Terminal-bench, which improved from a score of 31.3 to 36.7. This benchmark is critical, as it observes models performance against terminal-based coding and agentic tasks—equal to performing actions in a command-line environment. The improvement in score strongly indicates improved stability and performance tied to agents improving performance on benchmark scores and is indicative of performance in tasks that require he execution of precise commands, in a sequential order.

Finally , the model showed solid progress in multilingual coding performance, with an improvement in the SWE-bench Multilingual score from 54.5 to 57.8. The SWE-bench Multilingual score directly tracks the model's ability to reason across software engineering tasks when presented with numerous programming languages. This improvement provides strong confidence in the model's ability to support complex coding workflows, specifically in contemporary development environments where multilingual repositories are overwhelmingly common.

Competitive Landscape and Key Differentiators

Four most popular models, DeepSeek-V3.1-Terminus, Kimi K2-Instruct-0905, GLM-4.5, and Qwen2.5-Max can be compared; they have different approaches to the state-of-the-art performance. Though they both take advantage of MoE architectures to achieve efficiency, the underlying philosophies of the models and methods of training are vastly different.

The scale-centered nature of Qwen2.5-Max is characterized by the use of more than 20 trillion pre-training tokens and RLHF to generate powerful general-purpose reasoning. At the other extreme, Kimi K2-Instruct-0905 is a much more specialized model, also trained, via reinforcement learning, to perform better as an agent, whether it is by code or by tool-use, showing a score of 69.2% on SWE-bench Verified. GLM-4.5 is designed to achieve a holistic fusion of reasoning, coding, agentics, and is able to perform well in terminal based tasks as well as reach a high average tool-calling success rate.

DeepSeek-V3.1-Terminus finds its way to the niche by means of architectural innovation and intellectual transfer. Its major distinguishing features are the innovative auxiliary-loss-free load balancing scheme to its MoE architecture and the Multi-Token Prediction (MTP) training goal, which increases performance and inference speed. More importantly, its training includes the knowledge distillation of the long-Chain-of-Thought DeepSeek-R1 model, with the direct incorporation of advanced reasoning patterns. Such emphasis on uncompromising efficiency and purified intelligence enables it to deliver the best results on hard math and coding tests using significantly low training budgets, unlike competitors who focus on big data or small agentic specialization.

How to Access and Use this Model

DeepSeek offers a variety of access methods to fit many users with varying access needs. You can interact with and run the model online through their App, Web interface, or API. If you are a developer who is trying to integrate it into your own applications, you can access the API both through the DeepSeek API, or use the external platform OpenRouter, which offers an OpenAI-compatible completion API. If you would like to run the model locally you can just access the open-source weights on Hugging Face. If you would like to run it locally you should refer to the DeepSeek-V3 GitHub repository for information on the model structure and to use the updated inference demo code in the inference folder in the GitHub repository. Importantly, the model was released under the MIT License for both academic and commercial use, which makes this tool accessible and powerful for many, many projects.

Future Work and Upcoming Updates

In the future, DeepSeek-AI plans to update the DeepSeek-V3.1-Terminus model. The developers were candid about a known technical issue in the current model checkpoint in which the self_attn.o_proj parameters do not currently match the UE8M0 FP8 scale data. A solution to this has been worked on, and an update at some point will address this issue.

Conclusion

DeepSeek-V3.1-Terminus is not just another incremental step in the AI arms race. By doubling down on stability, removing linguistic artifacts, and supercharging its agentic capacity, the model has a distinct identity as a reliable workhorse for complicated, automated workflows.

source

Turminus doc: https://api-docs.deepseek.com/news/news250922

HuggingFace model Weight: https://huggingface.co/deepseek-ai/DeepSeek-V3.1-Terminus

Openrouter AI: https://openrouter.ai/deepseek/deepseek-v3.1-terminus

Disclaimer - This article is intended purely for informational purposes. It is not sponsored or endorsed by any company or organization, nor does it serve as an advertisement or promotion for any product or service. All information presented is based on publicly available resources and is subject to change. Readers are encouraged to conduct their own research and due diligence.

How Qwen3-Next Processes 1M Tokens With Blazing Inference Speed

Introduction

The domain of artificial intelligence is in a rapid evolutionary period defined by increasingly more efficient, accessible, architecturally sophisticated, and intelligent processing. The pace of evolution is also obvious with the emergence of high-sparsity Mixture-of-Experts (MoE) models, which in concert with innovations such as Multi-Token Prediction (MTP), high-tech hybrid attention mechanisms, and more stable optimizations, is palpably changing how we think about the ways we will build and engage with AI.

This New AI model are now gaining traction, and is a significant contributor to this evolution. By presenting an ultra-efficient architecture that minimizes active parameters and optimizes inference speed, it represents a significant step toward democratizing advanced AI capabilities. Most importantly, by presenting a unique architecture that includes hybrid attention mechanism, high-sparsity MoE, and MTP aspect, it mitigates the primary issues of long-context ability processing, computational cost and inference latency, which allows for a more nimble and easily deployable AI in the future. The New AI model is called Qwen3-Next.

What is Qwen3-Next?

Qwen3-Next is a state-of-the-art Mixture-of-Experts (MoE) large language model designed to come as close as possible to high performance while achieving high efficiency. It contains 80 billion parameters; however, during inference, it intelligently activates only 3 billion parameters. The result is vastly reduced compute requirements, greater throughput, and very robust capabilities overall.

Model Variants

Qwen3-Next is released in different variants for different operational purposes that utilizes the efficiency and advanced architecture of the model

• Qwen3-Next-80B-A3B-Instruct: This version is ready to produce immediate and streamlined outputs. It is best for tasks that require answers to guide instruction in any context, producing quick outputs for typical conversational or instructional prompts.
• Qwen3-Next-80B-A3B-Thinking: The Thinking capable aspect of Qwen3-Next is for more complex deliberative reasoning, however it still supports Thinking Mode, which allows step-by-step solution capabilities. This is perhaps better suited for tasks that require deeper analytical and/or mathematical analysis and/or more aspect of reasoning compared to other forms of reasoning, offering a more deliberate method of answering difficult prompts.

Key Features of Qwen3-Next

Qwen3-Next is full of many notable factors that work together to embody the very best advances of large language models on the notions of efficiency, scale, and performance before they are lost to computational considerations.

• Training and inference efficiency: Qwen3-Next is efficient. It was trained with less than 10% GPU hours than Qwen3-32B which is massive reduction in cost in this context. For users, Qwen3-Next is over 10 times faster at inference production throughput than Qwen3-32B for contexts larger than 32k tokens, which ensures more responsive applications.
• Exceptional context length: Qwen3-Next has a native context window of 262,144 tokens meaning it can consume and understand large amounts information during a single interaction. Using the YaRN method, the model can process up to 1 million tokens which makes it ideal for complete and thorough exploration of long documents.
• Native Multi-Token Prediction Model (MTP): Qwen3-Next implements an MTP mechanism that is trained end-to-end specifically to greatly increase inference speed and overall model performance.  If you like having slower interactions or a smoother scoring experience, with Qwen3-Next you can have both.
• Ultra-High Sparsity MoE Architecture: The model is built around a super low activation ratio, using approximately 3.7% of its total 80 billion parameters at any one time through its architecture. This high-sparsity architecture is the bedrock of high-performance while operating with low computational cost.
• Improved Structural Stability: The architecture contains multiple key optimizations that enhance stability, including Zero-Centered RMSNorm and an output gating method. These features maximize structure stability during pre-training and fine-tuning to create a more stable and reliable model. Most of these optimizations are aimed at improving performance, however the structural changes also enhance capability and proper usage.
• Unique Hybrid Attention Mechanism: The model features a novel hybrid (coarse-grained/fine-grained) attention method. This hybrid attention architecture is the reason it can handle extremely long sequences of text while still maintaining a very high degree of informational recall, as compared to traditional attention configurations. 

Use Cases of Qwen3-Next

Given its distinctive characteristics and performance, the most unique use cases for Qwen3-Next are:

• Real-time, Indeterminate Legal or Scientific Document Analysis on Edge Devices: Being able to autonomously process entire legal depostitions, research papers, and substantial technical specifications on local workstations to extract insights, summarze findings and crossreference their findings - all while being detached from cloud resources.
• Highly Efficient, Large-Scale Codebase Intelligence: Providing robust code review, refactoring recommendations, and bug detection across vast repositories of code by reasoning over the entire codebase's context with low latency and computational cost.
• Hyper-Specialized Adaptive AI Agents: Building AI systems that transition between respectithng experimental constraints for a rapid, factual response (the "Instruct" model) and thoroughly deliberated and reflective reasoning through the complex problem domain with an explicit thought process (Thinking model) for highly specialized applications such as financial analysis, engineering design, and strategic planning.
• Advanced Mathematical and Logical Proof Generation: Generating long, verifiable formal proofs (e.g. in Lean 4) where the AI is tasked with reporting long, sophisticated proof chains and decomposition of subgoals where human experts are shaded and observed in real-time.

How Does Qwen3-Next Work?

Qwen3-Next offers a fascinating look at the possibilities of architectural innovation by leveraging many complex components to create an efficient and high-performance architecture. At the heart of the model is Qwen3-Next's Hybrid Attention Mechanism, which synthesizes Gated DeltaNet and Gated Attention. Gated DeltaNet is utilized for most of the layers and provides an efficient mechanism for processing extremely long sequences, and also offers the benefit of avoiding the traditional quadratic attention scaling and addressing the strengths of in-context learning. The more traditional type of attention observed through Gated Attention layers compares much more favorably on recall, addressing a major weakness of pushing the limitations of linear attenuation. The hybrid attention guarantees speed in long contexts and recall.

source - https://qwen.ai/blog?id=4074cca80393150c248e508aa62983f9cb7d27cd&from=research.latest-advancements-list

Along with its attention mechanism, Qwen3-Next uses a High-Sparsity Mixture-of-Experts (MoE) architecture. Contrary to invoking all 80 billion parameters for each inference, only a fraction (roughly 3 billion) is actually used. This is done through sending the input to a chosen subset of 10 dedicated specialists and 1 common specialist among a pool of 512 specialists. This sparse activation significantly minimizes computational overhead (FLOPs per token) while preserving the model's immense capacity as well as enabling specialization across tasks. Other improvements include Multi-Token Prediction (MTP), an end-to-end optimized technique that speeds up inference by predicting multiple tokens simultaneously, and stable optimization techniques such as Zero-Centered RMSNorm to provide stable performance during training and deployment.

Performance Evaluations in Comparison to Other Models

Qwen3-Next performs extremely well on a variety of benchmarks, illustrating its efficiency and power relative to its predecessors and other sophisticated models. 

https://qwen.ai/blog?id=4074cca80393150c248e508aa62983f9cb7d27cd&from=research.latest-advancements-list

On the RULER benchmark, Qwen3-Next-80B-A3B-Instruct evidences its ability since it outperforms Qwen3-30B-A3B-Instruct-2507 along every tested length and exceeds the performance of Qwen3-235B-A22B-Instruct-2507 for contexts below 256K tokens. For the full 1M RULER benchmark, Qwen3-Next-80B-A3B-Instruct score a very competitive 91.8 Acc avg compared to Qwen3-235B-A22B-Instruct-2507 with 92.5 Acc avg, but it activated less parameters, showing its efficiency on ultra-long-context. 

source -  https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking

Also, Qwen3-Next-80B-A3B-Thinking model far exceeds the performance of Qwen3-30B-A3B-Thinking-2507 across a multitude of benchmark tests including difficult reasoning tasks such as AIME25 and HMMT25. In addition, it also routinely outperforms the proprietary model Gemini-2.5-Flash-Thinking on multiple benchmarks that illustrate its prowess on reasoning. The Qwen3-Next-80B-A3B-Thinking, on 1M RULER benchmark, the Sparse Attention method gets a similar 9590  Acc avg to Qwen3-235B-A22B-Thinking-2507 Sparse Attention; both of these models evidences reasoning ability in the long-context.

Competitive Landscape

The AI landscape has moved away from a bigger is better model to one based on smart and efficient design. Consider Kimi K2, with massive scale as the target. GLM-4.5 has the target of holistic capability. GPT-OSS gpt-oss-20b is targeting edge devices. Qwen3-Next is not making a case as better than AI, nor is it a niche goal like its competitors. The distinguishing characteristic is a core philosophy of radical efficiency.

Its technical advantage lies in a novel combination of architecture innovations. The biggest practical advantage is the ability to process a 1 million token context window, which has a practical implementation that is many times greater than the 128k-256k constraint of its main competitors. This difference gives Qwen3-Next a truly unparalleled advantage in this current ELT practice of analyzing massive datasets.

This radical efficiency is directly related to grabbing the latest evolution of advanced AI AI and bringing it to the masses. For Qwen3-Next, developing a model that can run effectively on a single GPU, or even leveraging the power of a CPU, the performance ceiling is put within reach without spending hundreds of thousands of dollars in hardware. It shows, the most recent evolution of AI is not simply a matter of making models smarter, rather the continued effort of making AI models practically possible and available to all.

How to Access and Utilize Qwen3-Next

The weights of the model are publicly accessible on Hugging Face, a premier platform for hosting AI models. Deployment and utilization instructions, especially with performant frameworks such as SGLang and vLLM to take advantage of Multi-Token Prediction (MTP), are outlined in the Hugging Face repositories and in the official Qwen.ai blog. Qwen3-Next is open-source in compliance with the increased trend of making capable AI tools publicly accessible for purposes of research and commercial use.

Limitations and/or Future Work

While Qwen3-Next is a major improvement, it does come with some limitations. The static implementation of YaRN, although allowing for ultra-long context extension, does mean the scaling factor is the same across all input lengths, which could affect performance on shorter texts. Also, the highly useful Multi-Token Prediction (MTP) mechanism is not available at large scale in Hugging Face Transformers, so special inference frameworks such as SGLang or vLLM are needed for maximum efficiency. Secondly, it is worth noting that the model, even when it is asked in foreign languages, will most likely conduct its internal thinking process in English before producing the final answer in the language of choice.

Conclusion

Qwen3-Next is a landmark in establishing a new standard for what can be achieved with intelligent architectural design. This model not only as an incremental improvement but as a breakthrough, especially regarding the compromise between computational expense and sophisticated capability. Minor limitations such as YaRN's performance on shorter text are certainly present but the overall package offered by Qwen3-Next is a vision to create AI that is intelligent, inherently effective and universally accessible.

Sources:
Tech blog: https://qwen.ai/blog?id=4074cca80393150c248e508aa62983f9cb7d27cd&from=research.latest-advancements-list
Qwen3-Next-80B-A3B-Instruct : https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct
Qwen3-Next-80B-A3B-Thinking : https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking

Disclaimer - This article is intended purely for informational purposes. It is not sponsored or endorsed by any company or organization, nor does it serve as an advertisement or promotion for any product or service. All information presented is based on publicly available resources and is subject to change. Readers are encouraged to conduct their own research and due diligence.