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[AINews] Top Local Models List - April 2026
a quiet day lets us check in on the local models scene
[AINews] AI Engineer Europe 2026
Two quiet days in a row let us reflect on the first AIE in London.
[AINews] Every Lab serious enough about Developers has bought their own Devtools
OpenAI buys Astral, Anthropic buys Bun, Google DeepMind bought the Antigravity team.
[AINews] MiniMax 2.7: GLM-5 at 1/3 cost SOTA Open Model
congrats MiniMax!!
MiniMax: MiniMax M2.7 (minimax/minimax-m2.7)
MiniMax-M2.7 is a next-generation large language model designed for autonomous, real-world productivity and continuous improvement. Built to actively participate in its own evolution, M2.7 integrates advanced agentic capabilities through multi-agent collaboration, enabling it to plan, execute, and refine complex tasks across dynamic environments. Trained for production-grade performance, M2.7 handles workflows such as live debugging, root cause analysis, financial modeling, and full document generation across Word, Excel, and PowerPoint. It delivers strong results on benchmarks including 56.2% on SWE-Pro and 57.0% on Terminal Bench 2, while achieving a 1495 ELO on GDPval-AA, setting a new standard for multi-agent systems operating in real-world digital workflows.
[AINews] NVIDIA GTC: Jensen goes hard on OpenClaw, Vera CPU, and announces $1T sales backlog in 2027
a quiet day lets us reflect on NVIDIA GTC 2026
[AINews] The high-return activity of raising your aspirations for LLMs
a quiet day lets us publish a thought from OpenAI researcher Aidan McLaughlin
Introducing Fireworks AI on Microsoft Foundry: Bringing high performance, low latency open model inference to Azure
With Fireworks AI, we're extending the Microsoft Foundry platform with high performance inference for state-of-the-art open models on Azure. The post Introducing Fireworks AI on Microsoft Foundry: Bringing high performance, low latency open model inference to Azure appeared first on Microsoft Azure Blog .
[AINews] Anthropic accuses DeepSeek, Moonshot, and MiniMax of >16 million "industrial-scale distillation attacks"
the US-China cold war takes a big step up
Anthropic accuses Chinese AI labs of mining Claude as US debates AI chip exports
Anthropic accuses DeepSeek, Moonshot, and MiniMax of using 24,000 fake accounts to distill Claude’s AI capabilities, as U.S. officials debate export controls aimed at slowing China’s AI progress.
[AINews] Qwen3.5-397B-A17B: the smallest Open-Opus class, very efficient model
Congrats Qwen team!
[AINews] new Gemini 3 Deep Think, Anthropic $30B @ $380B, GPT-5.3-Codex Spark, MiniMax M2.5
There's too much going on!
MiniMax: MiniMax M2.5 (minimax/minimax-m2.5)
MiniMax-M2.5 is a SOTA large language model designed for real-world productivity. Trained in a diverse range of complex real-world digital working environments, M2.5 builds upon the coding expertise of M2.1 to extend into general office work, reaching fluency in generating and operating Word, Excel, and Powerpoint files, context switching between diverse software environments, and working across different agent and human teams. Scoring 80.2% on SWE-Bench Verified, 51.3% on Multi-SWE-Bench, and 76.3% on BrowseComp, M2.5 is also more token efficient than previous generations, having been trained to optimize its actions and output through planning.
What’s next for Chinese open-source AI
The past year has marked a turning point for Chinese AI. Since DeepSeek released its R1 reasoning model in January 2025, Chinese companies have repeatedly delivered AI models that match the performance of leading Western models at a fraction of the cost. Just last week the Chinese firm Moonshot AI released its latest open-weight model,…
[AINews] Z.ai GLM-5: New SOTA Open Weights LLM
We have Opus 4.5 at home
MiniMax: MiniMax M2-her (minimax/minimax-m2-her)
MiniMax M2-her is a dialogue-first large language model built for immersive roleplay, character-driven chat, and expressive multi-turn conversations. Designed to stay consistent in tone and personality, it supports rich message roles (user_system, group, sample_message_user, sample_message_ai) and can learn from example dialogue to better match the style and pacing of your scenario, making it a strong choice for storytelling, companions, and conversational experiences where natural flow and vivid interaction matter most.
Chinese AI unicorn MiniMax soars in Hong Kong debut
Shares in Chinese AI startup MiniMax soared as much as 78% as it went public in Hong Kong on Friday, raising US$619 million in a sign that strong investor demand is rewarding the country's rapidly developing sector.
Startups go public in litmus test for Chinese AI
Leading Chinese artificial intelligence startup Zhipu AI soared as it went public in Hong Kong on Thursday, a day before rival MiniMax also makes its market debut in a litmus test for the country's rapidly developing sector.
MiniMax: MiniMax M2.1 (minimax/minimax-m2.1)
MiniMax-M2.1 is a lightweight, state-of-the-art large language model optimized for coding, agentic workflows, and modern application development. With only 10 billion activated parameters, it delivers a major jump in real-world capability while maintaining exceptional latency, scalability, and cost efficiency. Compared to its predecessor, M2.1 delivers cleaner, more concise outputs and faster perceived response times. It shows leading multilingual coding performance across major systems and application languages, achieving 49.4% on Multi-SWE-Bench and 72.5% on SWE-Bench Multilingual, and serves as a versatile agent “brain” for IDEs, coding tools, and general-purpose assistance. To avoid degrading this model's performance, MiniMax highly recommends preserving reasoning between turns. Learn more about using reasoning_details to pass back reasoning in our docs .
MiniMax: MiniMax M2 (minimax/minimax-m2)
MiniMax-M2 is a compact, high-efficiency large language model optimized for end-to-end coding and agentic workflows. With 10 billion activated parameters (230 billion total), it delivers near-frontier intelligence across general reasoning, tool use, and multi-step task execution while maintaining low latency and deployment efficiency. The model excels in code generation, multi-file editing, compile-run-fix loops, and test-validated repair, showing strong results on SWE-Bench Verified, Multi-SWE-Bench, and Terminal-Bench. It also performs competitively in agentic evaluations such as BrowseComp and GAIA, effectively handling long-horizon planning, retrieval, and recovery from execution errors. Benchmarked by Artificial Analysis , MiniMax-M2 ranks among the top open-source models for composite intelligence, spanning mathematics, science, and instruction-following. Its small activation footprint enables fast inference, high concurrency, and improved unit economics, making it well-suited for large-scale agents, developer assistants, and reasoning-driven applications that require responsiveness and cost efficiency. To avoid degrading this model's performance, MiniMax highly recommends preserving reasoning between turns. Learn more about using reasoning_details to pass back reasoning in our docs .
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