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Mistral Releases Open Weights for Mistral Large 4 (Le Chonk)

Mistral has released the open weights for Mistral Large 4 (Le Chonk), a 1-trillion-parameter natively multimodal model that continues reinforcement learning on top of the preview version, with improved coding and defensive cybersecurity capabilities. It scores 19.7% on FrontierCode 2.0 and 48% on the Artificial Analysis Coding Agent Index, roughly on par with GLM-5.3, but uses an average of 47% fewer tokens per task; on Terminal-Bench 4.0 it improves by 13 percentage points to 41%. The model supports three reasoning modes, uses a modified MIT-based Mistral Ecosystem Open-Weight license, and is priced via API at $0.70 per million input tokens, $0.07 for cached input, and $2.10 for output.

Liquid AI Releases Six 2B-Class Open-Weight Models in the LFM3 Series

On October 27, Liquid AI released the LFM3 series. All six 2B-parameter-class open-weight models branch from the same LFM3-1.7B-Base, covering text, vision, audio, omni-modal, and decision models. LFM3-1.7B has 1.73B parameters, a 256K token context, supports 16 languages, and adopts a Tri-Hybrid architecture with 13 layers of gated short convolutions, 5 layers of sliding-window attention, and 6 layers of global attention. It is available under the LFM Open License v1.0 on Hugging Face, the Liquid AI Playground, and via API. The company claims that LFM3-1.7B surpasses LFM2.5-2.6B with 35% fewer parameters and occupies about 1 GB of memory at 4-bit precision, but the model card states that it is not suitable for agentic coding or complex multi-step reasoning.

Google Releases Gemini 4 Argon, Showcasing Ability to Handle 8 Categories of Complex Tasks

Google has released its latest model, Gemini 4 Argon, saying it is aimed at complex real-world tasks and can perform reasoning, coding, and coordinate tools. The company provided 8 examples, including writing 5,000 lines of mathematical code from scratch with zero source material to render a 3D sunlit library, and, in Antigravity, processing more than 80,000 raw files from a NASA Mars mission and generating executive briefing slides.

OpenAI Releases GPT-6.1 Terra, Cutting API Prices by 50% and Resuming Frontier Model Training

OpenAI has released GPT-6.1 Terra, which combines visual reasoning, computer use, and coding capabilities. Its API pricing is $1 per million input tokens, $0.05 per million cached input tokens, and $5 per million output tokens, 50% lower than GPT-5.6 Terra and GPT-6.1 Sol. Starting October 27, the model is available to Plus, Pro, Enterprise, and Edu users of ChatGPT Work and Codex, and developers can call it via the API as gpt-6.1-terra. On Terminal-Bench 4.0, it solves 50.3% of tasks at maximum reasoning effort, compared with the 21.5% score published for GPT-5.6 Terra; on an internal induced-error benchmark, the error rate at low reasoning effort dropped from 53.6% to 16.7%. The release comes as OpenAI resumes frontier model training after an 11-day pause over safety concerns.

OpenAI Releases GPT-6.1 Terra, Agent Tasks 3x Faster and 50% Cheaper

OpenAI has released GPT-6.1 Terra, focused on computer operation, visual reasoning, coding, and everyday office work. API pricing is $1 per million input tokens and $5 per million output tokens, 50% lower than GPT-5.6 Terra and GPT-6.1 Sol. On Agents' Last Exam, at medium reasoning effort it scores 15 percentage points higher than GPT-5.6 Terra at about one-third the cost, and completes tasks more than 3x faster than GPT-6 Astra and GPT-6.1 Sol. Internal benchmarks show the error rate at low reasoning effort dropped from 53.6% to 16.7%, and it scores 50.3% on Terminal-Bench 4.0 at maximum reasoning effort, compared with 21.5% for GPT-5.6 Terra. The model is now available to Plus, Pro, Enterprise, and Edu users in ChatGPT Work and Codex, with the API model name gpt-6.1-terra.

Kimi releases K3.1 Pro open-weight model, 2.8T total parameters, 1M token context

Kimi releases K3.1 Pro, its strongest 3T-class open-weight model, using a MoE architecture with 2.8T total parameters and 104B activated parameters, supporting a 1M token context and multimodality. Officially it is claimed to match the strongest closed-source models on coding, agent, and knowledge benchmarks, and to support full-project game development in Unreal, Unity, and Godot as well as parametric 3D modeling in Blender and CAD, with hallucinations in financial scenarios reduced by 65%. K3.1 Pro and K3.1 Pro Highspeed, which is 2–3 times faster, are already live on the Kimi official website, Kimi Work, Kimi Code, and the Kimi API, with open weights planned for release on November 4, 2026.

Kimi K3.1 Pro launches on OpenRouter, 1M context, 2.8T parameter MoE open-weight model

Kimi K3.1 Pro is now live on OpenRouter; it is a 1M-context multimodal open-weight model post-trained from Kimi K3, targeting long-horizon coding and agent tasks, priced at $2 per million input tokens and $10 per million output tokens. According to Kimi_Moonshot, the model is a 3T-class open-weight model with 2.8T total parameters and 104B activated parameters (MoE), excelling at full-project coding in Unreal, Unity, and Godot and at parametric 3D modeling in Blender and CAD, with financial modeling hallucinations reduced by 65%, and it is said to match the strongest closed-source models on coding, agent, and knowledge benchmarks. Kimi K3.1 Pro and the 2–3x faster K3.1 Pro Highspeed are now available on the Kimi platform, Kimi Work, Kimi Code, and Kimi API, with weights to be opened on November 4, 2026.

Google releases Gemini 4 Argon, showcasing 5 community creative projects

Google releases new frontier model Gemini 4 Argon, stating it performs outstandingly on complex tasks such as coding, mathematics, and 3D world building, and surpasses other frontier models on benchmarks across multiple domains. The official showcase features 5 community projects, including a 3D Earth that maps wildfires and seismic activity in real time, a turbofan engine simulation with real-time data, a 3D carriage generated from a single image, and a multiplayer capture-the-flag browser game. The model has been widely available since last week.

MiniMax publicly releases M3.1 Flash weights on Hugging Face

MiniMax has publicly released the MiniMax M3.1 Flash weights on Hugging Face; the model has 428B total parameters and 23B activated per token. The company claims generational improvements in agent tasks, coding, and cybersecurity, with scores of 76.2 on BankerToolBench, 73.7% on DeepSWE v1.1, 61.7 on OSWorld V2, 29.6 on Agents' Last Exam, and 82.2 on CyberGym. This model is the stealth model Space Bunny on OpenRouter, released on October 19, and is now available on the MiniMax platform and API.

MiniMax Opens M3.1 Flash Model Weights

MiniMax has announced that the model weights for M3.1 Flash are now open. Together AI, a day-one launch partner, has deployed the model in its AI Native Cloud production environment, so users do not need to manage GPUs themselves. According to Together AI, M3.1 Flash targets agentic tasks, coding, computer use, and cybersecurity scenarios, and supports a 1M context window and vision capabilities.

MiniMax M3.1 Flash releases open-source weights, 428B total parameters, 23B activated per token

MiniMax M3.1 Flash weights are now public, with 428B total parameters and 23B activated per token. The company claims substantial improvements in agentic tasks, coding, and cybersecurity: BankerToolBench 76.2, DeepSWE v1.1 73.7%, OSWorld V2 61.7, Agents' Last Exam 29.6, CyberGym 82.2. The model uses MiniMax Sparse Attention and DSpark speculative decoding to improve inference efficiency; the weights are now available on Hugging Face and ModelScope, with API and MCode desktop entry points also provided.

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MiniMax releases the M3.1 Flash model, at one-third the cost of M3 and 3x faster

MiniMax has released M3.1 Flash, which reaches frontier-level performance on tasks such as Cowork (Automation-Bench 62%, ALE 30%), coding (DeepSWE 74%), and visual reasoning (BabyVision 91%), at one-third the cost of M3 and 3x the speed. The unified API price is $0.15/$0.60 per 1M input/output tokens, with cache reads at $0.015 per 1M tokens. The company says Space Bunny, which previously topped global leaderboards and processed 200T+ tokens, is an early version of M3.1 Flash, and the model weights will be released on October 23.

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MiniMax Claims Blind-Test Model Space Bunny, Confirms It as MiniMax-M3.1-Flash

MiniMax officially claimed Space Bunny, a model tested anonymously on an overseas blind-test platform, confirming it as MiniMax-M3.1-Flash. The model supports a generation speed on the order of one hundred words per second and a one-million-token context; community tests measured generation speed steady at 85 to 112 words per second, capable of running through complex 3D scenes and code builds in a single pass within three minutes, and achieving near-imperceptible response in multi-file project repairs. The author believes this approach of anonymous blind testing first and public claiming afterward turns inference speed and million-token context into efficiency tools usable for everyday coding.

Memory Tensor Open-Sources MemT 1.0 Series of Memory-Native Models

Memory Tensor has open-sourced the MemT 1.0 series of memory-native models, comprising two variants: Nano with 36B parameters and 3B activated, and Flash with 288B parameters and 17B activated, built on the Qwen3.6-35B-A3B and DeepSeek-V4-Flash bases, respectively. The models add a memory branch parallel to attention within selected Transformer modules, encoding experience into an updatable matrix state that participates in subsequent generation. Across 500 SWE-bench Verified tasks, Nano achieved an average success rate of 69.73% at key checkpoints, versus a no-experience baseline of 66.07%; under a 128K long history, end-to-end inference speed is approximately 1.86 times that of the full-context approach, and the persistent state footprint per trajectory is one sixty-sixth that of the full-context KV Cache.

Meta's open-weight model Muse Spark Open launches on Fireworks: 816B parameters, 1M context

Meta's open-weight model Muse Spark Open is available on Fireworks from day one of launch. It is an 816B-parameter MoE model that activates 42B parameters per token, with a 1M-token context window, targeting long-horizon coding and agentic tasks. Fireworks says it ranks first among 75 models on Vals AI's Harvey legal agent benchmark with 25.4%, nearly 4 times that of Claude Opus 5 (6.7%); on the Rogo Big Finance Bench it surpasses Claude Opus 5.5 at every difficulty level. Fireworks validated the weights with its own production stack, scoring 91.1% on AIME26, 89.7% on GPQA Diamond, and 84.3% on SWE-bench Verified, and reproduced consistent results with SGLang.

Meta and Apple Partner to Release Open-Weight Model Muse Spark Open

Meta has released the open-weight model Muse Spark Open and worked with Apple's hardware team to enable it to run efficiently on the M5 Mac Studio Ultra. The model has 42 billion active parameters, a 1 million token context window, and multimodal capabilities. Meta says it performs well on agentic and coding tasks, and the weights have been made public on HuggingFace under Meta's Muse Model license. Running the model on an Apple computer requires two Mac Studios equipped with the M5 Ultra, each starting at $5,499.

Meta releases open-weight model Muse Spark Open

Meta released the open-weight model Muse Spark Open on Thursday, saying its capabilities can match and in some scenarios surpass comparable products from Chinese vendors. Meta AI head Alexandr Wang called it the most capable U.S. open-weight model to date; the model targets coding and agentic tasks, excels at long-horizon tasks, complex software engineering and multi-step reasoning, and has built-in safety safeguards. The model is optimized for the Apple M5 Ultra chip, and users can run it on the desktop with two Mac Studios equipped with that chip.