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Mistral Large 4 open-weight release: improved coding and safety capabilities, API price halved

Mistral AI released the open weights of Mistral Large 4 (codename Le Chonk) on Hugging Face on October 29. The model has 100 billion parameters out of a total parameter scale of the 100-billion class, with about 4.9 billion parameters actually invoked per token, using a MoE architecture with 128 experts, of which 4 are activated each time plus 1 shared expert. Compared with the October 6 preview version, Mistral says it continued reinforcement learning over three weeks, raising the Terminal-Bench 4.0 score from 28% to 41%; the Artificial Analysis coding index is 48%, on par with GLM-5.3 while using an average of 47% fewer tokens per task. API pricing drops to $0.70 per million input tokens and $2.10 per million output tokens; the new Mistral Ecosystem Open-Weight License permits commercial use, but reselling model services requires a separate agreement, and the full version requires 8 Nvidia B200 or B300 GPUs.

Google DeepMind releases Gemini 4 Carbon, topping Artificial…

Google DeepMind has released Gemini 4 Carbon, which ranks first on the Artificial Analysis Intelligence Index v5 with a score of 59 and leads in 4 of 9 evaluations including HLE-Diamond and TB-Science. The model focuses on cybersecurity and long-horizon tasks, claiming 88.2% on SEC-bench Pro, 54.8% on ExploitGym, and 71.0% on CWE-bench, and is initially available only to trusted cybersecurity personnel through the Fairwind Program. In terms of specifications, it supports a 1M token context and 1M maximum output, with input covering text, images, video, and speech, and output in text; Artificial Analysis lists a standard price of $4/$20 per million input/output tokens, plus a 50% launch discount.

Google releases Gemini 4 Carbon, topping 15 of 17 benchmarks

Google has released Gemini 4 Carbon, a frontier model aimed at cybersecurity and long-horizon coding tasks, topping 15 of Google's own 17 benchmarks, but it has not yet been made public and is available only to some cybersecurity defenders. It scores 59 on Artificial Analysis' Intelligence Index, surpassing Claude Opus 5.5 and up 9 points from Argon four weeks ago; on long-input reasoning it leads OpenAI Astra and Opus by 24 points. The introductory API price is $2 per million input tokens and $10 per million output tokens, half that of Opus 5.5, doubling after the discount period to Opus's rates. Google has not announced when it will broaden access.

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.

Google releases Gemini 4 Carbon, just two weeks after Argon

Google has released Gemini 4 Carbon, just two weeks after the release of the previous-generation frontier model Gemini 4 Argon. According to Google, Carbon surpassed Anthropic's Claude Opus 5.5 to take first place on Artificial Analysis's Intelligence Index, while Argon had previously ranked only sixth. Carbon mainly improves complex coding tasks and cybersecurity capabilities, scoring 88.2% on SEC-bench Pro, higher than Argon's 83.4% and OpenAI GPT-6 Astra's 85.4%; however, it still trails Claude Opus 5.5 on the Coding Agent Index, Terminal-Bench, and Frontiercode. Currently, Carbon is available only to selected users such as cybersecurity defense experts, and will be opened to developers, enterprises, and consumers only after passing the U.S. government's voluntary pre-review process.

Moonshot AI releases Kimi K3.1 Pro: 2.8T total parameters, 104B activated parameters, weights scheduled to be opened on November 4

Moonshot AI releases Kimi K3.1 Pro, a multimodal MoE model with 2.8T total parameters, 104B activated parameters, and a 1 million token context window. The Standard and Highspeed versions are now available on the Kimi website, App, Kimi Work, Kimi Code and API. The company says Highspeed offers 2–3x faster output at twice the price of the Standard version. Artificial Analysis ranks it fourth on the Intelligence Index with a score of 55, behind Opus 5.5's 58; on Arena's Code Arena WebDev it ranks 8th with 1692 points, up nine places from K3. Standard pricing is $2 per million tokens for input, $10 for output, and $0.2 for cached input, one-third lower than K3; the weights have not yet been released, and the company promises to open them on November 4.

Kimi K3.1 Pro released, rises to 8th on Code Arena WebDev

Moonshot AI released Kimi K3.1 Pro, which ranks 8th on Code Arena WebDev with 1,692 points, matching Claude Opus-5 (Max) while costing 70% less, and rising 9 places from Kimi K3 (Max)'s 17th. The model is priced at $2/$10 per million input/output tokens, with a median cost of $1.23 per task, 29% lower than Kimi K3, and its weights will be opened on November 4. Kimi officially calls it a 3T-class open-weight model, featuring a 1M-token context, a 2.8T total parameter/104B active parameter MoE multimodal architecture, and a 65% reduction in hallucination rate.

Kimi K3.1 released under the name 3.1 Pro, with benchmark results leading current open-weight models

Kimi K3.1 was released under the name 3.1 Pro, and Nathan Lambert believes its benchmark scores are clearly the strongest among current open-weight models, provided the weights are released by November 4 as officially stated. The Kimi team explicitly discusses cyber risk and seeks to keep the open-weight cyber capability frontier in check, keeping it restrained relative to the closed-source frontier. K3.1's per-token price is lower than K3's, but some benchmarks in the blog show that it consumes significantly more tokens per answer, so the actual price may be similar.

Moonshot AI releases Kimi K3.1 Pro: a 3T-class open-weight model with a 1 million token context

Moonshot AI releases Kimi K3.1 Pro, the first model in the K3.1 series and its strongest 3T-class open-weight model to date, with a 1 million token context window, alongside Kimi K3.1 Pro Highspeed, which is 2-3 times faster. The model is now available on Kimi.com, Kimi Work, Kimi Code and the Kimi API, with full model weights to be released on November 4. On Terminal-Bench 4.0, K3.1 Pro scores 65.7%, higher than GPT-6 Astra's 58.2% and slightly below Claude Opus 5.5's 66.4%; on the internal benchmark Kimi Code Bench 3.0 Long-horizon it scores 61.2%, higher than K3's 55.95%.

MiniMax open-sources the M3.1 Flash model, reducing usage cost by 70%

MiniMax announced on October 23 the open-sourcing of the M3.1 Flash model, which processed over 100 trillion tokens within 72 hours of launch. Compared with the previous generation M3, M3.1 Flash has comprehensively improved capabilities, with usage cost reduced by 70% and online inference speed increased by nearly 3 times. MiniMax stated it will work with chip manufacturers such as Huawei Ascend, NVIDIA, and AMD, as well as inference frameworks and communities such as vLLM, SGLang, and Hugging Face, to advance day-one open-source integration.

Baseten Base Labs releases decision models mammoth-1 and elephant-1

Baseten's Base Labs releases two decision models, elephant-1 (9B) and mammoth-1 (27B), both fully fine-tuned from Qwen3.5, with model weights publicly released. mammoth-1 ranks first among 208 models on the Jev Decision Index 0.4 with a score of 71.24, leading the second-place Perplexity Decider v1.2 by 1.8 points; elephant-1 scores 62.65 on that leaderboard and ties for first with d1-ultra on the Artificial Analysis Decision Index with a score of 65. Training used the eighth version of the data mixture with 6.21M samples, and replaced the full-attention layers with a block bidirectional attention mask; under fixed data, this change raised the public 10k-sample score from 44.8 to 48.6.

MiniMax Releases M3.1 Flash, Tested Frontend Coding Capabilities

MiniMax claimed the anonymous model Space Bunny on October 19 and officially released M3.1 Flash, which uses a sparse attention architecture with 428B total parameters and about 23B activated, nearly 3x faster inference, 70% lower usage cost, and pricing of 1 yuan per million input tokens, 4 yuan for output, and 0.1 yuan for cache reads. Model weights will be open-sourced on October 23, and the official announcement previews a Pro version with a higher intelligence ceiling coming soon. The author replaced the community-viral cases with four new questions of the same type for retesting, and believes its frontend Coding capabilities improved noticeably while retaining the self-verification behavior of proactively opening a browser to trial and error.

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MiniMax Releases M3.1 Flash Model, ALE Score of 29.6% Ranks First Among Domestic Models

MiniMax officially released M3.1 Flash, formerly known as Space Bunny (Yutu), which previously topped the charts anonymously on OpenRouter. The model scores 29.6% on Agents' Last Exam, ranking first among domestic models, and scores 43 on the AA leaderboard; it achieves 73.7% on DeepSWE v1.1, 82.2 points on CyberGym, 76.2 points on BankerToolBench, tying for first with Claude Opus 5, and 61.7 points on OSWorld V2. The model adopts a sparse attention architecture, with output speeds of up to 200+ TPS and a median of 170+ TPS, input at 1 yuan per million tokens, output at 4 yuan, and cache reads at 0.1 yuan, with comprehensive usage costs reduced by 70% compared to the previous generation M3.

MiniMax Releases M3.1 Flash, Scoring 29.6% on Agents' Last Exam to Rank First Among Chinese Models

MiniMax has officially released M3.1 Flash, the model previously known as Space Bunny (Yutu) that topped the charts anonymously on OpenRouter. It scores 29.6% on Agents' Last Exam, ranking first among Chinese models. The model achieves 73.7% on DeepSWE v1.1, 82.2 points on CyberGym, and 61.7 points on OSWorld V2, with output speeds of up to 200+ TPS and a P50 maintained at 170+ TPS. Pricing is 1 yuan per million input tokens, 4 yuan for output, and 0.1 yuan for cache reads.

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