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NVIDIA releases the Nemotron 3.5 Transcribe speech transcription model, tying for first among open-source models with 2.8% WER

NVIDIA has released the open-weight speech transcription model Nemotron 3.5 Transcribe, with 30B total parameters and 4B activated parameters. On Artificial Analysis's non-streaming speech transcription leaderboard, it achieves a 2.8% AA-WER, tying for first among open-source models with Mistral's Voxtral Small. On the speech agent dialogue dataset AA-AgentTalk, it leads Voxtral Small's 2.3% with 1.8%. Its activated parameters are 4B, while Voxtral Small's are 24B; other open-source models of comparable scale have a WER of about 4%.

Mistral AI Opens the Mistral Large 4 Model Weights: 1T Parameters, 49 Billion Activated Parameters

On October 29 local time, Mistral AI officially opened the model weights of Mistral Large 4 (codename le Chonk). This is a natively multimodal model with 1 trillion total parameters and 49 billion activated parameters, trained from scratch on 3,800 NVIDIA Grace Blackwell GPUs in its own data center in Europe. The company says it scores 48% on the Artificial Analysis Coding Agent Index, roughly on par with GLM-5.3, but consumes 47% fewer Tokens per task on average. The model is released under a modified MIT license: personal, academic, fine-tuning, and conventional commercial use are free, while cloud providers offering MaaS resale need to obtain a separate commercial license; online API pricing is $0.70 per million input Tokens and $2.10 per million output Tokens.

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.

Mistral Releases Weights for Trillion-Parameter Open Model Mistral Large 4, Focused on Cybersecurity

Mistral has released the weights for its trillion-parameter open-source model Mistral Large 4 (ML4, nicknamed Le Chonk), which enterprises and government agencies can download and deploy themselves; the weights are already available on Hugging Face and via its API. Mistral says the model's performance is comparable to that of the open-source model GLM-5.3, but it consumes 47% fewer tokens per task, and it ranks first among open-source models on Artificial Analysis's Coding Agent Index. Pierre Stock, Mistral's Vice President of Science, said that open weights mean organizations can deploy locally in their own environments to identify, coordinate, and respond to major security incidents in real time.

Diffusion Intelligence releases Clover-35B-A3B-Preview, a 35-billion-parameter diffusion language model

Diffusion Intelligence releases the diffusion language model Clover-35B-A3B-Preview, which uses a MoE architecture with 35 billion total parameters and about 3 billion activated per token, and supports a thinking mode. On the 49 tasks of the web Agent benchmark WindTunnel, its median execution time was 1.52 seconds, lower than the 3.21 seconds of the Jev + Mercury 2.5 combination, with success rates of 98.6% and 95.9% respectively. On code benchmarks, it scored 73.6 on SWE-bench Verified and 51.7 on Terminal-Bench 2.1, and the team said it will subsequently launch a multimodal version and advance on-device adaptation.

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.

Leiphone.com's In-Depth Breakdown of Google DeepMind's World Model Genie 4

Google DeepMind has released Genie 4, a new-generation world model that can generate interactive environments in real time and collaborates with Gemini 3.8 Flash through a division of labor: the latter observes the visuals, decomposes tasks, and adjusts plans. Leiphone.com analyzes its technical challenges from three aspects—dynamic language condition injection, Agent causal hallucination, and dual-model asynchronous collaboration—and notes that DeepMind has not yet disclosed the network architecture or scheduling implementation of Genie 4.

OpenAI releases GPT-6.1 Terra, with API pricing at half that of GPT-5.6 Terra

On October 26, 2026, OpenAI released and made available GPT-6.1 Terra, strengthening visual understanding, computer operation, and writing capabilities. The standard API price is half that of GPT-5.6 Terra, namely $1 per million tokens for input, $0.05 for cached input, and $5 for output. The model has a context window of 1.05 million tokens, a maximum input of 922,000 tokens, and a maximum output of 128,000 tokens. Reasoning effort can be selected from none to max, with medium as the default. On Agents' Last Exam, the medium setting scores 15 points higher than GPT-5.6 Terra while costing about one-third as much; under the max setting, the average speed for completing tasks is more than 3 times that of GPT-6 Astra and GPT-6.1 Sol. The model is already available to Plus, Pro, Enterprise, and Edu users of ChatGPT Work and Codex, and the API model ID is gpt-6.1-terra.

OpenAI releases GPT-6.1 Terra, focusing on low-cost computer operation capabilities

OpenAI has released GPT-6.1 Terra, with API pricing at $1 per million input tokens and $5 per million output tokens, representing a 50% and approximately 58% decrease in input and output prices respectively compared to the previous generation, and only half that of GPT-6.1 Sol. The company claims its task completion rate on Terminal-Bench 4.0 increased from 21.5% to 50.3%, and it scored 60.0% on Agents' Last Exam, close to Claude Opus 5.5's 63.2%. Artificial Analysis measured an intelligence index of 48 at the highest reasoning tier, lower than GPT-6.1 Sol's 52 and Claude Opus 5.5's 58, but the average cost per task dropped from $1.40 to $0.40. Terra is now available via API, and Plus, Pro, Enterprise, and Edu users can use it in ChatGPT Work and Codex; it is not yet supported in regular Chat conversations.

OpenAI Releases Low-Cost Model GPT-6.1 Terra, Cost per Task About One-Ninth That of Claude Opus 5.5

On October 27, OpenAI launched a new model, GPT-6.1 Terra, focused on completing complex computer operation tasks at lower cost, with particular strength in visual reasoning and desktop software operation. At medium reasoning effort on Agents' Last Exam, its score is 15 percentage points higher than GPT-5.6 Terra, at about one-third the cost of the latter; at the highest reasoning effort, the cost per task is about one-ninth that of Claude Opus 5.5. The model is now available to Plus, Pro, Enterprise, and Edu users, can be used in ChatGPT Work and Codex, and has the API model identifier gpt-6.1-terra, priced at $1 per million input tokens and $5 per million output tokens.