GPT-6.1 Terra: delivering fast and capable computer use at a lower price. GPT-6.1 Terra understands what’s on screen, takes action across apps, writes naturally, and is a more capable model for your whole workday with the efficiency to take on more of it. https://t.co/8JTN1yonmV
#Model releases#OpenAI
#Product updates#OpenAI#Model releases
OpenAI has launched GPT-6.1 Terra, a new model in the GPT-6 series positioned as an efficient model for high-throughput professional work, with strong image analysis and computer operation capabilities. The system card addendum states that under the Preparedness Framework, the model is rated Critical in the cybersecurity domain and High in the biological and chemical domains, using the same set of safeguards as GPT-6.1 Sol and GPT-6 Astra. On Production Benchmarks, its safety performance is on par with GPT-5.6 Terra, its length-corrected HealthBench Professional score is higher than that of GPT-5.6 Terra, and its MentalHealthBench score is 55.5%, higher than the latter's 46.2%.
#Model releases#Safety / alignment#OpenAI
OpenAI released GPT-6.1 Terra, with input/output prices of $1/$5 per million tokens, lower than GPT-5.6 Terra's $2/$12. On the Artificial Analysis Intelligence Index, at the highest effort level it scores 6 points higher than GPT-5.6 Terra, with a cost per task of $0.40 versus $1.40; at the low and medium effort levels it sits at the frontier of intelligence and cost. On the AA-Omniscience benchmark, the model's guess rate when it does not know the answer dropped from 88% to 38%, while accuracy simultaneously improved by 2 points; Terminal-Bench 4.0 improved by about 20 points, and Coding Agent Index v2.0 scored 48.
#Model releases#OpenAI#Evaluations / benchmarks
OpenAI has released GPT-6.1 Terra, focused on visual understanding, cross-application computer operation, and natural language writing. Its standard API price is 50% lower than that of GPT-5.6 Terra, at $1 per million input tokens and $5 per million output tokens. OpenAI says that at medium reasoning effort on Agents' Last Exam, it improves on GPT-5.6 Terra by 15 percentage points, and at the highest reasoning effort its token efficiency is more than three times that of Claude Opus 5.5, while its price per token is one quarter of the latter's. Starting October 26, the model is available to Plus, Pro, Enterprise, and Edu users of ChatGPT Work and Codex, and the API model name is gpt-6.1-terra.
#Model releases#OpenAI#Agents
#Model releases#Agents#Reasoning
#Model releases#OpenAI
#Model releases#OpenAI#Evaluations / benchmarks
#Model releases#OpenAI#Multimodal
Codex update: GPT-6.1 Terra in Work and Codex What changed: • GPT-6.1 Terra is now available in ChatGPT Work and Codex on Plus, Pro, Business, Enterprise, and Edu plans. • Combines strong image understanding with precise computer use — reason about images and screens, then act across browser, desktop, and apps. • Access via the model list under Advanced in the ChatGPT desktop app or ChatGPT Work on the web; also supported on mobile in Work. Details in thread.
#Model releases#OpenAI#English text is being prepared.
#Model releases#OpenAI#Agents
Moonshot AI has released Kimi K3.1 Pro, which rose from 44 points for Kimi K3 to 55 points on the Artificial Analysis Intelligence Index, ranking fourth and trailing Claude Opus 5.5's 58 points. The model retains the MoE architecture with 2.8 trillion parameters and 104 billion activated parameters, has a context window of 1 million tokens, and supports text, image, and video input. The success rate on Terminal-Bench 4.0 rose from 13% to 67%, API input and output prices are $2 and $10 per million tokens respectively, and the weights are scheduled for release on November 4.
#Model releases#Open-source ecosystem#Agents
Moonshot has launched Kimi K3.1 Pro, an upgraded version of July's K3. The company says it matches the strongest closed-source models on key benchmarks, and freely downloadable weights will be released by November 4. Artificial Analysis ranks it fourth on the Intelligence Index, ahead of OpenAI's GPT-6 Astra and behind only Anthropic's two 5.5-series models and Meta's Muse Wonder. Pricing is $2 per million input tokens and $10 per million output tokens, comparable to Sonnet 5.5, but its token consumption rate is higher than that of frontier rivals; AA also rated it the best publicly available cyber-defense model, with a score of 64% and no safety refusals.
#Model releases#Open-source ecosystem#DeepSeek
On October 23, 2026, Moonshot AI released the multimodal MoE model Kimi K3.1 Pro, with a context length of 1 million tokens, 2.8T total parameters, and 104B activated parameters, while also launching Kimi K3.1 Pro Highspeed with a 2 to 3 times speed improvement. This version restructured the reinforcement learning process, surpassing GPT-6 Astra on Terminal-Bench 4.0 and slightly below Claude Opus 5.5, tying with GPT-6 Astra and Claude Opus 5.5 for SOTA on OfficeQA Pro v2, and reducing hallucinations in financial scenarios by 65%. Both models are now available on Kimi.ai, Kimi Work, Kimi Code, and the API, with open-source weights planned to be publicly released by November 4, 2026.
#Model releases#Open-source ecosystem#Multimodal
🚀Excited to announce Nanbeige4.3-33B! To push the performance ceiling of small agents, our strategy in Nanbeige4.3 starts with building a powerful teacher first. Nanbeige4.3-33B delivers SOTA agentic performance among open models at ~30B parameters: * Strong results on agentic coding and cowork benchmarks. * When integrated into mainstream agent harnesses, it tackles real-world tasks with cost-efficient inference. * It will serve as a teacher for upcoming smaller Nanbeige models, starting with Nanbeige4.3-3B. https://t.co/tSToQgZf2O https://t.co/D228euvybz #LLM #Agent
#Model releases#Open-source ecosystem#Agents
Shenzhen-based company Yanxi Dynamics (Primotion), founded three months ago, has released its embodied foundation model Paimon-0, ranking first on RoboColiseum with a score of 0.7754 and scoring 0.6613 on the general manipulation sub-leaderboard, and topping RoboCasa365 with 58.1 points and VLA-Arena with an average success rate of 0.73. The model is centered on a World Action Model (WAM) and uses query token distillation to compress the inference time of a single action chunk from about 3000ms to 300ms. The company positions itself in embodied chips, proposes a device-cloud division of labor with a local brain plus a meta-brain, and plans to begin delivering its self-developed chips in 2028.
#Model releases#Embodied AI#Evaluations / benchmarks
Yanxin Matrix, under RockAI Technology, together with universities including HKUST and CMU, has released and open-sourced EdgeRock-4B. At a 256K context, its KV Cache is fixed at 72MiB, a 99.1% reduction compared with the source model Qwen3.5-4B. A single 80GB GPU can run 512 concurrent long-inference streams, with an output throughput of 12,124 tokens/s, 6.42 times that of the source model; the average pass@1 on AIME 2024–2026 is 82.9%. The model is derived from Qwen3.5-4B through staged conversion, requiring no pretraining from scratch; the weights and inference code have been open-sourced and are available for commercial use, supporting text and image inference.
#Model releases#Open-source ecosystem#Reasoning
Inception Labs releases the diffusion LLM Mercury 3. In official benchmarks, it scores 89.23 on GPQA Diamond, higher than GPT-6 Luna's 86.36 and Claude Haiku 5.5's 84.51. Its output speed reaches 1,068 tokens per second, up to about 8x that of GPT-6 Luna, its context window has been expanded to 1 million tokens, and during the launch period it costs $0.10 per million input tokens and $0.35 per million output tokens. Its total parameter count is more than 40x larger than Mercury 1, its overall capability is about 3x higher, and its speed still remains at the thousand-tokens-per-second level.
#Model releases#Reasoning#Agents
Moonshot AI released Kimi K3.1 Pro in the early hours of October 24. Artificial Analysis ranks it fourth globally on its intelligence index and judges that once the weights are released it will top the open-weight model leaderboard. On Terminal-Bench4.0, K3.1 Pro scores 65.7, second only to Claude Opus5.5's 66.4. The article also notes that K3.1 Pro completed its upgrade amid intensified anti-distillation efforts by Anthropic and other vendors, which the author views as a counterexample to the claim that Chinese models rely entirely on distillation.
#Model releases#Open-source ecosystem#Evaluations / benchmarks
China's AI 3D creation platform Hi3D has announced the open-sourcing of the V1.0 model weights and inference code, with the resources now available on GitHub and Hugging Face. The model is based on the Sparc3D architecture, and its training data has been expanded from about 500,000 to about 2 million curated 3D assets. It supports an effective sparse grid resolution of up to 1536³ and takes a reference image and a coarse mesh as input to progressively refine the geometric structure, but it outputs only the geometric model and does not generate textures, materials, or vertex colors. V1.0 uses a custom license agreement: upon exceeding 200,000 monthly active users or reaching US$1 million in monthly revenue, the licensee must notify the licensor within 60 days and apply for a separate written authorization.
#Model releases#Open-source ecosystem#Hugging Face