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OpenAI Releases GPT-6.1 Terra Model, API Price 50% Lower Than Previous Generation

OpenAI has launched a new model, GPT-6.1 Terra, focused on visual reasoning, computer operation, and writing capabilities. The standard API price is $1 per million input tokens and $5 per million output tokens, 50% lower than GPT-5.6 Terra and GPT-6.1 Sol. At medium reasoning effort on Agents' Last Exam, its computer operation score is 15 percentage points higher than GPT-5.6 Terra, performing on par with GPT-6 Astra while costing only one-tenth as much per token. The model is now available to Plus, Pro, Enterprise, and Edu users of ChatGPT Work and Codex, but GPT-6.1 Sol and GPT-6 Astra are still recommended for the most complex programming tasks.

OpenAI Releases GPT-6.1 Terra, API Price at Half of GPT-6.1 Sol

OpenAI has released GPT-6.1 Terra, replacing GPT-5.6 Terra, with API pricing at $1 per million input tokens and $5 for output, half that of GPT-6.1 Sol. The model supports a 1.05M token context and a maximum output of 128K, targets visual reasoning, computer operation and writing, and is offered only through OpenAI-hosted API, ChatGPT Work and Codex. On Agents' Last Exam v1 it scores 60.0% at the highest effort tier at $1.01 per task, and 85.5% on MMMU-Pro vision, but only 50.3% on Terminal-Bench 4.0, trailing GPT-6.1 Sol and Claude Sonnet 5.5.

OpenAI Releases GPT-6.1 Terra: Enhanced Computer Operation Capabilities, API Price Cut in Half

OpenAI has launched a new model, GPT-6.1 Terra, focusing on improving visual understanding and computer operation capabilities, with the standard API price reduced by 50% compared to both GPT-5.6 Terra and GPT-6.1 Sol. 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; at the highest reasoning effort on Terminal-Bench 4.0, it completes 50.3% of tasks. The model is available starting today to Plus, Pro, Enterprise, and Edu users of ChatGPT Work and Codex, with the API model name gpt-6.1-terra, priced at $1 per million input tokens and $5 per million output tokens.

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.

OpenAI GPT-6.1 Terra Launches on OpenRouter

OpenAI's GPT-6.1 Terra is now available on OpenRouter, matching GPT-6 Astra on Agents' Last Exam at one-tenth the price per token. The model completes tasks on average more than 3 times faster than Astra and GPT-6.1 Sol, with pricing at $1/M input and $5/M output, and a context window of 1.05M.

OpenAI Releases GPT-6.1 Terra, Standard API Price Cut 50% Compared with GPT-5.6 Terra

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.

GPT-6.1 Terra Released: Agent Benchmark Improves by 15 Points Over 5.6 Terra, at About One-Third the Cost

GPT-6.1 Terra has been released, focused on building faster and cheaper agents that can reason over images and operate a computer. It scores 15 points higher than 5.6 Terra on Agents' Last Exam, at about one-third the cost of the latter. API pricing is $1/M for input, $0.05/M for cached input, and $5/M for output, and it is gradually rolling out in the paid plans of ChatGPT and Codex.

OpenAI Releases GPT-6.1 Terra with Halved Pricing and Hallucination Rate Dropped to 38%

OpenAI has released GPT-6.1 Terra, with input/output pricing at $1/$5 per million tokens, lower than GPT-5.6 Terra's $2/$12, with cache writes reduced to $1.25 and cached input reduced to $0.05. Artificial Analysis measured that at maximum effort, the model's intelligence index is 6 points higher than GPT-5.6 Terra, with per-task cost of $0.40 versus $1.40, and gains of 7 to 13 points at the low effort tier. In AA-Omniscience, the model's guessing rate on questions where it does not know the answer is 38%, down from 88%, while accuracy improved by 2 points; it scored 48 on the Coding Agent Index v2.0, and improved by about 20 points on Terminal-Bench 4.0.

OpenAI Releases GPT-6.1 Terra, Further Improving Cost Efficiency

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.

Kimi Releases 3-Trillion-Parameter Model K3.1 Pro, Launching a Pro Version for the First Time

In the early hours of October 24, Moonshot AI (Kimi) officially launched Kimi K3.1 Pro, calling it its most capable 3-trillion-level parameter model to date, supporting a 1-million context window, with notable improvements in coding, 3D creation, financial research, and long-horizon Agent capabilities. Kimi K3.1 Pro is a post-trained version of Kimi K3; through enhanced post-training compute and technical optimization, it achieves comprehensive improvements across all domains from large-scale RL. This is the first time Kimi has introduced a Pro model in its model versions, and it is also the first model in the K3.1 series, leaving room for subsequent cost-effective models.

Moonshot AI releases Kimi K3.1 Pro with 3-trillion-level parameters and a 1-million-token context

Moonshot AI has released Kimi K3.1 Pro, a new-generation open-source large model with 3-trillion-level parameters that supports a 1-million-token context window, retaining its title as the open-source model with the largest parameter scale in the world. The model is a post-trained version of Kimi K3, with a substantial increase in post-training compute, and it shows notable improvements in coding, 3D creation, financial research and long-horizon Agent capabilities; across multiple international benchmarks, its overall intelligence level is now approaching that of the world's leading closed-source models. The report also noted that OpenRouter statistics show global total AI large-model API calls from October 12 to 18 reached 164 trillion tokens, and that the weekly call volume of Chinese large models has exceeded that of the United States for 25 consecutive weeks.

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 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.

Recursive releases its first model Flint: local coding MoE, 125+ tokens/s on a MacBook

Recursive releases its first model Flint, a MoE with 40 billion total parameters and 2.5 billion activated parameters per token, aimed at local Agent coding, with weights open-sourced under Apache 2.0. With short context, it exceeds 125 tokens per second on an M4 Pro MacBook Pro and over 150 on a 64GB RTX Spark, and supports llama.cpp, Ollama, LM Studio, vLLM, SGLang, and Unsloth. Nearly every stage of its R&D involved the automated research system Eureka Machine, including architecture search, quantizing the output layer to 4 bit with offline distillation, and fusing Metal kernels for M4/M5.

Kling 4.0 Launches on the fal Platform

Kling 4.0 is now available on the fal platform. It is a new-generation native multimodal video model that improves the stability of complex motion, reference consistency for characters and scenes, and control precision for cinematic storytelling. It is currently available only through fal UX and fal Agent, with API access coming soon.

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DeepSeek-V4.1-Flash Tops Arena's Open-Source Model Leaderboard

Arena announced that DeepSeek-V4.1-Flash-20261017 (Max) has entered the Arena, ranking first among open-source models in both the Agent Arena and Code Arena: WebDev leaderboards. Arena also mentioned that the 3D world generated by this model, as well as more detailed ranking information, can be viewed in the related post.

ElevenLabs Releases Two Real-Time Speech-to-Speech Models, Eleven Realtime

ElevenLabs has released two speech-to-speech models, Eleven Realtime, among which v1 Max scores 98.8% on Big Bench Audio with a response time of 0.57 seconds. The smaller v1 Mini targets low-cost natural conversation and can tell whether the other party has finished speaking or only said "mm-hmm," while v1 Max targets more complex tasks such as customer service. Both models are available through the ElevenAgents platform and the OpenAI-compatible Realtime API, billed per minute plus tokens, with v1 Max priced higher than v1 Mini.

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.

English translation is being prepared.