#模型发布

First Publicly Available Neuron-Derived AI Video Model

The Biological Computing Co. (TBC) launched TBC mischief 1, an image-to-video model built on Wan 2.2 that generates video 5x faster than its base model and is available on fal and OpenRouter at half a cent per second. TBC says the speed and cost gains come from efficiencies it discovered by measuring living neurons in the lab and applying them as proprietary software that adds less than 0.1% to the base model's parameter count. The company says it is already applying the process to newer open-weight video models and developing applications in language models and physical AI.

Deep Principle Releases Hydrogen-1.0: A 753B Open-Weight Model for Scientific Discovery

Deep Principle has released Hydrogen-1.0, an MIT-licensed 753B-parameter open-weight model built by post-training Z.ai's GLM-5.3 on chemistry, materials science and life sciences data, laboratory records and agent discovery trajectories, with a 1M-token context and 128K max output. Deep Principle reports it scores 38.3% on FrontierScience-Research versus 33.3% for GPT-6-Astra and 93.0% on GPQA-Diamond, while trailing GPT-6-Astra by 3.33 points on NMR structure elucidation. Weights are on Hugging Face and ModelScope, with BF16 serving tested via SGLang on 8 GPUs, NVFP4 builds for NVIDIA B200/B300 and MXFP4 for AMD MI350X/MI355X, though full datasets and recipes are not released.

Avartha: Fastest open-weight frontier voice AI beats OpenAI, Google, and xAI

Avartha announced Ava Zero, a voice agent built entirely on open models that ranks #1 on Sierra's τ³-Voice benchmark with 86.0% task completion across six languages, ahead of OpenAI's GPT Live 1 (79.9%), xAI's Grok (71.4%), and Google's Gemini (68.5%). The Georgia Tech spinout says it co-designed its inference engine and agent harness as one system, supporting up to an order of magnitude more concurrent conversations per GPU than vLLM and SGLang, and prices Ava at $2.50 per hour of conversation. Its benchmark submission combines Qwen3-ASR, Fish Audio Drama 3, GLM-5.3, and DeepSeek 4.1 Flash.

Nace AI Open-Sources Drex Max v1: A 27B Decision Model That Runs Locally on Apple Silicon

Nace AI has released Drex Max v1, a dense 27B decision model with a 262,144-token window that returns option probabilities in one forward pass, as open weights under Nace.AI Open RAIL-M, shipping bf16 weights, a Q8_0 GGUF, an affine Q4 checkpoint and forks of llama.cpp and Ollama. It runs on NVIDIA GPUs, Apple Silicon Macs (24 GB+ for affine Q4, 48 GB for Q8_0) and CPU, and Nace reports 67.52 on the public Decision Index 0.4, ahead of Drex v2.0 at 61.25. Local Mac and CPU paths are text only for now.

Show HN: JJ Suna AI – Open 5B Flow-Matching DiT for 48 KHz Music Synthesis

JJ coders Group published the terms, privacy, DMCA and AI-ethics framework for Suna AI (suna.jjcoders.com), an open 5B flow-matching DiT for 48 KHz music synthesis whose foundation weights are released as jjcoders/suna-v1. The license grants a worldwide, non-exclusive, perpetual, royalty-free right to use, modify and distribute synthesized instrumental audio for personal and commercial projects, with the service provided "as is" and liability capped at fees paid in the prior 12 months or $100, whichever is less. The documents also cover ephemeral GPU-RAM-only processing of uploaded vocals, GDPR/CCPA rights, a DMCA agent at [email protected], and prohibitions on unauthorized vocal clones under the Tennessee ELVIS Act, BIPA and COPPA.

Samaya AI Releases Sterling: A 3B-Active Finance Research Agent Model that Beats Claude Fable 5.1 on FrontierFinance at 11x Lower Cost

Samaya AI released Samaya Sterling, a LoRA post-trained version of NVIDIA's Nemotron 3.5 Lightning (30B total, 3B active parameters) tuned as a financial research agent. On Samaya's self-reported FrontierFinance benchmark, Sterling scores 58.9% (60.6% in high-effort mode) versus 55.9% for Claude Fable 5.1, at $0.61 per question against $6.89. Training used LoRA on Tinker across two SFT stages and two GRPO stages, with a harness adapted for the small model.

Edge0 Releases Audio8-TTS-Realistic, a 0.6B Voice Cloning TTS Model

Edge0 has released Audio8-TTS-Realistic, a 0.6B-parameter open-weight text-to-speech model for zero-shot voice cloning from one reference clip and transcript, supporting 23 languages under Apache 2.0. It uses a dual-autoregressive design on Qwen2.5 0.5B and a three-stage training pipeline whose final GRPO stage is guided by a learned realism critic. Edge0 reports 4.86% average error on CV3 across 9 languages, ahead of Qwen3-TTS-1.7B (7.73%) and VoxCPM2 (5.35%), though larger models like Higgs TTS 3 still lead and Thai WER is near 49%.

Baseten Releases joey-1, a 2B Decision Model at $0.01 per 1M Tokens

Baseten's Base Labs released joey-1, a 2B decision model post-trained from Qwen3.5-2B that returns a label and calibrated probability for choice, yes/no and score questions instead of generated text, priced at $0.01 per 1M input tokens with MIT-licensed weights on Hugging Face. Baseten reports 20 ms P50 latency for an average 2,000-token request and a score of 63.30 on the Jev Decision Index 0.4 public split, ranking #37 of 230 models and top under 4B, though it trails its 9B sibling elephant-1 by 8.94 points. It runs on Baseten Model APIs, dedicated deployments (262k context) or self-hosted on 1 CUDA GPU, and is also listed on OpenRouter.

Introducing SFX v3 in ElevenCreative and ElevenAPI

ElevenLabs launched SFX v3, its newest sound effects model, which handles prompts describing multiple sounds more reliably, generates music and vocals more convincingly, and produces cleaner, more natural audio than SFX v2 in benchmark testing. It is available now in ElevenCreative (Flows, the Sound Effects tab, Studio, the Music Editor, and the mobile app) and through ElevenAPI for developers.

Mistral releases weights for its trillion-parameter AI model, targeting cybersecurity

Mistral has released the weights for Mistral Large 4 (ML4), nicknamed "Le Chonk," a trillion-parameter open-weight model that businesses and governments can download and deploy themselves, available on Hugging Face and via its API. Mistral says the model is comparable in performance to Chinese open-weight competitor GLM-5.3 while using 47 percent fewer tokens per task, and claims it outranks all other open models on Artificial Analysis' Coding Agent Index for agentic coding tasks. The company positions ML4 for sovereign cyber defense, with VP of Science Pierre Stock saying open weights give organizations access to frontier intelligence "that can't be cut off by definition."

Featured coverage of this story: 《Le Chonk: Built for Coders, Trusted by Defenders.》

Decisions for a penny: introducing joey-1, our fastest decision model

Base Labs introduces joey-1, a decision model with 3B or fewer parameters that returns a label and calibrated probability score in a single forward pass, serving an average 2,000-token request at a P50 end-to-end latency of 20 ms (about 2.5x faster than elephant-1) at $0.01 per million tokens. Post-trained from Qwen3.5-2B using the same data and recipe as elephant-1 and mammoth-1, it is available on Baseten Model APIs with open weights on Hugging Face, and supports a 16k context window on Model APIs versus the full 262k on dedicated deployments.

Paris-based Mistral releases "Le Chonk," a strong coding model that still trails China overall

Mistral released the weights of Mistral Large 4, nicknamed "Le Chonk," a roughly one-trillion-parameter multimodal model (52 billion active per step) that it says leads open models on coding, scoring 41 percent on Terminal-Bench 4.0 and 19.7 percent on FrontierCode 2.0 while trailing Claude Opus 5.5. Independent testing by Artificial Analysis put the final version at 26 points on its Intelligence Index, behind Chinese open models DeepSeek V4.1 Flash and GLM-5.3 at 32 and MiMo-V2.6-Pro at 30, though it is fast at 161 tokens per second and cheaper per task than the preview. The model is under a modified MIT license requiring a separate license for commercial cloud hosting, and API pricing is $0.70 per million input tokens and $2.10 per million output tokens.

Welcome Le Chonk: Mistral Large 4 on Hugging Face

Mistral Large 4 ("Le Chonk") is a multimodal, multilingual Mixture-of-Experts model with about 1T total and 49B active parameters and a 1M-token context window, released in FP8 and NVFP4 variants under the Mistral Ecosystem Open-Weight License. It combines a 72-layer text decoder using MLA with a DeepSeek Sparse Attention indexer and a 1.6B Pixtral vision encoder, and the post reports 82% on the Artificial Analysis Cyber Index vulnerability reproduce-and-patch test and 93% on Cybench. Day-0 support covers Transformers, vLLM, llama.cpp, TRL fine-tuning, Inference Endpoints and Dell Enterprise Hub, with GGUFs from Unsloth and the llama.cpp team.

Le Chonk: Built for Coders, Trusted by Defenders.

Mistral released the open weights of Mistral Large 4 (Le Chonk), a 1-trillion-parameter natively multimodal model trained from scratch in Europe, reporting leading open-weight results on coding benchmarks such as FrontierCode 2.0 (19.7%) and Terminal-Bench 4.0 (41%), plus 51% on the Artificial Analysis Cyber Index. It offers three reasoning modes (Low, Medium, High) and is priced at $0.70 input / $0.07 cached input / $2.10 output per million tokens under a modified MIT license that requires a separate commercial license for Model-as-a-Service offerings.

Granite Switch 4.1.1 — Update models like you update software

Granite Switch 4.1.1 is a backwards-compatible patch release to Granite Switch 4.1 that adds an updated Granite Speech ASR model covering multilingual speech-to-text across 10 languages with native punctuation and capitalization, plus an updated Decision adapter in granitelib-core with improved performance on the Jev Decision Index test set. Apart from training the new ASR model and Decision adapter, nothing was recomputed and the base model is untouched. The team says it is working on tutorials and an Adapter SDK so developers and third-party providers can build their own adapters.

Block Unveils Dessa, a Full-Duplex Speech Model Trained Only on Speech

Block announced on October 23, 2026 a research preview of Dessa, a full-duplex speech-to-speech model trained only on human conversation audio, with no text and no pretrained models. Block says it trained Dessa on one NVIDIA GB200 NVL72 rack and that it runs locally on a MacBook Pro M4 Max or better, faster than real time. No benchmark scores, parameter count, weights, paper, API or price have been published, and access is via a waitlist.

[AINews] Gemini 4 Carbon tops leaderboards, launches for cyber defenders

Google DeepMind released Gemini 4 Carbon, a frontier model that takes the #1 spot on Artificial Analysis' Intelligence Index v5 with a score of 59, two weeks after Argon. Access is initially limited to trusted cyber defenders through the Fairwind Program, with claimed results of 88.2% on SEC-bench Pro, 54.8% on ExploitGym and 71.0% on CWE-bench, a 1M-token context window, and pricing listed at $4/$20 per million tokens with a 50% launch discount. Independent evaluations place it first in Text Arena and Vending-Bench 2 but behind Opus 5.5 on coding-agent and FrontierSWE benchmarks, and its hallucination rate rose from Argon's 4% to 11%.

NVIDIA Releases Nemotron 3.5 Transcribe: An Open 30B MoE Model for 40-Language Transcription with ~4B Active Parameters

NVIDIA released Nemotron 3.5 Transcribe, an open-weights speech LLM with about 34B total and 4.3B active parameters that handles 40 languages and audio up to 1 hour, with text instructions selecting verbatim, speaker-attributed, contextual or translated output. NVIDIA reports 4.71% mean WER on 9 public English Open ASR sets, the best open-weights score, while commercial APIs still lead, and notes weak Mandarin meeting accuracy (34.95% cpCER on AliMeeting) and offline-only operation. Weights ship in BF16, FP8, NVFP4 and GGUF under the OpenMDW-1.1 license, with runtimes including vLLM 0.32.0, Transformers and SGLang-Omni 0.2.0.

Google launches Gemini 4 Carbon with a ‘significant leap’ in cybersecurity

Google released Gemini 4 Carbon, a frontier model it says offers significant gains in coding, agentic tasks, and cybersecurity, and which tops the Artificial Analysis Intelligence Index (v5.0) benchmark ahead of Claude Opus 5.5 and GPT-6 Astra. It is currently available only to a select group of cybersecurity defense companies, with plans to expand to other partners in its Fairwind Program, and Google says it leads SEC-bench Pro and ExploitGym while running longer multi-step tasks and cutting unsupported claims by two times versus Argon. It launches at the same introductory price as Argon, $2 per million input tokens and $10 per million output tokens.

Google Deepmind releases Gemini 4 Carbon just two weeks after Argon with gains across the board

Google DeepMind shipped Gemini 4 Carbon two weeks after Argon; it tops the Artificial Analysis Intelligence Index v5.0 with 59 points versus Argon's 50, and leads 15 of 16 of Google's own benchmarks, especially in long-context and security tasks. Independent evaluations show it second on Vals AI (70.36%) and the Agent Arena (+16.2%), fourth in the Code Arena's WebDev category (1,768), and still slightly behind Claude Opus 5.5 in agentic coding, while costing $7.76 per task at the same introductory price as Argon ($2/$10 per million input/output tokens). Google is rolling it out first to selected cybersecurity experts and through the Fairwind Program before wider release.

Featured coverage of this story: 《Gemini 4 Carbon: Our next frontier model》

Google DeepMind Introduces Gemini 4 Carbon, Its Top Model for Cyber Defense and Coding

Google DeepMind introduced Gemini 4 Carbon, a frontier model built for long-horizon agentic coding and cybersecurity defense, which ranks #1 on the v5.0 Artificial Analysis Intelligence Index with a score of 59. It leads 15 of 17 published benchmarks versus Gemini 4 Argon, GPT-6 Astra, Claude Opus 5.5 and Claude Fable 5.1, including SEC-bench Pro (88.2%), ExploitGym (54.8%) and GraphWalks (96.2%), though it trails Claude Opus 5.5 on OSWorld-2.1 (81.5% vs 87.2%) and GPT-6 Astra on FrontierSWE (61.2% vs 65.5%). Access begins with a vetted group of cyber defenders and the Fairwind Program rather than the public API, at an introductory price of $2 per million input tokens and $10 per million output tokens.

Featured coverage of this story: 《Gemini 4 Carbon: Our next frontier model》

How your transcript knows where each word is spoken

ElevenLabs introduced Cue, a forced alignment model that places every character in an audio file on a precise timeline, powering its Scribe speech-to-text family and available through a Forced Alignment API. ElevenLabs says Cue was more accurate than every aligner it tested across all 29 benchmark languages, handles recordings of any length in a single pass, and aligns an hour of audio in under a second. It also aligned songs in English, French, Spanish and German in one pass, placing 92.7% of words within 0.3 seconds of the label.

Google announces Gemini 4 Carbon, its latest frontier AI model that you can't use

Google announced Gemini 4 Carbon, an improvement on Gemini 4 Argon released two weeks earlier, with a staged release that may take time. Google says Carbon shows a significant leap in vulnerability detection, validation and remediation, is twice as likely as Argon to generate vulnerability-free code, and gained 89 points on WebDev Arena to rank fourth. Carbon edges out Argon and beats GPT-6 Astra, Claude Opus 5.5 and Claude Fable 5.1 in almost every test, though the article notes the margins are just a few points.

Gemini 4 Carbon: Our next frontier model

Google announced Gemini 4 Carbon, its strongest model with state-of-the-art cybersecurity and long-horizon capabilities, ranking #1 on the Artificial Analysis Intelligence Index v5.0 at 59 and scoring 88.2% on SEC-bench Pro, 54.8% on ExploitGym, and 71.0% on CWE-bench. It launches at Argon's introductory price of $2 per million input tokens and $10 per million output tokens, initially available to a subset of cyber defenders and trusted partners through the Fairwind Program before wider release. Google reports reduced misalignment alerts, a 0.0% prompt-injection attack success rate on Gray Swan's IPI Benchmark, and 0.0% successful cheating on Terminal-Bench 4.0.

Nvidia's open Nemotron 3.5 Transcribe combines transcription, translation, and speaker recognition

Nvidia released Nemotron 3.5 Transcribe, an open speech model that follows text instructions to transcribe 40 languages, translate 31 to or from English, identify speakers in 13, and answer questions about recordings up to an hour long with up to eight speakers. The 30-billion-parameter model activates about 4 billion parameters per step, runs on a single Nvidia GPU, and ranks first among open-weight models on Hugging Face's English Open ASR Leaderboard with a 5.33 percent average word error rate. Nvidia also released the Granary v2 dataset of about 844,000 hours of speech in 48 languages plus dataset-building tools, and reported that targeted fine-tuning on recordings of Indian doctors and medical students cut error rates from 30.8 to 11.8 percent in about 14 GPU hours on one GB300.

Voice AI in vogue: Nvidia releases new open transcription model

Nvidia released Nemotron 3.5 Transcribe, a free, open-weight model that transcribes audio in up to 40 languages and translates 31 languages to and from English, alongside version 2 of its open source Granary speech dataset. Nvidia claims it handles recordings up to an hour long, distinguishes up to eight speakers, and beats rivals on benchmarks including Hugging Face's Open ASR Leaderboard; the 30-billion-parameter model runs on a single chip but works with recorded audio, not livestreams. The release targets developers building business meeting and medical transcription apps and joins other recent voice model launches from Mistral, ElevenLabs and Salesforce.

NVIDIA Nemotron 3.5 Transcribe and Granary v2: Intelligent Multilingual Transcription You Can Prompt and Adapt

NVIDIA released Nemotron 3.5 Transcribe, an open-weight, promptable model that handles transcription in 40 languages, translation of 31 to or from English, speaker attribution in 13 languages, and audio question answering in one checkpoint, running on a single NVIDIA GPU. It ranks first among open-weight models on the Hugging Face Open ASR Leaderboard for English at 5.33% average WER and posts the lowest diarization error rate on David AI's 21-language leaderboard. The release also includes Granary v2, an open data curation pipeline covering 463,062 hours across 48 languages, and NeMo Speech fine-tuning recipes that cut held-out WER by 45.8% relative to the best prompt in a medical case study.

Meet Google DeepMind’s Genie 4: Real-Time World Model With Native Audio

Google DeepMind released Genie 4 as a research preview for trusted testers; it generates explorable worlds from text and image prompts in real time, along with ambient audio, music and character dialogue. A Gemini model runs inside the simulation loop as a planner, using Gemini 3.8 Flash to break goals into steps, verify frames and replan, while Gemini Omni anchors each world's look and behavior. DeepMind disclosed no parameters, resolution, frame rate or benchmarks, and lists drift in longer sessions and interaction limited to a few minutes as open problems.

Featured coverage of this story: 《Genie 4: A new generation of world simulation》

Liquid AI Releases LFM3: 6 Open-Weight 2B-Class Multimodal Models

Liquid AI released LFM3, a family of six open-weight 2B-class models built on one shared tri-hybrid backbone covering text, vision, audio, omni and decision tasks, each fine-tunable on a single GPU. LFM3-1.7B has 1.73B parameters and a 256K context window, runs at about 1 GB of memory at 4-bit, and averages 49.37 across 29 benchmarks (Liquid's own eval), above Qwen3.5-9B at 47.24, while trailing it on GPQA Diamond (63.37 vs 80.40) and SWE-bench Verified (43.40 vs 51.20). Weights are on Hugging Face, the Liquid AI Playground and the Liquid API, with GGUF, MLX, ONNX, FP8 and NVFP4 checkpoints, and free commercial use stops at $10M annual revenue under the LFM Open License v1.0.

Google Deepmind's Genie 4 puts a modern spin on agent designs that predate LLMs

Google Deepmind introduced Genie 4, a real-time world model that renders scenes frame by frame in response to keyboard and text-prompt input, generates its own background audio, and simulates talking characters. Gemini models plan the simulation in the background to keep it coherent, with Gemini 3.8 Flash checking multistep goals and Gemini 4 Argon tested in a demo where Genie 4 simulates Apptronik's Apollo robot playing checkers. Deepmind calls Genie 4 an early experimental model that stays consistent over short sessions but can drift in longer ones; it is available to select testers as a research preview, with SynthID watermarking on all output.

Build specialized multimodal models with LFM3

Liquid AI introduces LFM3, a generation of Liquid Foundation Models in the 2B parameter range that places text, vision, audio, omni, and decision models on one shared backbone built from LFM3-1.7B-Base, which was pre-trained on 22T tokens and uses a tri-hybrid layer mix with a 256K context window. The release includes six 2B-class models, new in-house vision and audio encoders, and LiquidCodec, with every model fine-tunable on a single GPU and the text model fitting in about 1 GB at 4-bit precision. All LFM3 models are open-weight and available on Hugging Face, with fine-tuning notebooks and agent harness guides provided.

DeepMind unveils Genie 4 world model promising more realistic physics and humans

Google DeepMind has revealed Genie 4, an updated world model that improves on Genie 3 in almost every way, with more realistic physics and humans, and which paying Google customers may be able to use before too long. DeepMind frames world models as a key element toward AGI: they generate a virtual world frame by frame in real time as you explore it, without pre-rendered environments or programmed interactive elements, and could create infinite training environments for physical robots or enable rapid prototyping. The improvements are due in part to Google's updated Gemini models.

Featured coverage of this story: 《Genie 4: A new generation of world simulation》

Google’s Genie 4 world model adds audio, more realistic worlds, and real-time steering

Google DeepMind released Genie 4 in research preview, a world model that generates photorealistic, interactive worlds with real-time audio and lets users type prompts mid-simulation to change scenes, weather, camera angles, or add objects. It maintains a consistent scene for up to two minutes, double Genie 3's limit, and integrates with Gemini, which plans steps while Genie creates visuals; a Gemini Robotics experiment had a Gemini 4 Argon model break tasks into steps that Genie 4 simulated in real time. Genie 4 is available now to trusted testers and will roll out in the coming weeks to Google AI Ultra subscribers in Project Genie and to early access partners of the Gemini API and Google AI Studio.

Featured coverage of this story: 《Genie 4: A new generation of world simulation》

Google’s next Genie AI model generates more realistic experiences

Google announced Genie 4, a world model for generating interactive experiences, with four main updates: more realistic worlds, real-time updates via natural language prompts, audio generation, and Gemini 3.8 Flash under the hood. It can create experiences about two minutes long, up from one minute for Genie 3, and Google pitches it for uses like filmmaker pre-visualization, game prototyping, and robot training scenarios, while warning that details and object permanence can shift over longer simulations. Genie 4 is launching first as a research preview to trusted testers, will be "coming soon" to Google AI Ultra subscribers, and Google has opened a waitlist to try it in Project Genie.

Genie 4: A new generation of world simulation

Google announced Genie 4, a natively multimodal world model that generates photorealistic, real-time interactive simulations with native audio, including ambient sound and interactive dialogue, and builds on Gemini models for reasoning and coherence. It is rolling out as a research preview to trusted testers, with plans to reach Google AI Ultra subscribers in Project Genie and early access partners via the Gemini API and Google AI Studio. Google notes limitations such as drift in visual details and object permanence over longer sessions, and says all generated video and audio include SynthID watermarking.

Mirendil Launches Fern 0.1, Its First Model, With 10M-Token Context

Mirendil released Fern 0.1, a from-scratch coding model with a context window of up to 10M tokens (about 23 MB of code), built on Mirendil-Base after a short round of post-training and available only through hosted early access. Mirendil says one researcher pretrained the base model in 3.5 months for under $10M, with AI agents writing over 99% of the code and training data drawn only from public sources. The base model scores 100% on RULER single-needle retrieval from 32K to 10M tokens, while Fern 0.1 scores 6.1% on Terminal Bench 4.0 and 73.4% on SWE-Bench Verified, trailing the frontier and large open models in its comparison table.

vLLM Semantic Router Releases d3-ultra: A 27B Open Multimodal Decision Model

The vLLM Semantic Router team released d3-ultra, a 26.09B-parameter dense multimodal decision model under Apache-2.0 that does not generate text but reads an input once and returns probabilities for typed questions (choice, yes/no, score, box, track), with 131,072 tokens of context per question. It scored 87.2 on the team's private SR-Text test, the highest of six decision models compared, but trailed its sibling d3-turbo on SR-Box grounding (58.2% vs 60.0% over 10 domains). It was tested on one AMD Instinct MI325X with BF16 weights, where 255 questions about a photo took 2.16 s, and is available via Transformers, the vLLM Semantic Router Engine, a free API and a Hugging Face Space.

Baidu Open-Sources Unlimited OCR 2 for Hundred-Page Document Parsing

Baidu released Unlimited OCR 2, a 3.34B-parameter OCR model under the MIT license that parses documents of hundreds of pages in a single request using SR-SWA, a sparse attention scheme that keeps per-step decoding cost nearly flat. Baidu reports first place on MPDocBench (84.6) and MarathonDocBench (77.1), with heading hierarchy its lowest sub-score at 48.2 on long documents. All benchmark figures are self-reported, and the model runs on one CUDA GPU in BF16 with code on GitHub.

OpenAI Launches GPT-6.1 Terra, Halving GPT-6.1 Sol’s API Price for Agent Work

OpenAI released GPT-6.1 Terra, a closed-weights model for visual reasoning, computer use and writing with a 1.05M-token context window and 128K max output, priced at $1 per million input tokens and $5 per million output tokens—half of GPT-6.1 Sol. OpenAI reports 85.5% on MMMU-Pro (Vision) at max effort, 60.0% on Agents' Last Exam v1 at $1.01 per task, and 50.3% on Terminal-Bench 4.0, trailing GPT-6.1 Sol (55.9%) and Claude Sonnet 5.5 (70.6%). It is available in the API as gpt-6.1-terra and in ChatGPT Work and Codex for Plus, Pro, Enterprise and Edu users.

Featured coverage of this story: 《Introducing GPT-6.1 Terra》

GPT-6.1 Terra

A link-blog post notes that OpenAI's GPT-6.1 Terra announcement emphasized visual reasoning and computer use, describing it as a fast, efficient model positioned between the cheaper Luna and the more expensive Sol, priced at $1/million input tokens and $5/million output. The post quotes OpenAI's Ted Sanders saying Terra is better than Astra and Sol at technical communication, similar on vision and computer use but faster, and a bit worse on most other things, and includes pelican test images across reasoning levels.

Featured coverage of this story: 《Introducing GPT-6.1 Terra》