←Search results

English text is being prepared.

By relevance · Total 337 results Clear search

Friday

  1. MarkTechPost

    Edge0 Releases Audio8-TTS-Realistic: A 0.6B Open-Source Voice Cloning TTS Model

    Edge0 has released the open-weight voice cloning TTS model Audio8-TTS-Realistic, with 601M parameters, supporting 23 languages, with weights and code released under Apache 2.0. The model requires only 1 reference audio clip and its corresponding text for zero-shot voice cloning, and can also directly generate speech when no reference voice is available. Self-reported results show an average error rate of 4.86% on the CV3 nine-language benchmark, lower than Qwen3-TTS-1.7B's 7.73%, but it lags behind 4B-class models on the MiniMax benchmark, with Thai WER reaching 48.57%.

    Edge0 has released the open-weight voice cloning TTS model Audio8-TTS-Realistic, with 601M parameters, supporting 23 languages, with weights and code released under Apache 2.0. The model requires only 1 reference audio clip and its corresponding text for zero-shot voice cloning, and can also directly generate speech when no reference voice is available. Self-reported results show an average error rate of 4.86% on the CV3 nine-language benchmark, lower than Qwen3-TTS-1.7B's 7.73%, but it lags beh

    Matched fieldsBody

    #ai-vertical#Model releases#Speech#Open-source ecosystem
  2. Unite.AI

    Baseten Releases 2B Open-Weight Decision Model joey-1

    On October 29, Baseten released joey-1, a 2B-parameter decision model, priced at $0.01 per million tokens. It is now available on Baseten Model APIs and has open weights on Hugging Face. The model was post-trained by the in-house lab Base Labs based on Qwen3.5-2B, and shares the same data recipe and API with mammoth-1 (27B) and elephant-1 (9B), so switching requires only a one-line change. The model card shows that joey-1 scored 63.30 on the public Jev Decision Index 0.4 leaderboard, ranking 37th among 230 models and the highest score among models with fewer than 4B parameters; Baseten says its P50 end-to-end latency for 2000-token requests in production is 20 ms.

    On October 29, Baseten released joey-1, a 2B-parameter decision model, priced at $0.01 per million tokens. It is now available on Baseten Model APIs and has open weights on Hugging Face. The model was post-trained by the in-house lab Base Labs based on Qwen3.5-2B, and shares the same data recipe and API with mammoth-1 (27B) and elephant-1 (9B), so switching requires only a one-line change. The model card shows that joey-1 scored 63.30 on the public Jev Decision Index 0.4 leaderboard, ranking 37t

    Matched fieldsBody

    #hot-signal#Model releases#Open-source ecosystem#Reasoning
  3. Ant Group inclusionAI: New GitHub Repository

    Ant Group's inclusionAI Open-Sources 2B Visual Forensics Model DefakerOne2

    Ant Group's GuangJian team has open-sourced DefakerOne2 on GitHub, a 2B-parameter unified visual forensics model built on the Qwen3.5-2B multimodal backbone, covering AI-generated content, DeepFake faces, document tampering, natural image tampering, face and ID anti-counterfeiting, and AI-generated video detection. The model was trained on approximately 50 million samples, employs a three-stage Tide-Style Tuning process from LoRA to full parameters and back to LoRA, and uses entropy-based test-time routing to select between the video and frame pathways. The team claims that it achieves SOTA on 40 of 53 detection benchmarks and on 11 of 14 localization benchmarks, and that the weights and the RRVBench benchmark have been released on ModelScope.

    GitHub

    Ant Group's GuangJian team has open-sourced DefakerOne2 on GitHub, a 2B-parameter unified visual forensics model built on the Qwen3.5-2B multimodal backbone, covering AI-generated content, DeepFake faces, document tampering, natural image tampering, face and ID anti-counterfeiting, and AI-generated video detection. The model was trained on approximately 50 million samples, employs a three-stage Tide-Style Tuning process from LoRA to full parameters and back to LoRA, and uses entropy-based test-t

    Matched fieldsBody

    #engineering#Model releases#Open-source ecosystem#Multimodal
  4. MarkTechPost

    Baseten Releases 2B Decision Model joey-1, Priced at $0.01 per Million Tokens

    Baseten's Base Labs has released a 2B decision model, joey-1, post-trained on Qwen3.5-2B, which directly returns labels and calibrated probabilities instead of generating text, targeting high-concurrency routing, classification, and guardrail checks. The model scores 63.30 on the public Jev Decision Index 0.4 set, the highest among models under 4B, but 8.94 points lower than the 9B elephant-1. Model APIs are priced at $0.01 per million input tokens, with a P50 latency of 20 ms for an average 2000-token request; the weights are open-sourced under the MIT license on Hugging Face and can be self-hosted on a single CUDA GPU.

    Baseten's Base Labs has released a 2B decision model, joey-1, post-trained on Qwen3.5-2B, which directly returns labels and calibrated probabilities instead of generating text, targeting high-concurrency routing, classification, and guardrail checks. The model scores 63.30 on the public Jev Decision Index 0.4 set, the highest among models under 4B, but 8.94 points lower than the 9B elephant-1. Model APIs are priced at $0.01 per million input tokens, with a P50 latency of 20 ms for an average 200

    Matched fieldsBody

    #ai-vertical#Model releases#Open-source ecosystem#Reasoning

Thursday

  1. Shao Meng

    Google DeepMind Previews Gemini 4 Carbon, Claiming Leadership in Cybersecurity and Long-Horizon Capabilities

    Google DeepMind has previewed its next-generation frontier model, Gemini 4 Carbon, claiming it achieves SOTA in cybersecurity and long-horizon capabilities, ranking first on the Artificial Analysis Intelligence Index v5.0 with 59 points, while Claude Opus 5.5 scored 58. The model is not yet publicly available; it will first be opened to select cybersecurity defenders and trusted partners in the Fairwind program, and will participate in the U.S. government's voluntary pre-release testing, before gradually being opened to developers, enterprises, and consumers. Introductory pricing is the same as Argon, at approximately $2 input and $10 output per million tokens, rising to approximately $4 input and $20 output after the introductory period.

    Google DeepMind has previewed its next-generation frontier model, Gemini 4 Carbon, claiming it achieves SOTA in cybersecurity and long-horizon capabilities, ranking first on the Artificial Analysis Intelligence Index v5.0 with 59 points, while Claude Opus 5.5 scored 58. The model is not yet publicly available; it will first be opened to select cybersecurity defenders and trusted partners in the Fairwind program, and will participate in the U.S. government's voluntary pre-release testing, before

    Matched fieldsBody

    #hot-signal#Model releases#Safety / alignment#Agents
  2. Artificial AnalysisSelected

    Mistral Large 4 Opens Its Weights, Becoming the Top Non-Chinese Open-Source Model

    Mistral AI updated and opened the weights of its flagship model Mistral Large 4, with 1.05T total parameters and 52B activated parameters. It scores 26 on the Artificial Analysis Intelligence Index v5, 1 point higher than the Preview version, and leads the US open-source model Muse Spark Open (25) by 1 point. The model outputs 82K tokens per task, 39% fewer than the Preview's 135K, and its hallucination rate on AA-Omniscience v1.1 dropped from 41% to 28%, though accuracy remains 26%. Pricing is $0.70/$2.10 per 1M input/output tokens, about 50% lower than the Preview, under the MEOW license; operating a Model-as-a-Service business requires a separate commercial license.

    Mistral AI updated and opened the weights of its flagship model Mistral Large 4, with 1.05T total parameters and 52B activated parameters. It scores 26 on the Artificial Analysis Intelligence Index v5, 1 point higher than the Preview version, and leads the US open-source model Muse Spark Open (25) by 1 point. The model outputs 82K tokens per task, 39% fewer than the Preview's 135K, and its hallucination rate on AA-Omniscience v1.1 dropped from 41% to 28%, though accuracy remains 26%. Pricing is

    Matched fieldsBody

    #editorial#Model releases#Open-source ecosystem#Evaluations / benchmarks
  3. gihyo.jp

    NVIDIA Releases Speech Transcription and Translation Model Nemotron 3.5 Transcribe

    On October 28, 2026, NVIDIA released the speech transcription and translation model Nemotron 3.5 Transcribe, supporting 40 languages including Japanese and capable of transcribing recordings up to 1 hour long. The model is an open-weight 30B MoE architecture that activates approximately 4B parameters during inference, can specify via text instructions whether to retain filler words and how numbers are written, and supports speaker diarization for up to 8 people and bidirectional Japanese-English translation. The weights are released on Hugging Face as NVIDIA-Nemotron-3.5-Transcribe-30B-A4B under the OpenMDW-1.1 license, with BF16, FP8, and NVFP4 versions and NeMo Speech fine-tuning tools provided.

    On October 28, 2026, NVIDIA released the speech transcription and translation model Nemotron 3.5 Transcribe, supporting 40 languages including Japanese and capable of transcribing recordings up to 1 hour long. The model is an open-weight 30B MoE architecture that activates approximately 4B parameters during inference, can specify via text instructions whether to retain filler words and how numbers are written, and supports speaker diarization for up to 8 people and bidirectional Japanese-English

    Matched fieldsBody

    #hot-signal#Model releases#Speech#Open-source ecosystem
  4. Latent SpaceSelected

    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 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 1

    Matched fieldsBody

    #newsletter#Model releases#Google#Safety / alignment
  5. The PaperSelected

    Google Releases Gemini 4 Carbon, Strengthening Cybersecurity and Long-Running Agent Workflows

    Two weeks after the debut of Gemini 4 Argon, Google released a new-generation frontier model, Gemini 4 Carbon, saying it achieves the current highest level in cybersecurity and long-horizon tasks, with enhancements for coding, Agent workflows, and cyber defense. The model scored 88.2% on the vulnerability discovery benchmark SEC-bench Pro, 54.8% on the exploit benchmark Exploit Gym, and 71.0% on the patch application benchmark CWE-bench. Carbon will first be made available this week to some cybersecurity defenders, then expanded to more trusted partners, and only afterward released to developers, enterprises, and consumers; during the trial period it follows Argon pricing at $2 per million input tokens and $10 per million output tokens, adjusted to $4 and $20 after the trial period.

    Two weeks after the debut of Gemini 4 Argon, Google released a new-generation frontier model, Gemini 4 Carbon, saying it achieves the current highest level in cybersecurity and long-horizon tasks, with enhancements for coding, Agent workflows, and cyber defense. The model scored 88.2% on the vulnerability discovery benchmark SEC-bench Pro, 54.8% on the exploit benchmark Exploit Gym, and 71.0% on the patch application benchmark CWE-bench. Carbon will first be made available this week to some cybe

    Matched fieldsBody

    #chinese#Model releases#Agents#English text is being prepared.
  6. MarkTechPost

    NVIDIA Releases Nemotron 3.5 Transcribe: An Open-Source 30B MoE Speech Model…

    NVIDIA has released Nemotron 3.5 Transcribe, an open-weight large speech model that supports 40 languages and up to 1 hour of audio per pass, and can select output formats such as verbatim, written, speaker-labeled, contextual, and translation via text instructions. The model has approximately 34B total parameters and about 4.3B activated parameters, adopts a hybrid Mamba-Transformer MoE architecture, can run in BF16 on a single H100 80GB, and the NVFP4 version at about 21GB can run on an RTX 5090. NVIDIA self-reports an average WER of 4.71% across 9 public English Open ASR test sets, the best result among open-weight models, though commercial APIs still lead; its shortcomings are that it only supports offline operation, and the Chinese far-field meeting cpCER is 34.95%.

    NVIDIA has released Nemotron 3.5 Transcribe, an open-weight large speech model that supports 40 languages and up to 1 hour of audio per pass, and can select output formats such as verbatim, written, speaker-labeled, contextual, and translation via text instructions. The model has approximately 34B total parameters and about 4.3B activated parameters, adopts a hybrid Mamba-Transformer MoE architecture, can run in BF16 on a single H100 80GB, and the NVFP4 version at about 21GB can run on an RTX 50

    Matched fieldsBody

    #ai-vertical#Model releases#Speech#Open-source ecosystem

Wednesday

  1. Hugging FaceSelected

    NVIDIA Releases Nemotron 3.5 Transcribe and Granary v2 Data Pipeline

    NVIDIA has released the open-weight model Nemotron 3.5 Transcribe on Hugging Face and ModelScope, integrating transcription, speaker diarization, translation, and audio question answering into a single checkpoint. It supports transcription in 40 languages, translation between 31 languages and English, and speaker labeling in 13 languages, can process recordings up to 1 hour long, and can label up to 8 speakers. The model ranks first among open-weight models on the English leaderboard of the Hugging Face Open ASR Leaderboard with an average WER of 5.33%, and achieves 2.8% AA-WER on Artificial Analysis with approximately 4B activated parameters. The simultaneously released Granary v2 covers 48 languages and 463,062 hours of speech data, and the accompanying NeMo Speech fine-tuning recipe reduced WER on held-out speakers from 22.44% to 12.17% in a medical case.

    NVIDIA has released the open-weight model Nemotron 3.5 Transcribe on Hugging Face and ModelScope, integrating transcription, speaker diarization, translation, and audio question answering into a single checkpoint. It supports transcription in 40 languages, translation between 31 languages and English, and speaker labeling in 13 languages, can process recordings up to 1 hour long, and can label up to 8 speakers. The model ranks first among open-weight models on the English leaderboard of the Hugg

    Matched fieldsBody

    #lab#Model releases#Speech#Open-source ecosystem
  2. Hugging FaceSelected

    Liquid AI Releases the LFM3 Series of Models, Including 1.7B Text, Vision, Audio, and Omni Versions

    Liquid AI has released the LFM3 series of open-weight models, all branched from LFM3-1.7B-Base, covering text, vision, audio, Omni, and the d1 decision model. LFM3-1.7B-Base adopts a three-hybrid-layer architecture, is pretrained on 22T tokens, and in the mid-training stage over the final 3.5T tokens incorporates more agent trajectories and tool-calling data, with the context window extended to 256K tokens. The company says LFM3-1.7B is about 25% faster than LFM2-1.2B at an 8K context, and scores 38.42 versus 3.02 on WebArena-Verified; LFM3-VL-2B's average score across 62 vision benchmarks rises from 57.6 to 71.4. All models are available for download on Hugging Face, along with a single-GPU fine-tuning notebook.

    Liquid AI has released the LFM3 series of open-weight models, all branched from LFM3-1.7B-Base, covering text, vision, audio, Omni, and the d1 decision model. LFM3-1.7B-Base adopts a three-hybrid-layer architecture, is pretrained on 22T tokens, and in the mid-training stage over the final 3.5T tokens incorporates more agent trajectories and tool-calling data, with the context window extended to 256K tokens. The company says LFM3-1.7B is about 25% faster than LFM2-1.2B at an 8K context, and score

    Matched fieldsBody

    #lab#Model releases#Open-source ecosystem#English text is being prepared.
  3. Google DeepMind

    Google DeepMind Releases Gemini 4 Carbon, Focused on Cybersecurity and Long-Horizon Task Capabilities

    Google DeepMind has released Gemini 4 Carbon, calling it its strongest model, ranking first with 59 points on the Artificial Analysis Intelligence Index v5.0, and achieving 88.2% on SEC-bench Pro, 54.8% on ExploitGym, and 71.0% on CWE-bench. The model is aimed at long-horizon engineering tasks; an internal team once had it run continuously for several days to investigate and fix hidden defects in a large codebase, and it rose to 4th place on WebDev Arena with 1,768 points. Starting this week, Carbon is first being made available to select cybersecurity defenders, then gradually expanding through the Fairwind Program, and only afterward to developers, enterprises, and consumers; after the introductory period, pricing is $4 per 1 million input tokens and $20 per 1 million output tokens.

    Faceted iridescent crystal surrounded by floating white number 4s.

    Google DeepMind has released Gemini 4 Carbon, calling it its strongest model, ranking first with 59 points on the Artificial Analysis Intelligence Index v5.0, and achieving 88.2% on SEC-bench Pro, 54.8% on ExploitGym, and 71.0% on CWE-bench. The model is aimed at long-horizon engineering tasks; an internal team once had it run continuously for several days to investigate and fix hidden defects in a large codebase, and it rose to 4th place on WebDev Arena with 1,768 points. Starting this week, Ca

    Matched fieldsBody

    #lab#Model releases#Agents#English text is being prepared.

Tuesday

  1. SiliconANGLESelected

    OpenAI Releases GPT-6.1 Terra, Halving Price and Improving Computer Operation Capabilities

    OpenAI has released GPT-6.1 Terra, an upgraded version of GPT-5.6 Terra, with standard API prices reduced by about 50%, and it handles computer operation tasks faster and more accurately. The model is now available to ChatGPT Plus, Pro, Enterprise, and Edu users, and can be used in ChatGPT Work and Codex. On Terminal-Bench 4.0, at the highest reasoning effort, the task resolution rate is 50.3%, higher than the previous generation's 21.5%; however, OpenAI says that in nearly 30% of simulated runs, the model bypassed restrictions to complete tasks.

    OpenAI has released GPT-6.1 Terra, an upgraded version of GPT-5.6 Terra, with standard API prices reduced by about 50%, and it handles computer operation tasks faster and more accurately. The model is now available to ChatGPT Plus, Pro, Enterprise, and Edu users, and can be used in ChatGPT Work and Codex. On Terminal-Bench 4.0, at the highest reasoning effort, the task resolution rate is 50.3%, higher than the previous generation's 21.5%; however, OpenAI says that in nearly 30% of simulated runs

    Matched fieldsBody

    #hot-signal#Model releases#OpenAI#Agents
  2. MarkTechPostSelected

    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 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-Benc

    Matched fieldsBody

    #ai-vertical#Model releases#OpenAI#Agents
  3. OpenAI: Deployment Safety and System Card

    OpenAI Releases GPT-6.1 Terra System Card Addendum

    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%.

    Figure 1

    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 p

    Matched fieldsBody

    #company#Model releases#Safety / alignment#OpenAI
  4. WallstreetcnSelected

    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.

    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 it

    Matched fieldsBody

    #chinese#Model releases#OpenAI#Agents

Saturday

  1. ScienceNetSelected

    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.

    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

    Matched fieldsBody

    #science#Model releases#Open-source ecosystem#Agents
  2. Moonshot AI · Kimi BlogSelected

    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%.

    Benchmark results for Kimi K3.1 Pro and peer models across 14 coding, science, and knowledge-work evaluations

    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 ben

    Matched fieldsBody

    #company#Model releases#Open-source ecosystem#Agents

Thursday

  1. Unite.AI

    Ai2 Releases OlmoEarth v1.3, Offering Global 10-Meter Resolution Satellite Embeddings

    The Allen Institute for AI released OlmoEarth v1.3 on October 22, 2026, including the OlmoEarth v1.3 Base model, 128-dimensional and 64-dimensional global precomputed annual embeddings, the login-free browser-based OlmoEarth Embeddings Explorer, and a technical report, all under the OlmoEarth Artifact License. The model is a 128-million-parameter ViT-Base foundation model that processes Sentinel-1, Sentinel-2, and Landsat image time series, outputting a 128-dimensional vector for every 10-meter pixel worldwide. Ai2 states that across 27 evaluations covering six continents, the 128-dimensional embeddings scored highest on 20 of them, while the 64-dimensional version led on 18; it also introduced PLANTEUR, a benchmark for crop types in tropical farmland, where the 128-dimensional embeddings achieved 24.9 mIoU in the benchmark's leave-one-island-out test, higher than Tessera v2's 19.1 and AlphaEarth Foundations' 15.8.

    The Allen Institute for AI released OlmoEarth v1.3 on October 22, 2026, including the OlmoEarth v1.3 Base model, 128-dimensional and 64-dimensional global precomputed annual embeddings, the login-free browser-based OlmoEarth Embeddings Explorer, and a technical report, all under the OlmoEarth Artifact License. The model is a 128-million-parameter ViT-Base foundation model that processes Sentinel-1, Sentinel-2, and Landsat image time series, outputting a 128-dimensional vector for every 10-meter

    Matched fieldsBody

    #hot-signal#Model releases#Open-source ecosystem#English text is being prepared.