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Ant Ling officially open-sources Ling-3.0-tiny
On August 11, 2026, Ant Ling officially open-sourced Ling-3.0-tiny. According to the official introduction, this is a natively hybrid reasoning model with 7.9B total parameters, activating only 1.3B parameters during inference, and it is simultaneously offered in three versions: BF16, FP8, and INT4. The company states that it scores 25 on the Artificial Analysis Intelligence Index and 16 on the Agentic Index, with an output speed exceeding 160 tokens/s. In terms of deployment, the model can perform FP8 inference on a single NVIDIA DGX Spark, with weights of approximately 7.85GB, and has also been adapted for local operation on Apple Silicon MacBook and Mac mini. The model is currently in an open-source release state, with no further developments at this time.
nference, and it is simultaneously offered in three versions: BF16, FP8, and INT4. The company states that it scores 25 on the Artificial Analysis Intelligence Index and 16 on the Agentic Index, with an output speed exceeding 160 tokens/s. In terms of deployment, the model can perform FP8 inference on a single NVIDIA DGX Spark, with weights of approximately 7.85GB, and has also been adapted for local operation on Apple Silicon MacBook and Mac mini. The model is currently in an open-source releas
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Sarvam AI Releases Sarvam Prism, a Decision Model Based on Sarvam 105B
Sarvam AI has released Sarvam Prism, a System One decision model based on Sarvam 105B, which achieves an average score of 71.8 with a median response time of 44 ms on B200 GPUs, the highest score within 100 ms among the 25 decision models it tested. At confidence levels above 90%, the model's actual error rate is lower than expected: 2.2% versus 3.3% for English and 4.4% versus 5.4% for Indic languages, and it keeps its answers unchanged in a 2,000-question option-shuffling test. The weights are open-sourced under the Apache 2.0 license on Hugging Face, requiring at least 120 GB of VRAM on a single GPU to run in FP8, and it is also available via the Sarvam API.
Sarvam AI has released Sarvam Prism, a System One decision model based on Sarvam 105B, which achieves an average score of 71.8 with a median response time of 44 ms on B200 GPUs, the highest score within 100 ms among the 25 decision models it tested. At confidence levels above 90%, the model's actual error rate is lower than expected: 2.2% versus 3.3% for English and 4.4% versus 5.4% for Indic languages, and it keeps its answers unchanged in a 2,000-question option-shuffling test. The weights are
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OpenAI releases the GPT-Rosalind-6 system card addendum, offering restricted access for biological research
OpenAI releases the GPT-Rosalind-6 system card addendum. This model uses the same set of weights as GPT-6 Astra, with no new training data or post-training added, and is provided only through trusted access to vetted research institutions, enterprises, and government partners. At the highest reasoning budget, MedChemBench v7 averages 49.6% (GPT-Rosalind-5.5: 28.9%), GeneBench Pro v13 single-attempt success rate is 37.7% (previous generation: 16.7%), and LifeSciBench Gold average score is 61.3% (previous generation: 44.0%). Under the Preparedness Framework, this release is rated High capability in the biological and chemical domains, below the Critical threshold; rated Critical in the cybersecurity domain; and below High in AI self-improvement.
OpenAI releases the GPT-Rosalind-6 system card addendum. This model uses the same set of weights as GPT-6 Astra, with no new training data or post-training added, and is provided only through trusted access to vetted research institutions, enterprises, and government partners. At the highest reasoning budget, MedChemBench v7 averages 49.6% (GPT-Rosalind-5.5: 28.9%), GeneBench Pro v13 single-attempt success rate is 37.7% (previous generation: 16.7%), and LifeSciBench Gold average score is 61.3% (
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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
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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
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Chaoyan Intelligence Open-Sources 35B Model Apex-35B-V1.0, with AI Participating in Its Own R&D
Beijing-based large model startup Chaoyan Intelligence has released and open-sourced its first large model, Apex-35B-V1.0, with 35 billion total parameters, approximately 3 billion parameters activated per token, and support for a 262,000-token context window, optimized through mid-training and post-training based on Qwen3.6-35B-A3B. The model scored 60.9 and 40.0 on the paper reproduction test PaperBench and the scientific research challenge test FrontierScience, respectively, higher than GLM-5.3's 42.5 and 30.7; it scored 84.0 on the tool-calling test BFCL V4, higher than DeepSeek-V4.1-Flash-1017's 75.0. The model has been released with open weights under the Apache 2.0 license, and the official release provides approximately 72GB of BF16 weights and GGUF quantized versions at various precisions.
Beijing-based large model startup Chaoyan Intelligence has released and open-sourced its first large model, Apex-35B-V1.0, with 35 billion total parameters, approximately 3 billion parameters activated per token, and support for a 262,000-token context window, optimized through mid-training and post-training based on Qwen3.6-35B-A3B. The model scored 60.9 and 40.0 on the paper reproduction test PaperBench and the scientific research challenge test FrontierScience, respectively, higher than GLM-5
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Mistral releases Large 4 and opens the model weights, with improved coding capabilities
Mistral has released the model weights of its flagship model Large 4 (ML4), which was previously available only as an API preview. After three weeks of testing by customers and cybersecurity partners and further training with reinforcement learning, ML4's success rate on Terminal-Bench 4.0 rose from 28% to 41%, and two new reasoning effort levels, Low and Medium, were added. Mistral research lead Pierre Stock said that at equivalent capability the new version uses about 50% fewer tokens than the preview; the weights became available for download on Hugging Face at 19:00 and are also offered through the Mistral API and the coding assistant Vibe.
Mistral has released the model weights of its flagship model Large 4 (ML4), which was previously available only as an API preview. After three weeks of testing by customers and cybersecurity partners and further training with reinforcement learning, ML4's success rate on Terminal-Bench 4.0 rose from 28% to 41%, and two new reasoning effort levels, Low and Medium, were added. Mistral research lead Pierre Stock said that at equivalent capability the new version uses about 50% fewer tokens than the
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Mistral Opens the Weights of Mistral Large 4, a 1-Trillion-Parameter Natively Multimodal Model
Mistral AI announced that it is opening the weights of its frontier model Mistral Large 4 (codename Le Chonk), which has 1 trillion parameters with 52 billion active parameters and native multimodal support. ML4 was trained in a European data center on 3,800 Nvidia Grace Blackwell GPUs, improving from 28% to 41% on Terminal-Bench 4.0, tying GLM-5.3 for the best coding-agent score while using an average of 47% fewer tokens per task. Open weights mean enterprises can deploy it on their own hardware and behind their firewalls, and the model offers three reasoning modes: low, medium, and high.
ame Le Chonk), which has 1 trillion parameters with 52 billion active parameters and native multimodal support. ML4 was trained in a European data center on 3,800 Nvidia Grace Blackwell GPUs, improving from 28% to 41% on Terminal-Bench 4.0, tying GLM-5.3 for the best coding-agent score while using an average of 47% fewer tokens per task. Open weights mean enterprises can deploy it on their own hardware and behind their firewalls, and the model offers three reasoning modes: low, medium, and high.
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Mistral Releases Mistral Large 4 Weights: Coding Ability Leads Open-Source Models but Overall Trails Chinese Models
Mistral has released the weights of Mistral Large 4 on Hugging Face. The model, nicknamed Le Chonk, has approximately 1 trillion parameters with 52 billion activated per step, and supports text and image inputs. On Terminal-Bench 4.0, its score rose from 28% in the preview version to 41%, slightly above GLM-5.3's 40%, but on the new version of the Artificial Analysis Intelligence Index it scored only 26 points, below the 32 points of DeepSeek V4.1 Flash and GLM-5.3. API pricing is $0.70 per million input tokens and $2.10 per million output tokens, under a modified MIT license; using it as a commercial cloud service requires separate authorization.
Mistral has released the weights of Mistral Large 4 on Hugging Face. The model, nicknamed Le Chonk, has approximately 1 trillion parameters with 52 billion activated per step, and supports text and image inputs. On Terminal-Bench 4.0, its score rose from 28% in the preview version to 41%, slightly above GLM-5.3's 40%, but on the new version of the Artificial Analysis Intelligence Index it scored only 26 points, below the 32 points of DeepSeek V4.1 Flash and GLM-5.3. API pricing is $0.70 per mill
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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
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Google Releases Gemini 4 Carbon, Surpassing Claude Opus 5.5 on Multiple Benchmarks
Google has released its new flagship model Gemini 4 Carbon, which tops the Artificial Analysis Intelligence Index 5.0 with a score of 59, surpassing Claude Opus 5.5 and GPT-6 Astra. It scored 65.2% on Terminal-Bench 4.0, 96.2% on GraphWalks for 256k to 1M context, and 88.2% on SEC-bench Pro vulnerability discovery. Initial pricing is $2 per million tokens for input and $10 per million tokens for output, with cached input as low as $0.2; because of its vulnerability discovery and exploitation capabilities, it will first be made available to select cybersecurity defenders.
Google has released its new flagship model Gemini 4 Carbon, which tops the Artificial Analysis Intelligence Index 5.0 with a score of 59, surpassing Claude Opus 5.5 and GPT-6 Astra. It scored 65.2% on Terminal-Bench 4.0, 96.2% on GraphWalks for 256k to 1M context, and 88.2% on SEC-bench Pro vulnerability discovery. Initial pricing is $2 per million tokens for input and $10 per million tokens for output, with cached input as low as $0.2; because of its vulnerability discovery and exploitation cap
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Google releases Gemini 4 Carbon, topping the Artificial Analysis Intelligence Index with 59 points
Google has released its new-generation flagship model Gemini 4 Carbon, which tops the Artificial Analysis Intelligence Index v5.0 with 59 points, surpassing Claude Opus 5.5 and GPT-6 Astra. On Terminal-Bench 4.0, the model improved from Argon's 57.1% to 65.2%, and on the GraphWalks long-context test it rose from 84.2% to 96.2%, but developers report that its generation speed is about 2 to 4 times slower than Argon. This week Google first opened it to a small number of cybersecurity defense organizations, then expanded it to trusted partners through the Fairwind program; the opening time for general users has not yet been announced.
Google has released its new-generation flagship model Gemini 4 Carbon, which tops the Artificial Analysis Intelligence Index v5.0 with 59 points, surpassing Claude Opus 5.5 and GPT-6 Astra. On Terminal-Bench 4.0, the model improved from Argon's 57.1% to 65.2%, and on the GraphWalks long-context test it rose from 84.2% to 96.2%, but developers report that its generation speed is about 2 to 4 times slower than Argon. This week Google first opened it to a small number of cybersecurity defense organ
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Google releases Gemini 4 Carbon, tops the Artificial Analysis leaderboard with 59 points
Google released Gemini 4 Carbon, which scored 59 points on the Artificial Analysis Intelligence Index v5.0 leaderboard, surpassing Claude Opus 5.5's 58 points and GPT-6 Astra's 57 points. The model achieved 88.2%, 54.8%, and 71.0% on SEC-bench Pro, ExploitGym, and CWE-bench, respectively, and Google says it can handle engineering problems in large codebases continuously for several days. The model this week is first being made available to some cybersecurity defenders, then expanded to trusted partners through the Fairwind Program, with no timeline yet for general users; initial pricing is $2 per million input tokens and $10 per million output tokens.
Google released Gemini 4 Carbon, which scored 59 points on the Artificial Analysis Intelligence Index v5.0 leaderboard, surpassing Claude Opus 5.5's 58 points and GPT-6 Astra's 57 points. The model achieved 88.2%, 54.8%, and 71.0% on SEC-bench Pro, ExploitGym, and CWE-bench, respectively, and Google says it can handle engineering problems in large codebases continuously for several days. The model this week is first being made available to some cybersecurity defenders, then expanded to trusted p
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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
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