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Tencent Hunyuan Releases Hy4 Official Version, Introducing Vision and Computer Operation Capabilities for the First Time
Tencent Hunyuan Releases Hy4 Official Version, Significantly Expanding Pre-training and Post-training Scale Compared with Hy4 preview, Introducing Vision Capabilities and Supporting Computer Operation for the First Time. The company says Hy4 has for the first time ranked among the strongest open-source models, improving by 19.1% and 9.8% on the GameDev and GameLogic benchmarks respectively, while the hallucination rate dropped from 15.76% to 5.45%. Hy4 is now available on Tencent Cloud and OpenRouter and can be experienced in products such as WorkBuddy/CodeBuddy, Yuanbao, and ima. API pricing remains unchanged at 6 yuan for input and 18 yuan for output per million Tokens, and the model weights are planned to be open-sourced.
Tencent Hunyuan Releases Hy4 Official Version, Significantly Expanding Pre-training and Post-training Scale Compared with Hy4 preview, Introducing Vision Capabilities and Supporting Computer Operation for the First Time. The company says Hy4 has for the first time ranked among the strongest open-source models, improving by 19.1% and 9.8% on the GameDev and GameLogic benchmarks respectively, while the hallucination rate dropped from 15.76% to 5.45%. Hy4 is now available on Tencent Cloud and OpenR
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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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Mistral Releases Large 4 Open-Weight Model, Focused on Coding and Cyber Defense
French company Mistral AI has released the Large 4 model (ML4), featuring advanced coding and cyber defense capabilities; enterprises can download the open-weight version and deploy it to run on their own infrastructure. The model has a total of 1.05 trillion parameters, with approximately 52 billion parameters actually activated per task execution, and was trained in a European data center using 3,800 NVIDIA Grace Blackwell GPUs. Mistral claims it is comparable to other leading open models on coding tasks and emphasizes that open source helps prevent frontier AI technology from concentrating in the hands of a few players.
French company Mistral AI has released the Large 4 model (ML4), featuring advanced coding and cyber defense capabilities; enterprises can download the open-weight version and deploy it to run on their own infrastructure. The model has a total of 1.05 trillion parameters, with approximately 52 billion parameters actually activated per task execution, and was trained in a European data center using 3,800 NVIDIA Grace Blackwell GPUs. Mistral claims it is comparable to other leading open models on c
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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
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OpenBMB Releases MiniCPM-OCR 1.0 and Verification Model Jev
OpenBMB releases MiniCPM-OCR 1.0, a 1B-scale OCR model that achieves 93.68 on Real5-OmniDocBench and 54.7 on OCRBench v2. It also releases MiniCPM-OCR Jev, a 2B decision model used to compare parsing results with the original image and give a 0–1 correctness score, approaching Qwen3.5-397B-A17B on JevOCRBench. Both models have open weights, support deployment via llama.cpp, vLLM, SGLang, and Ollama, and come with open-source fine-tuning code.
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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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