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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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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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On October 28, Google released Gemini 4 Carbon, which topped the Artificial Analysis Intelligence Index v5.0 with a score of 59, surpassing Claude Opus 5.5's 58 and GPT-6 Astra's 57 to reclaim first place. The model is currently available only to a small number of cybersecurity defense personnel; when asked when ordinary developers and enterprises will be able to use it, officials responded only with "as soon as possible." On the GraphWalks ultra-long-context test, Carbon achieved 96.2% F1, far

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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

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Mistral AI has released its new flagship frontier model, Mistral Large 4, codenamed Le Chonk, making it available for download after ending its public preview. The model supports text and image processing, with a focus on strengthening defensive cybersecurity, agentic capabilities, and geospatial and grounding capabilities, and is said to achieve state-of-the-art results on agentic benchmarks such as SWE-bench and Terminal Bench. The model was trained on more than 160 languages and is available

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Google has released its flagship model Gemini 4 Carbon, built specifically for continuously running agentic workflows, with a focus on improving coding, agentic, scientific task, and full-stack web development capabilities. The model ranks first on the Artificial Analysis Intelligence Index v5.0 with a score of 59, surpassing Claude Opus 5.5, GPT-6 Astra, and GPT-6.1 Sol; the launch promotional price is $2 per million input tokens and $10 per million output tokens, with a 95% discount available

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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 AI released the open weights of Mistral Large 4 (codename Le Chonk) on Hugging Face on October 29. The model has 100 billion parameters out of a total parameter scale of the 100-billion class, with about 4.9 billion parameters actually invoked per token, using a MoE architecture with 128 experts, of which 4 are activated each time plus 1 shared expert. Compared with the October 6 preview version, Mistral says it continued reinforcement learning over three weeks, raising the Terminal-Benc

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Mistral has released the official version of Le Chonk, a trillion-parameter open-weight model, calling it the world's best open-weight model in automated coding and cybersecurity, and making it freely available. Pierre Stock, Mistral's VP of Science, said the model was trained on increasingly difficult cybersecurity and coding problems, enabling it to match or surpass models from Chinese labs such as DeepSeek and Z.ai. Stock also stressed that the West needs its own advanced open-weight models,

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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

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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

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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

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Hugging Face on Thursday released the open-source DNA understanding model Carbon-X, which can run locally on a laptop, letting users ask in natural language how DNA changes affect gene activity and health. The model was developed in collaboration with researchers at Zhongguancun Academy and the University of Naples Federico II; it extends the vocabulary of an existing language model to read DNA in six-letter units and was further trained on 300 billion tokens of DNA sequences, English text, and

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