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Qwen-Image-2.1 supports ComfyUI
On September 20, 2026, the Qwen team open-sourced the Qwen-Image-2.1 image model, unifying text-to-image and image editing into a single model that supports a variety of image editing tasks. According to the official introduction, the model's visual generation component is a 7B-parameter, 32-layer Single-Stream DiT that natively supports transparent image generation and editing, supports up to 10 reference images as well as local editing methods such as circling and masking, and reduces inference overhead and VRAM usage during multi-image editing through mixed-granularity attention and KV cache reuse; the model weights have been released on GitHub, Hugging Face, and ModelScope. On the same day, Qwen announced that Qwen-Image-2.1 is natively supported in ComfyUI; users can update ComfyUI or use Comfy Cloud, download the weights from Hugging Face, and load the template to use it.
up to 10 reference images as well as local editing methods such as circling and masking, and reduces inference overhead and VRAM usage during multi-image editing through mixed-granularity attention and KV cache reuse; the model weights have been released on GitHub, Hugging Face, and ModelScope. On the same day, Qwen announced that Qwen-Image-2.1 is natively supported in ComfyUI; users can update ComfyUI or use Comfy Cloud, download the weights from Hugging Face, and load the template to use it.
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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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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
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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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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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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, topping 15 of 17 benchmarks
Google has released Gemini 4 Carbon, a frontier model aimed at cybersecurity and long-horizon coding tasks, topping 15 of Google's own 17 benchmarks, but it has not yet been made public and is available only to some cybersecurity defenders. It scores 59 on Artificial Analysis' Intelligence Index, surpassing Claude Opus 5.5 and up 9 points from Argon four weeks ago; on long-input reasoning it leads OpenAI Astra and Opus by 24 points. The introductory API price is $2 per million input tokens and $10 per million output tokens, half that of Opus 5.5, doubling after the discount period to Opus's rates. Google has not announced when it will broaden access.
Google has released Gemini 4 Carbon, a frontier model aimed at cybersecurity and long-horizon coding tasks, topping 15 of Google's own 17 benchmarks, but it has not yet been made public and is available only to some cybersecurity defenders. It scores 59 on Artificial Analysis' Intelligence Index, surpassing Claude Opus 5.5 and up 9 points from Argon four weeks ago; on long-input reasoning it leads OpenAI Astra and Opus by 24 points. The introductory API price is $2 per million input tokens and $
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Mistral Releases Open Weights for Mistral Large 4 (Le Chonk)
Mistral has released the open weights for Mistral Large 4 (Le Chonk), a 1-trillion-parameter natively multimodal model that continues reinforcement learning on top of the preview version, with improved coding and defensive cybersecurity capabilities. It scores 19.7% on FrontierCode 2.0 and 48% on the Artificial Analysis Coding Agent Index, roughly on par with GLM-5.3, but uses an average of 47% fewer tokens per task; on Terminal-Bench 4.0 it improves by 13 percentage points to 41%. The model supports three reasoning modes, uses a modified MIT-based Mistral Ecosystem Open-Weight license, and is priced via API at $0.70 per million input tokens, $0.07 for cached input, and $2.10 for output.
Mistral has released the open weights for Mistral Large 4 (Le Chonk), a 1-trillion-parameter natively multimodal model that continues reinforcement learning on top of the preview version, with improved coding and defensive cybersecurity capabilities. It scores 19.7% on FrontierCode 2.0 and 48% on the Artificial Analysis Coding Agent Index, roughly on par with GLM-5.3, but uses an average of 47% fewer tokens per task; on Terminal-Bench 4.0 it improves by 13 percentage points to 41%. The model sup
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