20250801-More_model_releases_on_31st_July

原文摘要

Here are a few more model releases from today, to round out a very busy July:

  • Cohere released Command A Vision, their first multi-modal (image input) LLM. Like their others it's open weights under Creative Commons Attribution Non-Commercial, so you need to license it (or use their paid API) if you want to use it commercially.
  • San Francisco AI startup Deep Cogito released four open weights hybrid reasoning models, cogito-v2-preview-deepseek-671B-MoE, cogito-v2-preview-llama-405B, cogito-v2-preview-llama-109B-MoE and cogito-v2-preview-llama-70B. These follow their v1 preview models in April at smaller 3B, 8B, 14B, 32B and 70B sizes. It looks like their unique contribution here is "distilling inference-time reasoning back into the model’s parameters" - demonstrating a form of self-improvement. I haven't tried any of their models myself yet.
  • Mistral released Codestral 25.08, an update to their Codestral model which is specialized for fill-in‑the‑middle autocomplete as seen in text editors like VS Code, Zed and Cursor.
  • And an anonymous stealth preview model called Horizon Alpha running on OpenRouter was released yesterday and is attracting a lot of attention.
<p>Tags: <a href="https://simonwillison.net/tags/llm-release">llm-release</a>, <a href="https://simonwillison.net/tags/openrouter">openrouter</a>, <a href="https://simonwillison.net/tags/mistral">mistral</a>, <a href="https://simonwillison.net/tags/generative-ai">generative-ai</a>, <a href="https://simonwillison.net/tags/cohere">cohere</a>, <a href="https://simonwillison.net/tags/ai">ai</a>, <a href="https://simonwillison.net/tags/llms">llms</a></p>

原文链接

进一步信息揣测

  • Cohere的商用限制：Command A Vision虽然是开源权重，但采用CC-NC许可证，商业用途需额外授权或使用付费API，这可能导致企业面临隐性成本或法律风险。
  • Deep Cogito的“推理蒸馏”技术：其模型通过“将推理时逻辑蒸馏回模型参数”实现自我改进，这种技术细节未公开具体实现方式，可能是其核心竞争力，但缺乏第三方验证效果。
  • 匿名模型Horizon Alpha的炒作策略：通过OpenRouter匿名发布并迅速吸引关注，可能是刻意制造的“神秘营销”手段，利用社区好奇心推动早期测试，但实际性能可能未经验证。
  • Mistral Codestral的垂直领域适配：专注于代码补全（如VS Code、Zed等编辑器），暗示其可能针对开发者工具链优化，但未提及是否与这些编辑器存在私下合作或定制协议。
  • Deep Cogito的模型规模跳跃：从v1的3B-70B直接跃升至v2的109B-671B，可能依赖未公开的算力资源或训练技巧，但快速迭代背后可能存在技术债务风险。
  • 开源模型的商业化路径：Cohere和Deep Cogito均采用“开源权重+商业许可”模式，反映行业趋势——通过开源吸引开发者，再通过企业级服务盈利，但实际商用门槛可能高于预期。