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The release delivers an openly licensed MIT model geared toward coding and autonomous agent tasks. Combining native speculative decoding with low activated parameter counts targets high-throughput inference while challenging larger proprietary models on agent benchmarks.
DeepSeek's decision to bypass Jinja formatting in favour of an internal Python encoding helper introduces a specific implementation requirement for custom inference pipelines. Furthermore, benchmark results heavily rely on evaluation with the minimal mode of the upcoming DeepSeek Harness and maximum reasoning effort, highlighting DeepSeek's emphasis on multi-step reasoning capabilities.
Mia
@MiaAI_lab · 31 July 2026
Official DeepSeek v4 Flash weights are out in @huggingface 🔥🔥🔥 t.co/PLzrY356rs
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View on XSummary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.
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Link:deepseek-ai
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Link:deepseek-ai
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DeepSeek has open-sourced the preview release of DeepSeek-V4. The line-up features DeepSeek-V4-Pro and DeepSeek-V4-Flash, standardising a 1M context window and introducing architectural innovations to cut compute and memory overhead.
Link:deepseek-ai
DeepSeek-V4-Flash-0731 is a sparse Mixture-of-Experts language model featuring 13B active parameters and a 1-million-token context window. The NVIDIA Build model card outlines technical specifications, architecture details, and coding benchmark performances.
Article:Simon Willison
DeepSeek has introduced DeepSeek-V4-Pro and DeepSeek-V4-Flash under an MIT licence. Simon Willison reviews their Mixture of Experts architecture, KV cache efficiency gains, and pricing compared to OpenAI, Google, and Anthropic.
Article:DeepSeek
DeepSeek has launched its multimodal DeepSeek-V4-Flash-Vision-Exp model on the DeepSeek API platform. It matches DeepSeek-V4-Flash in text and reasoning benchmarks while adding visual understanding. A free Files API was introduced simultaneously for reusable asset uploads.
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