# DeepSeek Ships V4-Flash, a 304B MoE Tuned for Agents

> The latest checkpoint in DeepSeek's V4 line leans into agentic workflows while keeping the permissive MIT license.

Published by The Open Weights on Jul 31, 2026. Canonical: https://theopenweights.com/news/deepseek-v4-flash-0731-7oaz

## Key facts

- Company: DeepSeek
- Model: DeepSeek-V4-Flash-0731
- Version: V4-Flash-0731
- Category: Text / LLM
- Modalities: Text / LLM, Reasoning
- License: MIT (Open weights, commercial use allowed)
- Parameters: 304B (mixture of experts)
- Context window: 1.0M tokens
- Architecture: DeepseekV4ForCausalLM
- Precision: INT8
- Weights size: 305.8 GB
- Significance: notable
- Published: 2026-07-31
- Last verified: 2026-09-06
- Hugging Face: https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731
- Canonical URL: https://theopenweights.com/news/deepseek-v4-flash-0731-7oaz

DeepSeek has released **DeepSeek-V4-Flash-0731**, a 304-billion-parameter mixture-of-experts (MoE) model that extends the company's V4 family. According to the [model card on Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731), this checkpoint focuses on substantially enhanced agentic capabilities — the ability to plan, call tools, and carry out multi-step tasks rather than simply answering single prompts.

The model is positioned as a text-and-reasoning system, and like DeepSeek's other releases it ships under the permissive **MIT license**, meaning developers can use, modify, and deploy it commercially with minimal restrictions. That licensing posture has been a consistent differentiator for DeepSeek against more guarded open-weight peers.

## Why it matters

The MoE design is central to the appeal here. By routing each token to a subset of specialized experts, a 304B-parameter model can deliver the capacity of a very large network while activating only a fraction of those weights per forward pass — a practical route to strong performance without proportional inference costs.

- **Agent-first tuning:** the update targets tool use and multi-step task execution
- **304B MoE:** large total capacity with sparse activation
- **MIT license:** unusually open terms for a model of this scale

DeepSeek has not published a context-length figure or detailed benchmark suite alongside this checkpoint, so teams evaluating it for production agent pipelines will want to run their own tests. Still, the "Flash" branding and agentic emphasis signal a model built for responsive, action-oriented deployments rather than pure chat.

## Get the model

- [Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731)

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Source: The Open Weights (https://theopenweights.com/). Aggregated and written by Claude, curated by humans. Cite as: "DeepSeek Ships V4-Flash, a 304B MoE Tuned for Agents", The Open Weights, Jul 31, 2026, https://theopenweights.com/news/deepseek-v4-flash-0731-7oaz