DeepSeek
Open-source LLM optimized for advanced reasoning and code
4.9•49 reviews•3K followers
Open-source LLM optimized for advanced reasoning and code
4.9•49 reviews•3K followers
Intelligent assistant for coding, content creation, file reading, and more. Upload documents, engage in extended conversations, and receive expert assistance in AI, natural language processing, and beyond.
This is the 14th launch from DeepSeek. View more
DeepSeek-V4-Flash-0731
Launching today
DeepSeek-V4-Flash-0731 is the official release of V4-Flash, featuring a massive leap in agentic capabilities. It outperforms V4-Pro (Preview) on key benchmarks, natively supports the Responses API, and is fully adapted for Codex CLI.
Free
Launch Team
ChatGPT by OpenAI
Flowtica Scribe
Hi everyone!
This is another @DeepSeek moment.
DeepSeek-V4-Flash-0731 keeps the same architecture and size as the preview model, but the jump in agentic performance is hard to treat as a normal update.
Terminal-Bench 2.1 moved from 61.8 to 82.7. DeepSWE went from 7.3 to 54.4 (simply CRAZY). Flash now beats V4-Pro Preview on every benchmark shown in DeepSeek’s release table, while activating far fewer parameters.
The weights and inference code are already available under MIT too, so this is not just an API release.
Then there is the price. V4 Flash currently costs $0.14 per million uncached input tokens and $0.28 per million output tokens. GPT-5.6 Terra and Luna also just got much cheaper.
Top-tier intelligence is becoming extremely cheap. I mean extremely cheap.
I think we are entering a different phase of AI. When intelligence at this level is almost free and available through Codex, what will you build? How far can your imagination go when the cost of trying is no longer the main constraint??
P.S. one slightly crazy hint👀👀:
The number I care about isn't $0.14 per million, it's cost per completed task. A cheaper model that needs two retries on an agent run costs more than a pricier one that lands it first, which is why 61.8 to 82.7 on Terminal-Bench is the line that actually moves my bill. Where it gets interesting is cached input pricing, since on long agent loops most of my spend is context I'm re-sending, not new tokens.