DeepSeek-V4-Flash-0731

Frontier agent intelligence at Flash prices

API

Open Source

Artificial Intelligence

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I think the biggest shift isn't that intelligence is getting cheaper—it's that experimentation is becoming cheaper. When the cost of trying hundreds of ideas approaches zero, the competitive advantage shifts from access to AI to identifying the right problems worth solving.

Is this becoming a price match, Oai also reduced a lot on their frontier models..

This is likely the most cost efficient model right now, works better than kimi 2.7code, and def better option than gemini flash. 5.6 Luna and Grok build 0.1 are both very good as well

61.8 to 82.7 on Terminal-Bench and 7.3 to 54.4 on DeepSWE for a model that's "the same architecture and size" as the preview is the kind of jump that makes me want to know who ran the eval, not just what it scored. are these numbers reproduced by anyone outside DeepSeek yet, or is it still first-party only? weights being MIT and downloadable makes independent verification actually possible here, unlike a closed API release, so I'd rather wait for someone else's harness to confirm it than take the release table at face value.

Where does it land on long-horizon agent tasks vs. raw benchmarks? That gap is usually where the cheaper tiers fall short.

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.