DevOps / SRE / Platform · 03.08.2026, 13:48 UTC
DeepSeek’s smaller model just outperformed its own flagship
| Schweregrad | info |
|---|---|
| Kategorie | DevOps / SRE / Platform |
| Quelle | The New Stack ↗ |
| Veröffentlicht | 03.08.2026 UTC |
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DeepSeek has launched DeepSeek-V4-Flash-0731, delivering a significant boost in agent performance without changing the model’s core architecture.
Following an announcement last week, the company made the update available as a public beta through DeepSeek’s API, and the open weights were published on Hugging Face under the MIT license later the same day.
Although the model itself hasn’t changed, DeepSeek says additional post-training is responsible for the performance gains, showing that meaningful improvements don’t always require a larger model.
🚀 DeepSeek-V4-Flash Official API is now LIVE in public beta!🔷 We’ve massively upgraded its Agent capabilities—benchmark scores are now far surpassing the V4-Pro-Preview. Check out the massive performance leap below! 👇🔷 The official V4-Flash now natively supports the… pic.twitter.com/NUzOyxza2f— DeepSeek (@deepseek_ai) July 31, 2026
DeepSeek’s decision to release the production-ready weights under a permissive license gives organizations much more control over how they deploy and customize the model.
Although the model itself hasn’t changed, DeepSeek says additional post-training is responsible for the performance gains, showing that meaningful improvements don’t always require a larger model.
Same architecture, better results
DeepSeek says V4-Flash-0731 uses the same architecture as the preview release, with 284 billion total parameters and 13 billion activated parameters per token.
This is much smaller than V4-Pro, which has 1.6 trillion total parameters and 49 billion activated parameters. For companies running agents …