Term
DeepSeek V4.1-Flash
DeepSeek V4.1-Flash (September 10, 2026) is the smallest model in the new DeepSeek V4.1 architecture family — the first DeepSeek generation with native multimodal image processing. A speed-/cost-optimized "Flash" model with open weights (MIT), available via boostN.
DeepSeek V4.1-Flash — explained in more detail
DeepSeek V4.1-Flash is the smallest model in the new DeepSeek V4.1 architecture family, released on September 10, 2026. It is the first DeepSeek generation with native multimodal image processing — image input runs straight through the core architecture instead of a bolted-on encoder. As a “Flash” variant, the model is tuned for speed and cost, not maximum reasoning depth. The weights are open (MIT license, freely available on Hugging Face) — the model is self-hostable and is additionally offered through the DeepSeek API and several third-party providers; it is also available via boostN.
Key facts
- Release: September 10, 2026, the smallest model in the V4.1 family.
- Modality: first DeepSeek generation with native multimodal image processing.
- Class: “Flash” — speed-/cost-optimized, not a reasoning flagship.
- License/access: open weights (MIT) on Hugging Face, self-hostable; plus the DeepSeek API, third-party providers (Fireworks, DeepInfra, Novita, via OpenRouter) and boostN.
- Pricing (DeepSeek’s own figures): $0.30 input / $1.20 output per 1M tokens — well below Muse Spark ($1.25/$4.25), Claude Opus 5 ($5/$25) and Claude Fable 5.1 / GPT-6 Astra (both $10/$50).
- Benchmarks per DeepSeek: on typical coding tasks under an hour, V4.1-Flash leads with 74.2 points, ahead of Muse Spark (xhigh, 73) and GPT-5.6 Sol / Muse Spark (max, 72 each). On long-running agentic tasks over an hour it drops to 30 points, well behind Claude Fable 5.1 (58), GPT-6 Astra (56) and Claude Opus 5 (55).
- All figures are DeepSeek’s own measurements; no independent verification existed at writing time.
Example / Practical use
For short, well-scoped coding work — a bug fix, a single function, a refactor under an hour — V4.1-Flash reportedly beats far pricier models on its own numbers. For assignments needing hours of autonomous planning and multi-tool chaining, it falls short.
Distinction
V4.1-Flash does not replace reasoning-heavy models like Claude Fable 5.1 or GPT-6 Astra for long agentic work — there it scores roughly half their points. Its positioning stays that of a Flash model: fast and cheap for tightly scoped tasks, not multi-hour autonomous runs.