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Official launch · Tier A API model name: deepseek-flash 1M context · native vision MIT open weights
2026-09-10 · Model Watch · DeepSeek API Docs change log + Hugging Face model card, Sep 10

DeepSeek-V4.1-Flash Official Launch: deepseek-flash API From $0.15/$0.60 per 1M, 1M Context, MIT Open Weights

On September 10, 2026, DeepSeek officially released DeepSeek-V4.1-Flash — the smallest model in its new architecture family, with native multimodal vision, a 1M-token context window, and MIT-licensed open weights. On the API it answers to a new name, deepseek-flash, at a cut rate card: $0.15 input (cache miss) / $0.60 output per 1M tokens off-peak. The previous V4 Flash generation is retired with its old model names temporarily aliased to the new model, and from Sep 14 (12:00 Beijing) even deepseek-v4-pro traffic routes to V4.1 Flash at Flash pricing until V4.1 Pro ships.

Direct answer

DeepSeek-V4.1-Flash is officially live (Sep 10, 2026). Call it deepseek-flash — 552B-backbone MoE, 8B/16B active per token, native image+text input, 1M context, 384K max output, MIT weights on Hugging Face. Pricing (verified official rate card): $0.15 per 1M input (cache miss) / $0.003 cache-hit / $0.60 output off-peak; peak (01:00–04:00 & 06:00–10:00 UTC, Mon–Fri) doubles it. That's under 1/4 of V4 Pro's input price and under 1/3 of its output price on the same card. Routing changes: deepseek-v4-flash / deepseek-v4-flash-vision-exp now alias to V4.1 Flash (old models retired); from Sep 14, 12:00 Beijing (04:00 UTC), deepseek-v4-pro also routes to V4.1 Flash at Flash prices until V4.1 Pro is released. Concurrency limit: 2,500. For context on the predecessor's short experiment, see V4-Flash-Vision-Exp (Aug 21).

What's confirmed (both sources checked Sep 10, UTC+8)

  • The release: DeepSeek's change log entry dated 2026-09-10 announces the official release. V4.1-Flash is "the smallest model in our new architecture family, with native multimodal visual understanding"; the architecture targets a higher capability ceiling, faster inference, higher throughput, and scaling to larger models.
  • The model card: the Hugging Face README ("Pushing the Limits of KV Cache Compression") specs a 552B-backbone multimodal MoE — 1 shared + 384 routed experts, 6 activated per token — activating 8B parameters/token at prefill and 16B at decode, with a 1M-token context trained on 45T tokens (sparse attention at 64K, extended to 1M at 34T).
  • Architecture headline: a Causal Encoder-Decoder (CED) — 20-layer causal encoder + 20-layer decoder — projects the decoder's global KV cache from final encoder states; FP4 main KV caching brings the global cache to ~890 bytes/token, ≈1/4 of DeepSeek-V4-Flash (≈437× less than V1), while SWA Bounded Replay cuts the persistent cache to ≈1/8.
  • Headline benchmarks (official, max reasoning effort): GPQA Diamond 90.9, Codeforces rating 3471, Terminal-Bench 2.1 90.6, DeepSWE v1.1 74.2, HLE w/ tools 63.9, Agents' Last Exam 31.8. Reasoning effort is continuously controllable (1–100). Vendor-run numbers — treat as such until third-party replication lands.
  • API name and aliases: new requests should use deepseek-flash. deepseek-v4-flash and deepseek-v4-flash-vision-exp are "still accepted" but temporarily routed to V4.1 Flash — the V4-generation models they named have been retired.
  • V4 Pro retirement schedule: after 12:00 Beijing time (04:00 UTC) on September 14, 2026, and until the future release of V4.1 Pro, all deepseek-v4-pro requests are routed to V4.1 Flash and billed at the V4.1 Flash price. DeepSeek frames it as an "orderly retirement" after testing showed V4.1 Flash beats V4 Pro on performance, cost, speed and total time.
  • Price cuts are official: "With the release of DeepSeek-V4.1-Flash, API prices have been reduced accordingly." The verified card: off-peak $0.15/$0.60 per 1M (input cache-miss / output), $0.003 cache-hit input, peak = 2× during 01:00–04:00 & 06:00–10:00 UTC Mon–Fri (09:00–12:00 & 14:00–18:00 Beijing). Concurrency 2,500 vs V4 Pro's 500. Off-peak discount fans already know the pattern from the weekend off-peak pricing shift — this makes off-peak the default again.
  • Open weights, MIT: the HF repo ships weights under MIT, a technical report PDF, a self-contained prompt-encoding reference (no Jinja chat template), and local inference instructions — same open-weights posture as Vision-Exp's ungated MIT drop, now on the mainline multimodal model.

What didn't ship — and what to watch

  • No V4.1 Pro yet. As of Sep 10 (UTC+8), the change log names V4.1 Pro only as a future release bounding the V4 Pro routing switch. No date, no benchmarks. Expect a separate event — the new family "scales to larger models" by design.
  • Alias names are temporary. If you still call deepseek-v4-flash or deepseek-v4-flash-vision-exp, you're already getting V4.1 Flash at Flash prices — but the docs mark the routing as temporary. Migrate to deepseek-flash now to avoid a future break.
  • Sep 14, 04:00 UTC is the V4 Pro cliff. After that moment, deepseek-v4-pro responses come from V4.1 Flash — behavior and benchmark numbers will differ from V4-Pro-0813 even though the model name doesn't change. Pin your evals before Monday noon Beijing time.
  • Third-party coverage pending. Per this site's launch policy we cite only first-party official sources for release facts; independent benchmark replication and overseas/X analysis typically land 24–48h after a DeepSeek launch and are not yet reflected here.

Why the KV cache number matters for builders

  • Long-context agents get cheaper where it hurts: input. 890 bytes/token of global KV (vs ~3.5KB on V4-Flash) means million-token contexts stop being a memory bomb. With 8B active params at prefill, input-heavy workloads — repo-scale RAG, long agent trajectories, document pipelines — are exactly the profile this model optimizes.
  • The rate card rewards cache discipline. Cache-hit input at $0.003 (off-peak) is 50× cheaper than a cache-miss input call. With the DeepSeek API's implicit caching, stable prefixes (system prompts, tool schemas, long documents) become almost free to reuse.
  • Concurrency 2,500. Four times V4 Pro's limit of 500 — a signal about throughput headroom on the new architecture. Rate-limit details live in the official Rate Limit & Isolation page.
  • Self-host vs API: MIT weights make the hedge real, but a 552B-backbone model (even at 8B/16B active) is multi-node GPU territory. Most builders should consume the API and keep the weights as a fallback — DeepSeek's own DeepSeek-V4-Pro-for-builders calculus shifts again with V4 Pro itself now scheduled to route into Flash.

Access from outside China — and the usual disclaimer

The official endpoint (https://api.deepseek.com) serves OpenAI-format and Anthropic-format bases, with Responses API, tool calls, JSON output and vision on deepseek-flash. International builders should verify payment rails and billing independently — official top-up options remain China-centric for some payment methods.

Independence disclaimer: ChinaModelAPI is an independent information site, not affiliated with DeepSeek. Every fact and price in this article was transcribed from DeepSeek's official change log and official Hugging Face model card on Sep 10, 2026 (UTC+8); This article is not a purchase recommendation. Verify prices on the official rate card before you build.

Primary sources

FAQ (2026)

What is DeepSeek-V4.1-Flash?

The smallest model in DeepSeek's new architecture family, officially released Sep 10, 2026: 552B-backbone multimodal MoE (8B/16B active per token), native image+text input, 1M context, MIT open weights.

What API model name?

deepseek-flash. Legacy deepseek-v4-flash and deepseek-v4-flash-vision-exp still work but temporarily alias to V4.1 Flash — their original models are retired.

How much does it cost?

Off-peak $0.15/$0.60 per 1M (input cache-miss / output), $0.003 cache-hit input; peak (01:00–04:00, 06:00–10:00 UTC Mon–Fri) is 2×. Max output 384K, concurrency 2,500.

What about deepseek-v4-pro?

From Sep 14, 2026, 12:00 Beijing (04:00 UTC), until V4.1 Pro ships, deepseek-v4-pro requests route to V4.1 Flash and bill at Flash prices — an orderly retirement.

Are the weights open?

Yes — MIT on Hugging Face, with tech report and inference instructions. A 552B-backbone model is multi-node territory; most builders will use the API and keep weights as a hedge.

Why care about KV cache compression?

~890 bytes/token global KV (≈1/4 of V4-Flash) via FP4 KV + CSA2 + CED architecture — long-context, input-heavy agent workloads get cheaper and faster, with a 2,500-request concurrency ceiling.

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