DeepSeek-V4.1-Flash is a 552B MoE that activates 8B on the way in

DeepSeek released DeepSeek-V4.1-Flash on September 10, 2026. The homepage calls it the official version of the new architecture family: native image understanding, a 552B-parameter Mixture-of-Experts backbone, and a Causal Encoder–Decoder that activates 8B parameters on input and 16B on output. Weights are on Hugging Face under MIT. The API name is deepseek-flash.

The X post from @deepseek_ai is the launch signal. The news page and the model card are where the numbers live.

This is a cost story dressed as a flagship. DeepSeek is retiring V4-Flash and V4-Flash-Vision-Exp now, and will route deepseek-v4-pro to V4.1-Flash at Flash rates from 04:00 UTC on September 14, 2026, until a future V4.1-Pro. If you still pin Pro by name, you get Flash billing after that cutover.

What changed in the architecture

The model card describes a 40-layer Transformer split as a 20-layer causal encoder plus a 20-layer decoder. Decoder KV is projected from the encoder’s last hidden states, not rebuilt per decoder layer. Prefill is cheaper (8B active). Decode is 16B. MoE layout: 1 shared expert and 384 routed experts per layer, 6 routed experts active per token.

Compressed Sparse Attention 2 plus FP4 main KV caching (E2M1, one E4M3 scale per 16 channels) is how they get the cache number people will repeat: 890 bytes of global KV per token. DeepSeek says that is about 1/4 of V4-Flash and about 437× smaller than V1. The launch post also says 1/4 the HBM and 1/8 the SSD versus the previous generation. Cache-hit tokens are a large share of agent bills; shrinking the cache is the point.

Other card details worth keeping as DeepSeek-reported, not independently rerun: Engram conditional memory (196B parameters, sparse lookup), DSpark speculative decoding, a from-scratch DeepSeek-ViT, 45T multimodal pretrain tokens, context extended to 1M at 34T tokens. Instruct models expose a reasoning effort integer from 1 to 100. All instruct scores below use effort 100.

A GitHub repo named DeepSeek-V4.1-Flash was not public when checked September 10, 2026. The weights and the technical report PDF are on Hugging Face.

DeepSeek’s own table is mixed

DeepSeek-reported instruct scores, max effort, temperature=1.0, top_p=0.95. Agentic coding benches used DeepSeek Harness Minimal with a 1M window unless noted. These are vendor numbers.

Benchmark	Opus 5.0	GPT-5.6 Sol	V4-Pro	V4.1-Flash
GPQA Diamond	93.4	94.1	92.4	90.9
HLE	56.3	44.5	42.7†	36.8 (39.1†)
Terminal-Bench 2.1	89.1	88.8	87.9	90.6
Terminal-Bench 3.0	43.3	34.4	11.8	30.0
Terminal-Bench 4.0	51.8	39.9	12.4	31.2
DeepSWE v1.1	74.0	73.0	62.7	74.2
ProgramBench	37.0	23.0	15.5	20.3
NL2Repo-Bench	75.3	56.8	61.5	64.0
CyberGym	-	84.5	83.3	88.1
SEC-Bench Pro	-	74.3	56.4	62.8
ExploitGym	22.1	33.7	5.4	15.3
AutomationBench	50.3	45.8	43.2	54.8
Agent’s Last Exam	28.6	26.7	25.7	31.8
HLE w/ tools	63.6	-	60.0	63.9

† Text-only HLE subset.

V4.1-Flash is not “ahead of flagship models” as a blanket claim. It leads DeepSeek’s table on Terminal-Bench 2.1, DeepSWE, CyberGym, AutomationBench, and Agent’s Last Exam. It trails Opus 5.0 badly on Terminal-Bench 3.0 and 4.0, ProgramBench, NL2Repo, and HLE. GPT-5.6 Sol still owns GPQA Diamond, SEC-Bench Pro, ExploitGym, and ZeroBench-main in that same card.

Scaffolding moves the DeepSWE number: mini-SWE 74.2, DSH Minimal 72.6, Claude Code 69.8, Codex 65.6. If you quote 74.2, you are quoting mini-SWE, not a harness-invariant result.

LMArena’s text leaderboard, checked September 10, 2026, lists DeepSeek V4-Pro and V4-Flash variants. It does not list deepseek-v4.1-flash. Artificial Analysis had no public 4.1-Flash model page (404). Say not listed on the public leaderboards checked, not “no benchmarks exist.”

API: deepseek-flash, 1M context, vision, cheap cache hits

Pricing is per million tokens. New rates took effect 04:00 UTC on September 10, 2026. Off-peak is half of peak. Peak windows: 01:00–04:00 and 06:00–10:00 UTC, Monday through Friday.

deepseek-flash:

• Off-peak: cache hit $0.003, cache miss $0.15, output $0.60
• Peak: cache hit $0.006, cache miss $0.30, output $1.20

Context 1M. Max output 384K. JSON, tools, Responses API, Anthropic-format API, vision. FIM is non-thinking only. Thinking is the default; see DeepSeek’s thinking-mode guide. Concurrency limit 2500 vs 500 on the still-listed Pro id.

Legacy names deepseek-v4-flash and deepseek-v4-flash-vision-exp already route to V4.1-Flash at Flash prices.

Named synthetic bill, no tools, no subscription: 1M cache-miss input + 200k output.

• Flash off-peak: $0.27
• Flash peak: $0.54
• Same shape at GPT-5.6 Sol $4 / $20: $8.00
• Claude Fable 5.1 $10 / $50: $20.00

Cache hits change the story. 1M hit + 200k output off-peak is $0.123. That is why DeepSeek keeps talking about KV bytes.

OpenAI-format base URL https://api.deepseek.com. Anthropic-format https://api.deepseek.com/anthropic. DeepSeek says WorkBuddy (including CodeBuddy) and OpenCode already support the new id.

What to do with it

If you run agents on DeepSeek today, switch the id to deepseek-flash before September 14, when Pro requests become Flash anyway. Re-test Terminal-Bench 3/4 style long computer-use, not only TB 2.1. The vendor table says the cheap model wins some agent loops and still loses the newer terminal benches to Opus 5.0.

If you self-host, the card points at the inference folder for weight conversion. Recommended sampling: temperature 1.0, top_p 0.95 or 1.0, 1M context, max_tokens at least 256K. There is no Jinja chat template in this release; encoding is a Python reference plus deepseek-recipe.

Independent scores are still pending. Treat the Hugging Face table as DeepSeek-reported, with harness footnotes attached.