DeepSeek-V4.1-Flash

DeepSeek-V4.1-Flash is the model behind the API name deepseek-flash, released on 10 September 2026 under an MIT licence. It replaced the earlier V4 Flash, whose old API names now route to it, so a review of V4 Flash is a review of a different model and is not counted here.

On 6 October 2026 we read eleven sources: three official pages, three evaluations or reviews, two news reports, one Hacker News thread and two other pages, dated 10 to 16 September 2026 or read on the day. The buyer tier is empty because we read no app-store rating, so this is a reading of the maker, editors and developers, and not of long-term owners.

Consensus

Across the eleven sources we read, three themes recur: a low price, high speed, and strength at agent-style coding work. Confidence is mixed. Only three of the eleven sources are evaluations or reviews, the model was under four weeks old on the day we read it, and the coding figures are mostly the maker's own. This is a reading of that version on that date, not a measurement of the model.

The maker's pricing page, read on 6 October, lists $0.15 per million uncached input tokens and $0.60 per million output tokens off-peak, with both doubled at peak hours. It lists a context of one million tokens. The Hugging Face model card, also read that day, gives the licence as MIT. These are verified facts from official pages. A benchmark score on the model card is the maker's claim, and we report it as one.

Recurring strengths

A low price or cost. Seven of the eleven sources put the price or the cost in the foreground: the pricing page, two of the three evaluations and reviews, both news reports, the security firm's write-up and the Hacker News thread. Artificial Analysis lists an average of $0.27 per task on its own index. The security firm reports $4.65 for the accepted runs of its own benchmark, and that figure is its own measurement.

Speed. Two of the three evaluations and reviews describe it as fast. Artificial Analysis lists 214 output tokens a second. The MindStudio hands-on review reports a range of 300 to 400 and more tokens a second during its coding runs. These are different tests on different setups, so the two figures are not averaged.

Agent-style coding and security work. Seven of the eleven sources describe it as strong at this work: two of the three evaluations and reviews, both news reports, the model card, the security firm's write-up and the Hacker News thread. The news reports and the model card carry the maker's figures of 74.2 on a coding benchmark called DeepSWE v1.1, against 62.7 for the larger V4 Pro, at maximum reasoning effort. One security firm reports its own benchmark of eleven vulnerable targets, with code execution reached on all eleven, and states that its audit found routes its authors had not planned. The firm says those routes belong to its private test setup and carry no claim about the upstream products.

Recurring complaints

Uneven results from task to task. Three sources describe it: one of the three evaluations and reviews, one other page and the Hacker News thread. The MindStudio hands-on reviewer reports a repeating loop on a physics problem that cleared on a retry. A second MindStudio page reports a failed cube simulation and weaker spatial reasoning. The thread carries developer comments of loops, weak instruction following and over-built answers. How common this is we cannot say: no source counts it.

Results that depend on the harness and the provider. Two sources, one of the three evaluations and reviews and the Hacker News thread, say the outcome changes with the agent framework, the inference provider and the quantisation. OmniaKey reports an 8.7-point range in scores across agent frameworks.

Not ahead on every measure. Three sources, one of the three evaluations and reviews and both news reports, say so. OmniaKey reports that V4 Pro stays higher on a science benchmark and on a text-only exam. The Rundown reports an Artificial Analysis index of 40 for this model against 53 for two US models. It also reports the maker's GPQA Diamond score of 90.9 against 92.4 for V4 Pro. SiliconANGLE reports that Anthropic and OpenAI models led on that science benchmark too.

A large model to host yourself. Three sources, none of them an evaluation or review, put the model at 552 billion parameters: the model card, SiliconANGLE and the second MindStudio page. The MindStudio page says a single consumer graphics card is not viable without quantisation.

Where reviewers split

Speed is reported two ways. MindStudio's hands-on review reports 300 to 400 and more tokens a second while Artificial Analysis lists 214. We read the gap as two different tests, not as one of them being wrong.

The maker's coding figure and the independent index tell different stories. The model card carries the maker's 74.2 on DeepSWE v1.1, a score at maximum reasoning effort. Artificial Analysis places the model at 39 on its index, and The Rundown reported 40 on 11 September. We read the index as the broader and less flattering measure, and the discrepancy between 39 and 40 as unexplained.

The security result splits by tier. The security firm's write-up reports a clean run on its own benchmark. In the Hacker News thread that discussed it, some developers report weaker results than a rival model on complex tasks. The MindStudio hands-on review, dated release day, describes the model as a preview the maker could pull down, while the change log shows it released as the current Flash model; we count it, and mark its age. Artificial Analysis alone describes the model's output as long.

Who it suits

Developers who want a cheap model for high-volume agent work, and who will run it through their own harness before relying on it. The evidence for that sits in seven sources on price and seven on agent-style work. Teams that can host a model of this size, since the weights are published under an MIT licence.

Who should pass

Readers who need a verdict on long-term reliability: no owner reviews or app-store ratings were read, and the model was under four weeks old. Readers who want a single consumer graphics card to run it, since the sources describe quantisation as essential. Readers who treat a benchmark score as a promise: every coding score here is a maker's claim or a specific benchmark run, not a measure of how the model behaves in their work.

One verdict a week: the most useful Master Review we finished, the complaint that kept appearing, and who should skip it. Send the weekly verdict.

Sources

• DeepSeek API, Models and Pricing, official, read 6 October 2026.
• DeepSeek API change log, official, entry of 10 September 2026.
• DeepSeek-V4.1-Flash model card on Hugging Face, official, read 6 October 2026.
• Artificial Analysis, DeepSeek V4.1 Flash, editorial evaluation, read 6 October 2026.
• MindStudio, hands-on test, editorial, 10 September 2026.
• OmniaKey review, editorial, 10 September 2026.
• SiliconANGLE, release report, news, 10 September 2026.
• The Rundown, pricing report, news, 11 September 2026.
• Enclave, hacking benchmark write-up, other, a security firm's own benchmark, 16 September 2026.
• MindStudio, local deployment guide, other, 12 September 2026.
• Hacker News thread on the Enclave write-up, forum, read 6 October 2026.

On 6 October 2026 we read 3 official pages, 3 evaluations and reviews, 2 news reports, 1 forum thread and 2 other pages, dated 10 to 16 September 2026, by web search and plain page fetch, and synthesised them with AI; we collected no buyer reviews and tested nothing.