What it is
DeepSeek-V4.1-Flash is DeepSeek's fast open-weight model, first released on 10 September 2026 and updated on 17 October as DeepSeek-V4.1-Flash-1017, which now answers every deepseek-flash API call. It replaced V4-Flash, whose model names now route to the new version. It is a mixture-of-experts model with a 552B backbone that activates about 8 billion parameters per token when reading input and 16 billion when writing, plus a 196B 'Engram' memory looked up by token; the checkpoint totals 763 billion parameters.
It reads text and images and writes text, with a 1M-token context and up to 384K output. Reasoning effort can be set from 1 to 100. DeepSeek says its compressed attention cuts the KV cache to 890 bytes per token, about a quarter of V4-Flash's. Weights for both versions are on Hugging Face under the MIT licence.
On DeepSeek's API (model name deepseek-flash) it costs $0.30 per million input tokens and $1.20 per million output at peak, half that off-peak, and $0.003 to $0.006 for cached input. Peak hours are only 01:00 to 04:00 and 06:00 to 10:00 UTC on weekdays.
Our review
The October update made V4.1-Flash a serious coding-agent model. DeepSeek's card for the 1017 release reports 77.0% on DeepSWE v1.1, against 74.0% for Anthropic's Opus 5 in the same table, 60.5% on AutomationBench and 42.3% on Terminal-Bench 4.0, up from 31.2% for the September version. All are self-reported at maximum reasoning effort in harnesses DeepSeek chose, and we found no independent replication yet.
Where it trails is just as clear. Opus 5 still leads on Terminal-Bench 4.0 (51.8%), ProgramBench (37.0 to 29.1) and NL2Repo (75.3 to 68.2), and on Humanity's Last Exam without tools the score is 42.6 to Opus 5's 56.3. The harness matters as well: the September card shows DeepSWE falling from 74.2% with mini-SWE to 69.8% in Claude Code and 65.6% in Codex, so the headline figure is a best case.
The price is the argument. Off-peak covers weekends and most weekday hours, so a workload of 10 billion input and 1 billion output tokens a month costs about $2,100 off-peak or $4,200 at peak, before cache hits cut repeated input to $0.003 per million. The concurrency limit of 2,500 requests is five times V4-Pro's 500.
Two things limit who can use it. DeepSeek's privacy policy says data is processed and stored in China and may be used for training, with an opt-out on request, and it sets no separate terms for API traffic. Self-hosting avoids that, but the published checkpoint is about 765 GB in FP8 and int8, more than an eight-way H100 80 GB node holds. Third-party hosts fill some of the gap: on OpenRouter, DeepInfra serves an FP8 build at $0.112 input and $0.336 output per million, though that listing is for the September weights.
Where it earns its place
A developer-tools startup running background coding agents across 2,000 repositories
Resolve routine issues and dependency bumps overnight
Schedule runs outside 01:00 to 04:00 and 06:00 to 10:00 UTC to pay the off-peak $0.15/$0.60 rate; DeepSeek reports 77.0% on DeepSWE v1.1 with the mini-SWE harness. Expect less in Claude Code or Codex harnesses, which scored 4 to 9 points lower on the September card.
An operations team automating back-office work across SaaS tools
Run long multi-step automations that involve screenshots
Image input and a 1M-token context let one session hold tool output and screenshots, and the card's 60.5% on AutomationBench tops its comparison table. The API's 2,500-request concurrency limit rarely caps a mid-size deployment.
A CTO at a European fintech
Decide whether DeepSeek's API is acceptable for customer data
DeepSeek's privacy policy places processing and storage in China and lists model training among its uses, so customer data belongs on a self-hosted or EU-hosted deployment instead. The MIT licence allows that with no revenue cap, but the weights alone need more than 765 GB of GPU memory, for example eight 141 GB H200s.
Before you commit
- Setup
- OpenAI- or Anthropic-format API at api.deepseek.com with model deepseek-flash; thinking mode is on by default. For local use there is no Jinja chat template: DeepSeek ships a Python encoder and the deepseek-recipe Rust library instead.
- Integrations
- JSON output, tool calls, the Responses API, an Anthropic-compatible API and vision on DeepSeek's API; documented setups for Claude Code, Codex, OpenCode and DeepSeek Harness. OpenRouter lists third-party hosts including DeepInfra and Baidu.
- Data and privacy
- Weights under MIT with no use restrictions. On DeepSeek's service, the privacy policy (updated 10 February 2026) says data is stored in China, kept while the account exists and may train models, with an opt-out on request; no separate API retention terms are published.
- Cost at scale
- 10 billion input and 1 billion output tokens a month costs about $2,100 off-peak or $4,200 at peak on DeepSeek; cached input is $0.003 per million off-peak. Self-hosting needs over 765 GB of GPU memory for the weights alone.
- Limits
- 1M-token context and 384K output; 2,500 concurrent requests on the API. Legacy deepseek-v4-flash names now route to the 1017 model. Search and Fetch calls made through agent harnesses start billing at $0.75 per 1,000 on 1 November 2026, Beijing time.
What's good
- MIT licence with image input
- $0.15 / $0.60 per 1M tokens off-peak; cache hits from $0.003
- 77.0% on DeepSWE v1.1 (self-reported, 1017)
- KV cache of 890 bytes per token keeps 1M contexts cheap to hold
What's not
- Data stored in China; API training terms not spelled out
- Trails Opus 5 on Terminal-Bench 4.0 and HLE
- About 765 GB checkpoint for self-hosting
- Benchmarks self-reported and harness-sensitive
Best for
Cost-sensitive teams running coding and automation agents who can use a China-hosted API or a third-party host, or have multi-GPU servers for the open weights.
Alternatives
DeepSeek-V4.1-Flash: common questions
- Is DeepSeek-V4.1-Flash worth it?
- Near-frontier coding-agent scores for $0.15 per million input tokens, provided you can accept China-based data handling or host a 763B-parameter checkpoint yourself. We rate it 4.2 out of 5.
- How much does DeepSeek-V4.1-Flash cost?
- Free MIT-licensed weights. DeepSeek API: $0.15 input / $0.60 output per 1M tokens off-peak, $0.30 / $1.20 at weekday peak; cache hits $0.003 to $0.006.
- What are the downsides of DeepSeek-V4.1-Flash?
- Data stored in China; API training terms not spelled out; trails Opus 5 on Terminal-Bench 4.0 and HLE; about 765 GB checkpoint for self-hosting; benchmarks self-reported and harness-sensitive.
- Who is DeepSeek-V4.1-Flash best for?
- Cost-sensitive teams running coding and automation agents who can use a China-hosted API or a third-party host, or have multi-GPU servers for the open weights.
- How much does DeepSeek-V4.1-Flash cost for a team or in production?
- 10 billion input and 1 billion output tokens a month costs about $2,100 off-peak or $4,200 at peak on DeepSeek; cached input is $0.003 per million off-peak. Self-hosting needs over 765 GB of GPU memory for the weights alone.
- Is DeepSeek-V4.1-Flash safe for company data?
- Weights under MIT with no use restrictions. On DeepSeek's service, the privacy policy (updated 10 February 2026) says data is stored in China, kept while the account exists and may train models, with an opt-out on request; no separate API retention terms are published.
- What are the best DeepSeek-V4.1-Flash alternatives?
- The closest alternatives we have reviewed are DeepSeek-V4-Pro, Claude Haiku 5.5, GPT-6 Luna and Qwen3.8-Max.
Answers checked Oct 22, 2026.
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