deepseek
DeepSeek: DeepSeek V4.1 Flash 1017
deepseek/deepseek-v4.1-flash-1017
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
deepseek/deepseek-v4.1-flash-1017 is the October 17, 2026 snapshot of DeepSeek V4.1 Flash, according to OpenRouter and DeepSeek's documentation. The catalog lists text and image inputs, text output, a context window of about one million tokens, optional reasoning with several effort levels, and tool or structured-output controls. DeepSeek describes the update as a sparse mixture-of-experts model with substantial improvements in reasoning, coding, agentic and visual benchmarks, plus shorter task execution and output length. Those gains are vendor-reported and should be validated on target workloads.[1][2]
Strengths
- DeepSeek reports that this 1017 snapshot improves reasoning, coding, agentic and visual results over the prior V4.1 Flash while reducing task time and output length.[2]
- The OpenRouter catalog lists text and image inputs, text output, optional reasoning effort settings, tool calling, parallel tool calls and structured outputs.[1]
- The model documentation states that the repository and weights are MIT licensed and provides reference encoding plus a maintained deepseek-recipe toolkit.[2]
Limitations
- Evidence is limited to OpenRouter's catalog entry and DeepSeek's own documentation; independent verification and workload-specific testing remain necessary.[1][2]
- Benchmark gains are reported by DeepSeek rather than independently reproduced here, and OpenRouter's benchmark coverage is confined to selected design-arena categories and an incomplete analysis index.[1][2]
- DeepSeek does not provide a Jinja chat template; integrations may need its reference encoder or deepseek-recipe, and the OpenRouter entry leaves knowledge cutoff unspecified.[1][2]
Where to use it
- Consider for agentic coding or automation pilots where tool use, structured outputs and controllable reasoning matter, subject to validation.[1][2]
- Consider for long-context document or mixed text-and-image analysis, while confirming image behavior and effective context in the target stack.[1][2]
- Potentially suitable for cost-conscious production workloads because DeepSeek claims faster execution and shorter outputs than the prior snapshot; measure latency, token use and quality before rollout.[2]
Sources
- [1] OpenRouter catalog · DeepSeek: DeepSeek V4.1 Flash 1017Sources checked: Nov 13, 2026
- [2] Model documentation · DeepSeek: DeepSeek V4.1 Flash 1017Sources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
DeepSeek V4.1 Flash 1017 is the October 17, 2026 snapshot of DeepSeek V4.1 Flash, served by DeepSeek under the `deepseek-flash` API model name. V4.1 Flash is a sparse mixture-of-experts model...
Specifications
- Model ID
- deepseek/deepseek-v4.1-flash-1017
- Canonical ID
- deepseek/deepseek-v4.1-flash-20261017
- Creator
- deepseek
- Added to OpenRouter
- 17.10.2026
- Context length
- 1,048,576
- Modality
- text+image->text
- Input modalities
- text, image
- Output modalities
- text
- Token pricing
- USD per 1M tokens: in $0.150 / out $0.600
- Knowledge cutoff
- —
- Expiration date
- —
Tasks and capabilities
A grade appears only where the source supplies a matching measurement for that task, the model is active and the cohort holds at least 10 comparable models. The capability column lists facts the source declares — supported formats, reasoning and tool parameters — which are not measured quality.
| Task | Grade | Source-declared capability |
|---|---|---|
| General quality | BB — next 30% of the cohort | reasoning: optional · default high · efforts: max, high, low, none · parameters: include_reasoning, reasoning, reasoning_effort |
| Coding | No measurement | — |
| Agents | No measurement | parameters: structured_outputs, tool_choice, tools |
| Writing | No measurement | output: text |
| Reasoning | No measurement | reasoning: optional · default high · efforts: max, high, low, none · parameters: include_reasoning, reasoning, reasoning_effort |
| Document analysis | No measurement | — |
| Vision | No measurement | input: image |
| Image generation | No measurement | — |
| Audio | No measurement | — |
| Video | No measurement | — |
| Embeddings | No measurement | — |
Evaluations
| Source | Benchmark | Value | Grade | Data retrieved |
|---|---|---|---|---|
| Artificial Analysis | Intelligence index | 28.6 | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | agents · spreadsheetwin rate 51.2% · rank 4 | 1,292Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · 3dwin rate 47.3% · rank 12 | 1,347Elo | SS — top 10% of the cohort | 13.11.2026 |
| Design Arena | models · asciiartwin rate 47.9% · rank 20 | 1,235Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · codecategorieswin rate 48.2% · rank 19 | 1,287Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · datavizwin rate 49.7% · rank 31 | 1,259Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · gamedevwin rate 42.6% · rank 38 | 1,251Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · svgwin rate 51.3% · rank 19 | 1,243Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · uicomponentwin rate 48.9% · rank 24 | 1,286Elo | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · websitewin rate 51.9% · rank 15 | 1,297Elo | AA — next 20% of the cohort | 13.11.2026 |
How grades work
Each letter grade compares a model only against other models scored by the same source and metric in the current snapshot. S means the top 10%, A the next 20%, B the next 30%, C the rest. Models without a published measurement are shown as not rated — a supported format alone never implies measured quality.
- S — S — top 10% of the cohort
- A — A — next 20% of the cohort
- B — B — next 30% of the cohort
- C — C — below the B band
Providers
Optional live provider statistics from OpenRouter. Missing values are unknown, not zero.
| Provider | Context length | Price in /1M | Price out /1M | Latency p50 (30m, s) | Throughput p50 (30m, tok/s) | Uptime (30m) |
|---|---|---|---|---|---|---|
| Particle AI | 1,048,576 | $0.030 | $0.250 | — | — | 100% |
| Wafermxfp4 | 1,048,576 | $0.038 | $0.500 | — | — | 100% |
| PrimeIntellect | 1,048,576 | $0.044 | $0.700 | — | — | 100% |
| Particle AI | 1,048,576 | $0.060 | $0.500 | — | — | 100% |
| OpenInferencefp4 | 1,048,576 | $0.070 | $0.800 | — | — | 100% |
| Sail Researchfp4 | 1,048,576 | $0.073 | $0.108 | — | — | 100% |
| Sail Researchfp4 | 1,048,576 | $0.073 | $0.108 | — | — | 100% |
| Ionstream | 1,048,576 | $0.077 | $0.232 | — | — | 100% |
| DeepInfrafp8 | 1,048,576 | $0.078 | $0.235 | — | — | 100% |
| Wafermxfp4 | 1,048,576 | $0.079 | $0.500 | — | — | 100% |
| InferenceNetfp8 | 1,040,000 | $0.080 | $0.605 | — | — | 100% |
| InferenceNetfp8 | 1,040,000 | $0.080 | $0.605 | — | — | 100% |
| DeepInfrafp8 | 1,048,576 | $0.098 | $0.294 | — | — | 100% |
| Cloudflare | 1,048,576 | $0.100 | $0.600 | — | — | 100% |
| Morphfp8 | 1,048,576 | $0.104 | $0.583 | — | — | 100% |
| StepFunfp8 | 1,000,000 | $0.105 | $0.420 | — | — | 100% |
| Alibaba | 1,000,000 | $0.105 | $0.420 | — | — | 100% |
| StreamLakefp8 | 1,024,000 | $0.111 | $0.444 | — | — | 100% |
| River | 1,048,576 | $0.120 | $0.480 | — | — | 100% |
| Relacefp4 | 1,048,576 | $0.120 | $0.500 | — | — | 100% |
| DeepInfrafp8 | 1,048,576 | $0.147 | $0.441 | — | — | 100% |
| Baidufp8 | 1,048,576 | $0.150 | $0.605 | — | — | 100% |
| Coherefp8 | 1,048,576 | $0.150 | $0.500 | — | — | 100% |
| DeepSeek | 1,048,576 | $0.150 | $0.600 | — | — | 100% |
| Makorafp8 | 1,048,576 | $0.150 | $1.2 | — | — | 100% |
| Reka | 1,048,576 | $0.160 | $1.1 | — | — | 100% |
| Ionstream | 1,048,576 | $0.165 | $0.495 | — | — | 100% |
| Novitafp8 | 1,048,576 | $0.165 | $0.660 | — | — | 100% |
| DekaLLM | 1,048,576 | $0.180 | $0.680 | — | — | 100% |
| DigitalOcean | 1,048,576 | $0.180 | $0.720 | — | — | 100% |
| GMICloudfp8 | 1,048,575 | $0.180 | $0.720 | — | — | 100% |
| AtlasCloudfp8 | 1,048,576 | $0.195 | $0.780 | — | — | 100% |
| Fireworks | 1,048,576 | $0.210 | $0.840 | — | — | 100% |
| PrimaLabs | 1,048,576 | $0.240 | $0.960 | — | — | 100% |
| Modal | 1,048,576 | $0.250 | $1 | — | — | 100% |
| Crusoefp8 | 1,048,576 | $0.290 | $1.2 | — | — | 100% |
| SiliconFlowfp8 | 1,048,576 | $0.300 | $0.550 | — | — | 100% |
| Mistralfp8 | 1,048,576 | $0.300 | $1.2 | — | — | 100% |
| CoreWeavefp8 | 1,048,576 | $0.300 | $1.2 | — | — | 100% |
| Near AI | 1,048,576 | $0.300 | $1.2 | — | — | 100% |
| Venicefp8 | 1,000,000 | $0.300 | $1.2 | — | — | 100% |
| Nebiusfp8 | 1,048,576 | $0.300 | $1.2 | — | — | 100% |
| Together | 1,048,576 | $0.300 | $1.2 | — | — | 100% |
| Fireworks | 1,048,576 | $0.300 | $1.2 | — | — | 100% |
| BaseTen | 1,048,576 | $0.300 | $1.2 | — | — | 100% |
| Parasailfp8 | 1,048,576 | $0.300 | $1.2 | — | — | 100% |
| Fireworks | 1,048,576 | $0.450 | $1.8 | — | — | 100% |
| BaseTen | 1,048,576 | $0.600 | $2.4 | — | — | 100% |
Provider data retrieved: 13.11.2026
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