deepseek
DeepSeek: DeepSeek V4.1 Flash 1017 (batch)
deepseek/deepseek-v4.1-flash-1017:batch
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:batch is the batch catalog variant of DeepSeek’s October 17, 2026 V4.1 Flash snapshot. OpenRouter describes a sparse mixture-of-experts model that accepts text and images and returns text, with a 1M-token context and optional reasoning that defaults to high effort. The model card calls it a minor update and reports gains in reasoning, coding, agentic, and visual benchmarks plus shorter execution and outputs; those are publisher-reported results, so workload validation remains necessary.[1][2]
Strengths
- Accepts text and image inputs and produces text, with a 1M-token context and optional reasoning that defaults to high effort.[1][2]
- The publisher reports improvements over the prior V4.1 Flash across reasoning, coding, agentic, and visual evaluations.[2]
- Prompt-encoding materials cover multi-turn conversations, tool calls, thinking mode, and interleaved images; MIT licensing is stated.[2]
Limitations
- Evidence consists of OpenRouter’s catalog and DeepSeek’s model card; independent validation on the intended workload is not supplied.[1][2]
- No Jinja chat template is included, so integration may require the supplied Python encoder or deepseek-recipe.[2]
- The supplied evidence does not state a knowledge cutoff or explain batch-specific behavior.[1][2]
Where to use it
- Candidate for high-volume batch processing of long text-plus-image inputs, if the variant’s behavior fits.[1][2]
- Worth evaluating for coding, terminal, and agentic workflows that need tool-call formatting and tunable reasoning.[1][2]
- Potential fit for long-context image-to-text analysis where text-only output is sufficient.[1][2]
Sources
- [1] OpenRouter catalog · DeepSeek: DeepSeek V4.1 Flash 1017 (batch)Sources checked: Nov 13, 2026
- [2] Model documentation · DeepSeek: DeepSeek V4.1 Flash 1017 (batch)Sources 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:batch
- 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 $0.050 / out $0.150
- 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 | — |
| 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) |
|---|---|---|---|---|---|---|
| Sail Researchfp4 | 1,048,576 | $0.050 | $0.150 | — | — | — |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.