deepseek
DeepSeek: DeepSeek V4.1 Flash (batch)
deepseek/deepseek-v4.1-flash:batch
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
OpenRouter lists deepseek/deepseek-v4.1-flash:batch as a batch variant and maps it to the canonical identifier deepseek/deepseek-v4.1-flash-20260910. The catalog and DeepSeek's documentation describe text-and-image input, text output, a context window of roughly one million tokens, adjustable reasoning effort, tool calling, and structured outputs. Vendor-reported evaluations emphasize agentic and software tasks, but they depend on harness and sampling choices. Batch-specific execution behavior is not explained in the supplied evidence.[1][2]
Strengths
- Long-context multimodal scope: OpenRouter lists text and image inputs with a context window near one million tokens, and DeepSeek documents support for approximately that length.[1][2]
- Cost-oriented design for long inputs: DeepSeek describes a sparse MoE with a causal encoder-decoder design that activates only part of its parameters, plus KV-cache compression intended to improve input-heavy agentic workloads.[2]
- Vendor-reported measurements at maximum reasoning effort show competitive results on several agentic, terminal, software-engineering, and security-related evaluations, but not across every comparison.[2]
Limitations
- Most technical detail comes from documentation for the canonical model mapped by OpenRouter; this source pack does not define batch queueing, turnaround, retention, or request-completion behavior.[1][2]
- The benchmark evidence is vendor-reported and tied to named harnesses, sampling settings, and maximum reasoning effort; independent workload testing remains necessary.[2]
- OpenRouter does not provide a knowledge cutoff, and the supplied sources include no dedicated safety assessment for this exact catalog variant.[1][2]
Where to use it
- For suitable asynchronous work, consider large-scale summarization, question answering, or extraction over long documents containing text and images after confirming the batch semantics.[1][2]
- Consider it for offline software-maintenance or agent-style jobs that use tools and benefit from long context, treating vendor benchmark results as directional rather than proof.[1][2]
- It may fit high-volume structured extraction or classification into text outputs from mixed text-and-image inputs, subject to validation of schema adherence, latency, and quality.[1][2]
Sources
- [1] OpenRouter catalog · DeepSeek: DeepSeek V4.1 Flash (batch)Sources checked: Nov 13, 2026
- [2] Model documentation · DeepSeek: DeepSeek V4.1 Flash (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 is a sparse mixture-of-experts model from DeepSeek, and the first built on the company's Causal Encoder-Decoder (CED) architecture. It activates 8B parameters on input and 16B on...
Specifications
- Model ID
- deepseek/deepseek-v4.1-flash:batch
- Canonical ID
- deepseek/deepseek-v4.1-flash-20260910
- Creator
- deepseek
- Added to OpenRouter
- 10.09.2026
- Context length
- 1,048,576
- Modality
- text+image->text
- Input modalities
- text, image
- Output modalities
- text
- Token pricing
- USD per 1M tokens: in $0.112 / out $0.336
- 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 | No measurement | 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
No evaluations are supplied for this model.
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) |
|---|---|---|---|---|---|---|
| DeepInfrafp8 | 1,048,576 | $0.112 | $0.336 | — | — | — |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.