deepseek
DeepSeek: DeepSeek V4 Flash 0731 (batch)
deepseek/deepseek-v4-flash-0731:batch
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
DeepSeek V4 Flash 0731 (batch) is the OpenRouter catalog's batch listing of a sparse mixture-of-experts text model with 13B active parameters out of 284B total and a context window of about one million tokens. The underlying model card describes this checkpoint as the official V4 Flash release with substantially improved agentic capability and reports strong vendor-run coding and agent benchmarks. Reasoning effort is configurable, and tool calling plus structured outputs are exposed. Treat vendor figures as unaudited and validate batch behavior on your own workloads.[1][2]
Strengths
- Very long context window of roughly one million tokens, according to the OpenRouter catalog, making it suitable for very large text inputs.[1]
- The model card reports strong results on coding, tool-use and agent benchmarks versus prior DeepSeek releases and names proprietary rivals; these are vendor-reported, not independently verified.[2]
- Configurable reasoning effort, tool calling and structured-output parameters are listed in the catalog; the card documents low, high and max effort levels.[1][2]
Limitations
- Evidence for this exact ':batch' variant is catalog-only; batch-specific semantics such as latency, queuing or completion guarantees are not documented in the source pack.[1]
- Third-party design-arena listings in the catalog show mid-ranked, mixed outcomes, and the model card's benchmark table is self-reported; an independent model card for the batch endpoint was not included.[1][2]
- Text-only input and output, no stated knowledge cutoff, and no Jinja chat template; the card requires using its custom encoding scripts for prompts and output parsing.[1][2]
Where to use it
- Suggested for batch analysis, summarization or transformation of very large text corpora where deferred completion is acceptable.[1]
- Plausibly suited to coding, reasoning and agentic pipelines, per the vendor's positioning and benchmarks; validate on your own repositories and harness first.[2]
- Candidate for tool-calling workflows that need structured outputs, given the catalog-listed parameters, pending workload testing.[1]
Sources
- [1] OpenRouter catalog · DeepSeek: DeepSeek V4 Flash 0731 (batch)Sources checked: Nov 13, 2026
- [2] Model documentation · DeepSeek: DeepSeek V4 Flash 0731 (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 Flash 0731 is a sparse mixture-of-experts model from DeepSeek, with 13B active parameters out of 284B total. This re-post-trained revision is suited for coding, reasoning, and agent workflows....
Specifications
- Model ID
- deepseek/deepseek-v4-flash-0731:batch
- Canonical ID
- deepseek/deepseek-v4-flash-20260731
- Creator
- deepseek
- Added to OpenRouter
- 31.07.2026
- Context length
- 1,048,576
- Modality
- text->text
- Input modalities
- text
- Output modalities
- text
- Token pricing
- USD per 1M tokens: in $0.048 / out $0.144
- 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 | AA — next 20% of the cohort | — |
| Agents | AA — next 20% of the cohort | 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 | — |
| Image generation | No measurement | — |
| Audio | No measurement | — |
| Video | No measurement | — |
| Embeddings | No measurement | — |
Evaluations
| Source | Benchmark | Value | Grade | Data retrieved |
|---|---|---|---|---|
| Artificial Analysis | Coding index | 69.1 | AA — next 20% of the cohort | 13.11.2026 |
| Artificial Analysis | Agentic index | 41 | AA — next 20% of the cohort | 13.11.2026 |
| Design Arena | models · 3dwin rate 48.8% · rank 54 | 1,191Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · asciiartwin rate 34% · rank 64 | 1,093Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · codecategorieswin rate 46.5% · rank 58 | 1,220Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · datavizwin rate 40% · rank 72 | 1,170Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · gamedevwin rate 44.8% · rank 57 | 1,199Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · svgwin rate 44.7% · rank 42 | 1,171Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · uicomponentwin rate 47.1% · rank 51 | 1,228Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · websitewin rate 46.8% · rank 55 | 1,231Elo | BB — next 30% 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) |
|---|---|---|---|---|---|---|
| DeepInfrafp8 | 1,048,576 | $0.048 | $0.144 | — | — | — |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.