DeepSeek V4 Flash 0731 vs DeepSeek V4.1 FlashWhich Is Better in 2026?
DeepSeek V4 Flash 0731 vs DeepSeek V4.1 Flash: which should you choose in 2026?
DeepSeek V4 Flash 0731 (by DeepSeek) and DeepSeek V4.1 Flash (by DeepSeek) are compared below. Here is how they stack up on benchmarks, price, and capabilities, and which one to pick in 2026.
There are not enough shared, protocol-compatible benchmark results to declare a performance leader.
At least one model uses context-dependent token-pricing tiers, so the published base rates do not support an unconditional blended price comparison.
DeepSeek V4 Flash 0731 has the larger context window (1,310,720 tokens vs 1,048,576 tokens).
Choose DeepSeek V4 Flash 0731 if…
- • you work with longer documents, transcripts, or codebases
Choose DeepSeek V4.1 Flash if…
- • you need image inputs
The benchmark count includes only results measured with a matching benchmark version and protocol. Arena scores are shown separately. Missing, preliminary, and incompatible data is not treated as a controlled win. Published point-score comparisons are labeled separately when protocol details are incomplete. For text-output models, the verdict also compares token pricing and context windows.
Performance benchmarks
Every value links to its source. A dash means that no reviewed result is available for that exact model and protocol.
| Benchmark | DeepSeek V4 Flash 0731 | DeepSeek V4.1 Flash |
|---|---|---|
A 657-task benchmark of multi-step work across simulated SaaS applications in six business domains. The Artificial Analysis protocol reports a guardrail-aware score and is distinct from both the public Zapier split and the unrelated dynamic AutoBench framework. Guardrail-aware score | — | — |
A benchmark of difficult, verifiable information-seeking questions designed to measure an agent's ability to locate hard-to-find facts through web browsing. Accuracy | — | — |
A verified computer-use benchmark in which multimodal agents operate desktop applications and are graded from the resulting environment state. Mean task reward | — | — |
A human-validated subset of real GitHub issues used to measure whether a coding agent can produce repository patches that resolve the associated tests. Resolved | — | — |
Version 2.1 of the benchmark for completing realistic tasks in terminal environments. Harness, resource limits, and attempt count are part of the protocol. Mean task success | — | — |
A dynamic LLM evaluation framework in which models generate questions, answer them, and participate in reciprocal peer assessment. AutoBench is distinct from Zapier's AutomationBench. Weighted peer-assessment score | — | — |
The highest-quality subset of Graduate-Level Google-Proof Q&A, designed to test expert-level scientific reasoning in biology, physics, and chemistry. Accuracy | 90.8% Artificial Analysis | — |
A 2,500-question expert-level benchmark spanning dozens of academic fields. Tool-assisted and no-tools results are separate protocols and must not be merged. Accuracy | 38.6% Artificial Analysis | — |
A contamination-resistant benchmark refreshed on a fixed release cadence. Scores from different LiveBench releases must never be compared as the same protocol. Mean of category averages | 74.2% LiveBench | — |
Benchmark sources
- GPQA Diamond Benchmark Leaderboard
- Humanity's Last Exam Benchmark Leaderboard
- LiveBench 2026-06-25 Leaderboard
Reviewed evidence last updated 2026-08-09.
Pricing, capabilities, and model facts
| Feature | DeepSeek V4 Flash 0731 | DeepSeek V4.1 Flash |
|---|---|---|
| Context & model facts | ||
| Developer | DeepSeek | DeepSeek |
| API provider | DeepSeek | DeepSeek |
| Input context | 1,310,720 tokens | 1,048,576 tokens |
| Maximum output | 943,718 tokens | 384,000 tokens |
| Released | — | — |
| Added to Writingmate | Jul 31, 2026 | Sep 10, 2026 |
| License | Not available | Not available |
| Knowledge cutoff | — | — |
| Capabilities | ||
| Inputs | Text | Text, Image |
| Outputs | Text | Text |
| Provider endpoint accepts tool parameters | Yes | Yes |
| Reasoning | Yes | Yes |
| Vision | No | Yes |
| Image Generation | No | No |
| Video Generation | No | No |
| API pricing | ||
| Base input (per 1M tokens) | $0.065 | $0.30 |
| Base output (per 1M tokens) | $0.18 | $1.20 |
| Higher-context pricing tiers | Base rate only | Base rate only |
| Price-comparison caveat | At least one model changes token rates above a prompt-token threshold. Base rates are shown, but an unconditional blended price ratio would not be like-for-like. | |
| API performance | ||
| p95 latency | Not measured | Not measured |
| Output throughput | Not measured | Not measured |
| Writingmate shows API performance only when both models have enough observations from the same measurement window, prompt profile, and provider. Third-party latency values are not copied into this table. | ||
Data sources
- Writingmate model catalog (pricing, limits, and availability)
Catalog data last updated Sep 10, 2026.
DeepSeek V4 Flash 0731 vs DeepSeek V4.1 Flash FAQ
Which is better, DeepSeek V4 Flash 0731 or DeepSeek V4.1 Flash?
There are not enough shared, protocol-compatible benchmark results to declare a performance leader. At least one model uses context-dependent token-pricing tiers, so the published base rates do not support an unconditional blended price comparison. DeepSeek V4 Flash 0731 has the larger context window (1,310,720 tokens vs 1,048,576 tokens).
Which model is cheaper to use through an API?
At least one model uses context-dependent token-pricing tiers, so the published base rates do not support an unconditional blended price comparison.
Which model supports more context?
DeepSeek V4 Flash 0731 has the larger context window (1,310,720 tokens vs 1,048,576 tokens).
Can I switch between DeepSeek V4 Flash 0731 and DeepSeek V4.1 Flash?
Yes. Use the model selector on this page to open any current Writingmate model comparison. You can also run the same prompt with both models in Writingmate.
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