DeepSeek V4.1 Flash 1017 vs DeepSeek V4 Flash 0731
DeepSeek V4.1 Flash 1017 comes out ahead, 79 to 73 on our weighted score, though DeepSeek V4 Flash 0731 is 33% cheaper per token.
- Our pick
DeepSeekDeepSeek V4.1 Flash 1017
79/100- ECI158.8
- Price$0.15 / $0.60
- Context1M
- DeepSeek
DeepSeek V4 Flash 0731
73/100- ECI154.9
- Price$0.14 / $0.28
- Context1M
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DeepSeek V4.1 Flash 1017 is our pick
DeepSeek V4.1 Flash 1017 is the better all-round choice, scoring 79/100 against DeepSeek V4 Flash 0731 (73). It leads on capability and inputs & features. DeepSeek V4 Flash 0731 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityDeepSeek V4.1 Flash 1017Capabilities Index (ECI): DeepSeek V4.1 Flash 1017 158.8 · DeepSeek V4 Flash 0731 154.9
- Lowest priceDeepSeek V4 Flash 0731DeepSeek V4 Flash 0731 $0.175 · DeepSeek V4.1 Flash 1017 $0.263 per 1M tokens (3:1 blend)
- Longest contextAbout the sameDeepSeek V4.1 Flash 1017 1,000,000 · DeepSeek V4 Flash 0731 1,000,000 tokens
- Widest inputsDeepSeek V4.1 Flash 1017DeepSeek V4.1 Flash 1017: Text, Images · DeepSeek V4 Flash 0731: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | DeepSeek V4.1 Flash 1017 | DeepSeek V4 Flash 0731 |
|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 87 | 83 |
| Price | 25% | 74 | 82 |
| Inputs & features | 15% | 70 | 35 |
| Context window | 10% | 60 | 60 |
| Overall | 100% | 79/100 | 73/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | DeepSeek V4.1 Flash 1017DeepSeek | DeepSeek V4 Flash 0731DeepSeek |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 158.8 (best) | 154.9 |
| ECI rank | #19 of 165 (best) | #41 of 165 |
| GPQA DiamondGraduate-level science questions | 93.2% (best) | 91.0% |
| FrontierMath Tiers 1–3Research-level mathematics | 71.2% (best) | 57.5% |
| OTIS Mock AIME 2024–2025Competition mathematics | 99.4% (best) | 94.4% |
| SimpleQA VerifiedShort factual questions | 42.5% (best) | 33.6% |
| Price per million tokens | ||
| Input | $0.15 | $0.14 (best) |
| Output | $0.60 | $0.28 (best) |
| Cached input | $0.003 | — |
| Blended (3:1) | $0.263 | $0.175 (best) |
| Long-context rate | Same rate | Same rate |
| Price source | Official DeepSeek API | Median of 51 providers |
| Limits | ||
| Context window | 1,000,000 tokens | 1,000,000 tokens |
| Max output | 384,000 tokens | 384,000 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yeslow · high · max | Yes |
| Tool calling | Yes | Yes |
| Structured output | Yes | No |
| Availability | ||
| Weights | OpenMIT | OpenMIT |
| API model ID | deepseek-flash | — |
| API providers | 40 | 52 (best) |
| Released | Oct 17, 2026 | Jul 31, 2026 |
| Knowledge cutoff | May 2025 | May 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- DeepSeek V4.1 Flash 1017$2.70
- DeepSeek V4 Flash 0731$1.96
Which should you choose?
Which is better: DeepSeek V4.1 Flash 1017 or DeepSeek V4 Flash 0731?
DeepSeek V4.1 Flash 1017 is the better all-round choice, scoring 79/100 against DeepSeek V4 Flash 0731 (73). It leads on capability and inputs & features. DeepSeek V4 Flash 0731 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, DeepSeek V4.1 Flash 1017 or DeepSeek V4 Flash 0731?
DeepSeek V4 Flash 0731 is cheaper at $0.14 input / $0.28 output per million tokens (median across 51 API providers). DeepSeek V4.1 Flash 1017 costs $0.15 input / $0.60 output per million tokens (official DeepSeek API price). At a typical mix of three input tokens to one output token, that is $0.175 per million tokens for DeepSeek V4 Flash 0731 versus $0.263 for DeepSeek V4.1 Flash 1017 (1.5× as much).
Which scores higher on benchmarks?
DeepSeek V4.1 Flash 1017 scores higher on the Capabilities Index (ECI): DeepSeek V4.1 Flash 1017 158.8 (#19 of 165) and DeepSeek V4 Flash 0731 154.9 (#41 of 165). The confidence ranges of the top two overlap (156.7–162.2 vs 152.6–156.8), so treat the gap as small. On individual benchmarks: GPQA Diamond — DeepSeek V4.1 Flash 1017 93.2%, DeepSeek V4 Flash 0731 91.0%; FrontierMath Tiers 1–3 — DeepSeek V4.1 Flash 1017 71.2%, DeepSeek V4 Flash 0731 57.5%; OTIS Mock AIME 2024–2025 — DeepSeek V4.1 Flash 1017 99.4%, DeepSeek V4 Flash 0731 94.4%; SimpleQA Verified — DeepSeek V4.1 Flash 1017 42.5%, DeepSeek V4 Flash 0731 33.6%.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek V4.1 Flash 1017 and DeepSeek V4 Flash 0731 yet, so there is no like-for-like coding score. On overall capability, DeepSeek V4.1 Flash 1017 leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.
Which has the bigger context window?
DeepSeek V4.1 Flash 1017 and DeepSeek V4 Flash 0731 share the same 1,000,000-token context window. Maximum output per response: DeepSeek V4.1 Flash 1017 up to 384,000, DeepSeek V4 Flash 0731 up to 384,000 tokens.
Which can read images, PDFs, audio or video?
DeepSeek V4.1 Flash 1017 accepts text and images; DeepSeek V4 Flash 0731 accepts text. DeepSeek V4.1 Flash 1017 handles the widest range of inputs.
Are any of these open source?
Yes, both publish their weights (MIT), so you can self-host them.
Which is newer?
DeepSeek V4.1 Flash 1017 is the newest, released Oct 17, 2026. DeepSeek V4 Flash 0731 came out Jul 31, 2026. Knowledge cutoff: DeepSeek V4.1 Flash 1017 May 2025, DeepSeek V4 Flash 0731 May 2025.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Nov 2, 2026.
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