Mistral Large 2.1 vs DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 comes out ahead, 79 to 38 on our weighted score, and it is the cheaper option too.
Mistral AIMistral Large 2.1
38/100- ECI128.6
- Price$2.00 / $6.00
- Context131K
- Our pick
DeepSeekDeepSeek V4.1 Flash 1017
79/100- ECI158.8
- Price$0.15 / $0.60
- Context1M
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Make it a three-way comparison.
DeepSeek V4.1 Flash 1017 is our pick
DeepSeek V4.1 Flash 1017 is the better all-round choice, scoring 79/100 against Mistral Large 2.1 (38). It leads on capability, price, inputs & features and context window. 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 · Mistral Large 2.1 128.6
- Lowest priceDeepSeek V4.1 Flash 1017DeepSeek V4.1 Flash 1017 $0.263 · Mistral Large 2.1 $3.00 per 1M tokens (3:1 blend)
- Longest contextDeepSeek V4.1 Flash 1017DeepSeek V4.1 Flash 1017 1,000,000 · Mistral Large 2.1 131,072 tokens
- Widest inputsDeepSeek V4.1 Flash 1017Mistral Large 2.1: Text · DeepSeek V4.1 Flash 1017: Text, Images
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Mistral Large 2.1 | DeepSeek V4.1 Flash 1017 |
|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 50 | 87 |
| Price | 25% | 26 | 74 |
| Inputs & features | 15% | 25 | 70 |
| Context window | 10% | 25 | 60 |
| Overall | 100% | 38/100 | 79/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Mistral Large 2.1Mistral AI | DeepSeek V4.1 Flash 1017DeepSeek |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 128.6 | 158.8 (best) |
| ECI rank | #142 of 165 | #19 of 165 (best) |
| GPQA DiamondGraduate-level science questions | 51.3% | 93.2% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | — | 71.2% |
| OTIS Mock AIME 2024–2025Competition mathematics | 7.8% | 99.4% (best) |
| SimpleQA VerifiedShort factual questions | — | 42.5% |
| Price per million tokens | ||
| Input | $2.00 | $0.15 (best) |
| Output | $6.00 | $0.60 (best) |
| Cached input | — | $0.003 |
| Blended (3:1) | $3.00 | $0.263 (best) |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 2 providers | Official DeepSeek API |
| Limits | ||
| Context window | 131,072 tokens | 1,000,000 tokens (best) |
| Max output | 16,384 tokens | 384,000 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | Yes |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | No | Yeslow · high · max |
| Tool calling | Yes | Yes |
| Structured output | No | Yes |
| Availability | ||
| Weights | Open | OpenMIT |
| API model ID | — | deepseek-flash |
| API providers | 2 | 40 (best) |
| Released | Nov 18, 2024 | Oct 17, 2026 |
| Knowledge cutoff | Nov 2024 | 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.
- Mistral Large 2.1$32.00
- DeepSeek V4.1 Flash 1017$2.70
Which should you choose?
Which is better: Mistral Large 2.1 or DeepSeek V4.1 Flash 1017?
DeepSeek V4.1 Flash 1017 is the better all-round choice, scoring 79/100 against Mistral Large 2.1 (38). It leads on capability, price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Mistral Large 2.1 or DeepSeek V4.1 Flash 1017?
DeepSeek V4.1 Flash 1017 is cheaper at $0.15 input / $0.60 output per million tokens (official DeepSeek API price). Mistral Large 2.1 costs $2.00 input / $6.00 output per million tokens (median across 2 API providers). At a typical mix of three input tokens to one output token, that is $0.263 per million tokens for DeepSeek V4.1 Flash 1017 versus $3.00 for Mistral Large 2.1 (11× 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 Mistral Large 2.1 128.6 (#142 of 165). Their confidence ranges do not overlap (156.7–162.2 vs 124.3–130.7), so the gap is a real one. On individual benchmarks: GPQA Diamond — DeepSeek V4.1 Flash 1017 93.2%, Mistral Large 2.1 51.3%; OTIS Mock AIME 2024–2025 — DeepSeek V4.1 Flash 1017 99.4%, Mistral Large 2.1 7.8%.
Which is better for coding?
There are no published SWE-bench Verified results for Mistral Large 2.1 and DeepSeek V4.1 Flash 1017 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 has the largest context window at 1,000,000 tokens, against 131,072 for Mistral Large 2.1. Maximum output per response: Mistral Large 2.1 up to 16,384, DeepSeek V4.1 Flash 1017 up to 384,000 tokens.
Which can read images, PDFs, audio or video?
Mistral Large 2.1 accepts text; DeepSeek V4.1 Flash 1017 accepts text and images. 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. Mistral Large 2.1 came out Nov 18, 2024. Knowledge cutoff: Mistral Large 2.1 Nov 2024, DeepSeek V4.1 Flash 1017 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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