ZMIME
Comparison · 2 models · Updated Nov 2, 2026

Mistral Large 2.1 vs DeepSeek-V3

DeepSeek-V3 comes out ahead, 49 to 38 on our weighted score, and it is the cheaper option too.

  1. Mistral AI

    Mistral Large 2.1

    Released Nov 18, 2024

    38/100
    • ECI128.6
    • Price$2.00 / $6.00
    • Context131K
    Full specs Remove Mistral Large 2.1
  2. Our pick

    DeepSeek

    DeepSeek-V3

    Released Dec 26, 2024

    49/100
    • ECI132.3
    • Price$0.32 / $1.10
    • Context131K
    Full specs Remove DeepSeek-V3

  3. Add a model

    Make it a three-way comparison.

01 — Verdict

DeepSeek-V3 is our pick

DeepSeek-V3 is the better all-round choice, scoring 49/100 against Mistral Large 2.1 (38). It leads on capability and price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityDeepSeek-V3Capabilities Index (ECI): DeepSeek-V3 132.3 · Mistral Large 2.1 128.6
  • Lowest priceDeepSeek-V3DeepSeek-V3 $0.515 · Mistral Large 2.1 $3.00 per 1M tokens (3:1 blend)
  • Longest contextAbout the sameMistral Large 2.1 131,072 · DeepSeek-V3 131,072 tokens
  • Widest inputsSame inputsMistral Large 2.1: Text · DeepSeek-V3: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightMistral Large 2.1DeepSeek-V3
CapabilityCapabilities Index (ECI)50%5055
Price25%2661
Inputs & features15%2525
Context window10%2525
Overall100%38/10049/100
02 — Side by side

Every spec in one table

Highlighted cells lead their row. Dashes mean the data is not published.

Mistral Large 2.1 vs DeepSeek-V3 specifications side by side
SpecificationMistral Large 2.1Mistral AIDeepSeek-V3DeepSeek
Capability
Capabilities Index (ECI)128.6132.3 (best)
ECI rank#142 of 165#136 of 165 (best)
GPQA DiamondGraduate-level science questions51.3%56.5% (best)
OTIS Mock AIME 2024–2025Competition mathematics7.8%15.8% (best)
Price per million tokens
Input$2.00$0.32 (best)
Output$6.00$1.10 (best)
Cached input——
Blended (3:1)$3.00$0.515 (best)
Long-context rateSame rateSame rate
Price sourceMedian of 2 providersMedian of 8 providers
Limits
Context window131,072 tokens131,072 tokens
Max output16,384 tokens (best)8,192 tokens
Inputs and features
TextYesYes
ImagesNoNo
PDFsNoNo
AudioNoNo
VideoNoNo
ReasoningNoNo
Tool callingYesYes
Structured outputNoNo
Availability
WeightsOpenOpenDeepSeek Model License
API model ID——
API providers28 (best)
ReleasedNov 18, 2024Dec 26, 2024
Knowledge cutoffNov 2024—
03 — Cost

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-V3$5.40
04 — Questions

Which should you choose?

Which is better: Mistral Large 2.1 or DeepSeek-V3?

DeepSeek-V3 is the better all-round choice, scoring 49/100 against Mistral Large 2.1 (38). It leads on capability and price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Mistral Large 2.1 or DeepSeek-V3?

DeepSeek-V3 is cheaper at $0.32 input / $1.10 output per million tokens (median across 8 API providers). 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 $0.515 per million tokens for DeepSeek-V3 versus $3.00 for Mistral Large 2.1 (5.8× as much).

Which scores higher on benchmarks?

DeepSeek-V3 scores higher on the Capabilities Index (ECI): DeepSeek-V3 132.3 (#136 of 165) and Mistral Large 2.1 128.6 (#142 of 165). The confidence ranges of the top two overlap (127.5–135.5 vs 124.3–130.7), so treat the gap as small. On individual benchmarks: GPQA Diamond — DeepSeek-V3 56.5%, Mistral Large 2.1 51.3%; OTIS Mock AIME 2024–2025 — DeepSeek-V3 15.8%, 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-V3 yet, so there is no like-for-like coding score. On overall capability, DeepSeek-V3 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?

Mistral Large 2.1 and DeepSeek-V3 share the same 131,072-token context window. Maximum output per response: Mistral Large 2.1 up to 16,384, DeepSeek-V3 up to 8,192 tokens.

Which can read images, PDFs, audio or video?

Mistral Large 2.1 accepts text; DeepSeek-V3 accepts text. They handle the same number of input types.

Are any of these open source?

Yes, both publish their weights (DeepSeek Model License), so you can self-host them.

Which is newer?

DeepSeek-V3 is the newest, released Dec 26, 2024. Mistral Large 2.1 came out Nov 18, 2024. Knowledge cutoff: Mistral Large 2.1 Nov 2024.

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.

05 — Keep comparing

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