ZMIME
Comparison · 2 models · Updated Oct 4, 2026

Mistral Small 3.2 vs DeepSeek-R1

Mistral Small 3.2 comes out ahead, 60 to 51 on our weighted score, and it is the cheaper option too.

  1. Our pick

    Mistral AI

    Mistral Small 3.2

    Released Jun 20, 2025

    60/100
    • ECI131.7
    • Price$0.10 / $0.30
    • Context128K
    Full specs Remove Mistral Small 3.2

  2. DeepSeek

    DeepSeek-R1

    Released Jan 20, 2025

    51/100
    • ECI139.0
    • Price$0.70 / $2.60
    • Context128K
    Full specs Remove DeepSeek-R1

  3. Add a model

    Make it a three-way comparison.

01 — Verdict

Mistral Small 3.2 is our pick

Mistral Small 3.2 is the better all-round choice, scoring 60/100 against DeepSeek-R1 (51). It leads on price and inputs & features. DeepSeek-R1 wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityDeepSeek-R1Capabilities Index (ECI): DeepSeek-R1 139.0 · Mistral Small 3.2 131.7
  • Lowest priceMistral Small 3.2Mistral Small 3.2 $0.15 · DeepSeek-R1 $1.18 per 1M tokens (3:1 blend)
  • Longest contextAbout the sameMistral Small 3.2 128,000 · DeepSeek-R1 128,000 tokens
  • Widest inputsMistral Small 3.2Mistral Small 3.2: Text, Images · DeepSeek-R1: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightMistral Small 3.2DeepSeek-R1
CapabilityCapabilities Index (ECI)50%5564
Price25%8947
Inputs & features15%5035
Context window10%2424
Overall100%60/10051/100
02 — Side by side

Every spec in one table

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

Mistral Small 3.2 vs DeepSeek-R1 specifications side by side
SpecificationMistral Small 3.2Mistral AIDeepSeek-R1DeepSeek
Capability
Capabilities Index (ECI)131.7139.0 (best)
ECI rank#123 of 148#104 of 148 (best)
GPQA DiamondGraduate-level science questions49.1%71.7% (best)
OTIS Mock AIME 2024–2025Competition mathematics30.3%53.3% (best)
Price per million tokens
Input$0.10 (best)$0.70
Output$0.30 (best)$2.60
Cached input——
Blended (3:1)$0.15 (best)$1.18
Long-context rateSame rateSame rate
Price sourceOfficial Mistral APIMedian of 11 providers
Limits
Context window128,000 tokens128,000 tokens
Max output16,384 tokens32,768 tokens (best)
Inputs and features
TextYesYes
ImagesYesNo
PDFsNoNo
AudioNoNo
VideoNoNo
ReasoningNoYes
Tool callingYesYes
Structured outputNoNo
Availability
WeightsOpenOpen
API model IDmistral-small-2506—
API providers612 (best)
ReleasedJun 20, 2025Jan 20, 2025
Knowledge cutoffMar 2025Jul 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 Small 3.2$1.60
  • DeepSeek-R1$12.20
04 — Questions

Which should you choose?

Which is better: Mistral Small 3.2 or DeepSeek-R1?

Mistral Small 3.2 is the better all-round choice, scoring 60/100 against DeepSeek-R1 (51). It leads on price and inputs & features. DeepSeek-R1 wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Mistral Small 3.2 or DeepSeek-R1?

Mistral Small 3.2 is cheaper at $0.10 input / $0.30 output per million tokens (official Mistral API price). DeepSeek-R1 costs $0.70 input / $2.60 output per million tokens (median across 11 API providers). At a typical mix of three input tokens to one output token, that is $0.15 per million tokens for Mistral Small 3.2 versus $1.18 for DeepSeek-R1 (7.8× as much).

Which scores higher on benchmarks?

DeepSeek-R1 scores higher on the Capabilities Index (ECI): DeepSeek-R1 139.0 (#104 of 148) and Mistral Small 3.2 131.7 (#123 of 148). Their confidence ranges do not overlap (136.2–140.4 vs 126.6–133.9), so the gap is a real one. On individual benchmarks: GPQA Diamond — DeepSeek-R1 71.7%, Mistral Small 3.2 49.1%; OTIS Mock AIME 2024–2025 — DeepSeek-R1 53.3%, Mistral Small 3.2 30.3%.

Which is better for coding?

There are no published SWE-bench Verified results for Mistral Small 3.2 and DeepSeek-R1 yet, so there is no like-for-like coding score. On overall capability, DeepSeek-R1 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 Small 3.2 and DeepSeek-R1 share the same 128,000-token context window. Maximum output per response: Mistral Small 3.2 up to 16,384, DeepSeek-R1 up to 32,768 tokens.

Which can read images, PDFs, audio or video?

Mistral Small 3.2 accepts text and images; DeepSeek-R1 accepts text. Mistral Small 3.2 handles the widest range of inputs.

Are any of these open source?

Yes, both publish their weights, so you can self-host them.

Which is newer?

Mistral Small 3.2 is the newest, released Jun 20, 2025. DeepSeek-R1 came out Jan 20, 2025. Knowledge cutoff: Mistral Small 3.2 Mar 2025, DeepSeek-R1 Jul 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 Oct 4, 2026.

05 — Keep comparing

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