ZMIME
Comparison · 2 models · Updated Oct 4, 2026

DeepSeek-R1 vs Mistral Small 3.2

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

  1. DeepSeek

    DeepSeek-R1

    Released Jan 20, 2025

    51/100
    • ECI139.0
    • Price$0.70 / $2.60
    • Context128K
    Full specs Remove DeepSeek-R1
  2. 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

  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 sameDeepSeek-R1 128,000 · Mistral Small 3.2 128,000 tokens
  • Widest inputsMistral Small 3.2DeepSeek-R1: Text · Mistral Small 3.2: Text, Images
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightDeepSeek-R1Mistral Small 3.2
CapabilityCapabilities Index (ECI)50%6455
Price25%4789
Inputs & features15%3550
Context window10%2424
Overall100%51/10060/100
02 — Side by side

Every spec in one table

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

DeepSeek-R1 vs Mistral Small 3.2 specifications side by side
SpecificationDeepSeek-R1DeepSeekMistral Small 3.2Mistral AI
Capability
Capabilities Index (ECI)139.0 (best)131.7
ECI rank#104 of 148 (best)#123 of 148
GPQA DiamondGraduate-level science questions71.7% (best)49.1%
OTIS Mock AIME 2024–2025Competition mathematics53.3% (best)30.3%
Price per million tokens
Input$0.70$0.10 (best)
Output$2.60$0.30 (best)
Cached input——
Blended (3:1)$1.18$0.15 (best)
Long-context rateSame rateSame rate
Price sourceMedian of 11 providersOfficial Mistral API
Limits
Context window128,000 tokens128,000 tokens
Max output32,768 tokens (best)16,384 tokens
Inputs and features
TextYesYes
ImagesNoYes
PDFsNoNo
AudioNoNo
VideoNoNo
ReasoningYesNo
Tool callingYesYes
Structured outputNoNo
Availability
WeightsOpenOpen
API model ID—mistral-small-2506
API providers12 (best)6
ReleasedJan 20, 2025Jun 20, 2025
Knowledge cutoffJul 2024Mar 2025
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.

  • DeepSeek-R1$12.20
  • Mistral Small 3.2$1.60
04 — Questions

Which should you choose?

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

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, DeepSeek-R1 or Mistral Small 3.2?

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 DeepSeek-R1 and Mistral Small 3.2 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?

DeepSeek-R1 and Mistral Small 3.2 share the same 128,000-token context window. Maximum output per response: DeepSeek-R1 up to 32,768, Mistral Small 3.2 up to 16,384 tokens.

Which can read images, PDFs, audio or video?

DeepSeek-R1 accepts text; Mistral Small 3.2 accepts text and images. 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: DeepSeek-R1 Jul 2024, Mistral Small 3.2 Mar 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 Oct 4, 2026.

05 — Keep comparing

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