ZMIME
Comparison · 2 models · Updated Oct 4, 2026

Kimi K2 Thinking vs Mistral Small 3.1 24B

Too close to call on our weighted score (Kimi K2 Thinking 58, Mistral Small 3.1 24B 55). The right pick depends on what you value most.

  1. Moonshot AI

    Kimi K2 Thinking

    Released Nov 6, 2025

    58/100
    • ECI146.0
    • Price$0.60 / $2.50
    • Context262K
    Full specs Remove Kimi K2 Thinking

  2. Mistral AI

    Mistral Small 3.1 24B

    Released Mar 17, 2025

    55/100
    • ECI127.5
    • Price$0.229 / $0.436
    • Context128K
    Full specs Remove Mistral Small 3.1 24B

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01 — Verdict

Too close to call

It is close. Our weighted score puts them within 2 points (Kimi K2 Thinking 58/100, Mistral Small 3.1 24B 55/100), so choose by what matters most for your work: Kimi K2 Thinking for raw capability and Mistral Small 3.1 24B on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityKimi K2 ThinkingCapabilities Index (ECI): Kimi K2 Thinking 146.0 · Mistral Small 3.1 24B 127.5
  • Lowest priceMistral Small 3.1 24BMistral Small 3.1 24B $0.281 · Kimi K2 Thinking $1.07 per 1M tokens (3:1 blend)
  • Longest contextKimi K2 ThinkingKimi K2 Thinking 262,144 · Mistral Small 3.1 24B 128,000 tokens
  • Widest inputsMistral Small 3.1 24BKimi K2 Thinking: Text · Mistral Small 3.1 24B: Text, Images
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightKimi K2 ThinkingMistral Small 3.1 24B
CapabilityCapabilities Index (ECI)50%7350
Price25%4876
Inputs & features15%3560
Context window10%3724
Overall100%58/10055/100
02 — Side by side

Every spec in one table

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

Kimi K2 Thinking vs Mistral Small 3.1 24B specifications side by side
SpecificationKimi K2 ThinkingMoonshot AIMistral Small 3.1 24BMistral AI
Capability
Capabilities Index (ECI)146.0 (best)127.5
ECI rank#72 of 148 (best)#132 of 148
GPQA DiamondGraduate-level science questions84.2% (best)47.5%
OTIS Mock AIME 2024–2025Competition mathematics83.1% (best)5.8%
Price per million tokens
Input$0.60$0.229 (best)
Output$2.50$0.436 (best)
Cached input——
Blended (3:1)$1.07$0.281 (best)
Long-context rateSame rateSame rate
Price sourceMedian of 10 providersMedian of 2 providers
Limits
Context window262,144 tokens (best)128,000 tokens
Max output262,144 tokens (best)16,384 tokens
Inputs and features
TextYesYes
ImagesNoYes
PDFsNoNo
AudioNoNo
VideoNoNo
ReasoningYesNo
Tool callingYesYes
Structured outputNoYes
Availability
WeightsOpenOpen
API model ID——
API providers10 (best)2
ReleasedNov 6, 2025Mar 17, 2025
Knowledge cutoffAug 2024Jun 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.

  • Kimi K2 Thinking$11.00
  • Mistral Small 3.1 24B$3.16
04 — Questions

Which should you choose?

Which is better: Kimi K2 Thinking or Mistral Small 3.1 24B?

It is close. Our weighted score puts them within 2 points (Kimi K2 Thinking 58/100, Mistral Small 3.1 24B 55/100), so choose by what matters most for your work: Kimi K2 Thinking for raw capability and Mistral Small 3.1 24B on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Kimi K2 Thinking or Mistral Small 3.1 24B?

Mistral Small 3.1 24B is cheaper at $0.229 input / $0.436 output per million tokens (median across 2 API providers). Kimi K2 Thinking costs $0.60 input / $2.50 output per million tokens (median across 10 API providers). At a typical mix of three input tokens to one output token, that is $0.281 per million tokens for Mistral Small 3.1 24B versus $1.07 for Kimi K2 Thinking (3.8× as much).

Which scores higher on benchmarks?

Kimi K2 Thinking scores higher on the Capabilities Index (ECI): Kimi K2 Thinking 146.0 (#72 of 148) and Mistral Small 3.1 24B 127.5 (#132 of 148). Their confidence ranges do not overlap (143.4–147.6 vs 122.6–129.4), so the gap is a real one. On individual benchmarks: GPQA Diamond — Kimi K2 Thinking 84.2%, Mistral Small 3.1 24B 47.5%; OTIS Mock AIME 2024–2025 — Kimi K2 Thinking 83.1%, Mistral Small 3.1 24B 5.8%.

Which is better for coding?

There are no published SWE-bench Verified results for Kimi K2 Thinking and Mistral Small 3.1 24B yet, so there is no like-for-like coding score. On overall capability, Kimi K2 Thinking 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?

Kimi K2 Thinking has the largest context window at 262,144 tokens, against 128,000 for Mistral Small 3.1 24B. Maximum output per response: Kimi K2 Thinking up to 262,144, Mistral Small 3.1 24B up to 16,384 tokens.

Which can read images, PDFs, audio or video?

Kimi K2 Thinking accepts text; Mistral Small 3.1 24B accepts text and images. Mistral Small 3.1 24B 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?

Kimi K2 Thinking is the newest, released Nov 6, 2025. Mistral Small 3.1 24B came out Mar 17, 2025. Knowledge cutoff: Kimi K2 Thinking Aug 2024, Mistral Small 3.1 24B Jun 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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