ZMIME
Comparison · 2 models · Updated Oct 4, 2026

Qwen3.5 9B vs Mistral Small 3.2

Qwen3.5 9B comes out ahead, 72 to 60 on our weighted score, and it is the cheaper option too.

  1. Our pick

    Alibaba (Qwen)

    Qwen3.5 9B

    Released Feb 23, 2026

    72/100
    • ECI139.5
    • Price$0.10 / $0.15
    • Context262K
    Full specs Remove Qwen3.5 9B

  2. 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

Qwen3.5 9B is our pick

Qwen3.5 9B is the better all-round choice, scoring 72/100 against Mistral Small 3.2 (60). It leads on capability, price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityQwen3.5 9BCapabilities Index (ECI): Qwen3.5 9B 139.5 · Mistral Small 3.2 131.7
  • Lowest priceQwen3.5 9BQwen3.5 9B $0.113 · Mistral Small 3.2 $0.15 per 1M tokens (3:1 blend)
  • Longest contextQwen3.5 9BQwen3.5 9B 262,144 · Mistral Small 3.2 128,000 tokens
  • Widest inputsQwen3.5 9BQwen3.5 9B: Text, Images, Video · 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
MeasureWeightQwen3.5 9BMistral Small 3.2
CapabilityCapabilities Index (ECI)50%6555
Price25%9589
Inputs & features15%8050
Context window10%3724
Overall100%72/10060/100
02 — Side by side

Every spec in one table

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

Qwen3.5 9B vs Mistral Small 3.2 specifications side by side
SpecificationQwen3.5 9BAlibaba (Qwen)Mistral Small 3.2Mistral AI
Capability
Capabilities Index (ECI)139.5 (best)131.7
ECI rank#101 of 148 (best)#123 of 148
GPQA DiamondGraduate-level science questions79.0% (best)49.1%
OTIS Mock AIME 2024–2025Competition mathematics61.7% (best)30.3%
Price per million tokens
Input$0.10$0.10
Output$0.15 (best)$0.30
Cached input——
Blended (3:1)$0.113 (best)$0.15
Long-context rateSame rateSame rate
Price sourceMedian of 14 providersOfficial Mistral API
Limits
Context window262,144 tokens (best)128,000 tokens
Max output65,536 tokens (best)16,384 tokens
Inputs and features
TextYesYes
ImagesYesYes
PDFsNoNo
AudioNoNo
VideoYesNo
ReasoningYesNo
Tool callingYesYes
Structured outputYesNo
Availability
WeightsOpenOpen
API model ID—mistral-small-2506
API providers15 (best)6
ReleasedFeb 23, 2026Jun 20, 2025
Knowledge cutoff—Mar 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.

  • Qwen3.5 9B$1.30
  • Mistral Small 3.2$1.60
04 — Questions

Which should you choose?

Which is better: Qwen3.5 9B or Mistral Small 3.2?

Qwen3.5 9B is the better all-round choice, scoring 72/100 against Mistral Small 3.2 (60). 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, Qwen3.5 9B or Mistral Small 3.2?

Qwen3.5 9B is cheaper at $0.10 input / $0.15 output per million tokens (median across 14 API providers). Mistral Small 3.2 costs $0.10 input / $0.30 output per million tokens (official Mistral API price). At a typical mix of three input tokens to one output token, that is $0.113 per million tokens for Qwen3.5 9B versus $0.15 for Mistral Small 3.2 (1.3× as much).

Which scores higher on benchmarks?

Qwen3.5 9B scores higher on the Capabilities Index (ECI): Qwen3.5 9B 139.5 (#101 of 148) and Mistral Small 3.2 131.7 (#123 of 148). Their confidence ranges do not overlap (136.5–141.3 vs 126.6–133.9), so the gap is a real one. On individual benchmarks: GPQA Diamond — Qwen3.5 9B 79.0%, Mistral Small 3.2 49.1%; OTIS Mock AIME 2024–2025 — Qwen3.5 9B 61.7%, Mistral Small 3.2 30.3%.

Which is better for coding?

There are no published SWE-bench Verified results for Qwen3.5 9B and Mistral Small 3.2 yet, so there is no like-for-like coding score. On overall capability, Qwen3.5 9B 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?

Qwen3.5 9B has the largest context window at 262,144 tokens, against 128,000 for Mistral Small 3.2. Maximum output per response: Qwen3.5 9B up to 65,536, Mistral Small 3.2 up to 16,384 tokens.

Which can read images, PDFs, audio or video?

Qwen3.5 9B accepts text, images and video; Mistral Small 3.2 accepts text and images. Qwen3.5 9B 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?

Qwen3.5 9B is the newest, released Feb 23, 2026. Mistral Small 3.2 came out Jun 20, 2025. Knowledge cutoff: 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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