ZMIME
Comparison · 3 models · Updated Oct 4, 2026

Qwen3.5 9B vs Mistral Small 3.2 vs GPT-5 Nano

Too close to call on our weighted score (Qwen3.5 9B 72, GPT-5 Nano 70, Mistral Small 3.2 60). The right pick depends on what you value most.

  1. 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. OpenAI

    GPT-5 Nano

    Released Aug 7, 2025

    70/100
    • ECI139.4
    • Price$0.05 / $0.40
    • Context400K
    Full specs Remove GPT-5 Nano
01 — Verdict

Too close to call

It is close. Our weighted score puts them within 2 points (Qwen3.5 9B 72/100, GPT-5 Nano 70/100, Mistral Small 3.2 60/100), so choose by what matters most for your work: Qwen3.5 9B for raw capability and GPT-5 Nano for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityQwen3.5 9BCapabilities Index (ECI): Qwen3.5 9B 139.5 · GPT-5 Nano 139.4 · Mistral Small 3.2 131.7
  • Lowest priceQwen3.5 9BQwen3.5 9B $0.113 · GPT-5 Nano $0.138 · Mistral Small 3.2 $0.15 per 1M tokens (3:1 blend)
  • Longest contextGPT-5 NanoGPT-5 Nano 400,000 · Qwen3.5 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 · GPT-5 Nano: Text, Images
  • Self-hostingQwen3.5 9B and Mistral Small 3.2Publishes downloadable weights
How the score is built
MeasureWeightQwen3.5 9BMistral Small 3.2GPT-5 Nano
CapabilityCapabilities Index (ECI)50%655565
Price25%958991
Inputs & features15%805070
Context window10%372444
Overall100%72/10060/10070/100
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 vs GPT-5 Nano specifications side by side
SpecificationQwen3.5 9BAlibaba (Qwen)Mistral Small 3.2Mistral AIGPT-5 NanoOpenAI
Capability
Capabilities Index (ECI)139.5 (best)131.7139.4
ECI rank#101 of 148 (best)#123 of 148#102 of 148
GPQA DiamondGraduate-level science questions79.0% (best)49.1%69.4%
FrontierMath Tiers 1–3Research-level mathematics——20.0%
OTIS Mock AIME 2024–2025Competition mathematics61.7%30.3%81.1% (best)
SimpleQA VerifiedShort factual questions——11.7%
Price per million tokens
Input$0.10$0.10$0.05 (best)
Output$0.15 (best)$0.30$0.40
Cached input——$0.005
Blended (3:1)$0.113 (best)$0.15$0.138
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 14 providersOfficial Mistral APIOfficial OpenAI API
Limits
Context window262,144 tokens128,000 tokens400,000 tokens (best)
Max output65,536 tokens16,384 tokens128,000 tokens (best)
Inputs and features
TextYesYesYes
ImagesYesYesYes
PDFsNoNoNo
AudioNoNoNo
VideoYesNoNo
ReasoningYesNoYesminimal · low · medium · high
Tool callingYesYesYes
Structured outputYesNoYes
Availability
WeightsOpenOpenProprietary
API model ID—mistral-small-2506gpt-5-nano
API providers15621 (best)
ReleasedFeb 23, 2026Jun 20, 2025Aug 7, 2025
Knowledge cutoff—Mar 2025May 30, 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.

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

Which should you choose?

Which is better: Qwen3.5 9B, Mistral Small 3.2 or GPT-5 Nano?

It is close. Our weighted score puts them within 2 points (Qwen3.5 9B 72/100, GPT-5 Nano 70/100, Mistral Small 3.2 60/100), so choose by what matters most for your work: Qwen3.5 9B for raw capability and GPT-5 Nano for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Qwen3.5 9B, Mistral Small 3.2 or GPT-5 Nano?

Qwen3.5 9B is cheaper at $0.10 input / $0.15 output per million tokens (median across 14 API providers). GPT-5 Nano costs $0.05 input / $0.40 output per million tokens (official OpenAI API price); 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 $0.138 for GPT-5 Nano (1.2× as much) and $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), GPT-5 Nano 139.4 (#102 of 148) and Mistral Small 3.2 131.7 (#123 of 148). The confidence ranges of the top two overlap (136.5–141.3 vs 134.9–141.7), so treat the gap as small. On individual benchmarks: GPQA Diamond — Qwen3.5 9B 79.0%, GPT-5 Nano 69.4%, Mistral Small 3.2 49.1%; OTIS Mock AIME 2024–2025 — GPT-5 Nano 81.1%, 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, Mistral Small 3.2 and GPT-5 Nano 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. All three support tool calling for agent workflows.

Which has the bigger context window?

GPT-5 Nano has the largest context window at 400,000 tokens, against 262,144 for Qwen3.5 9B and 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, GPT-5 Nano up to 128,000 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; GPT-5 Nano accepts text and images. Qwen3.5 9B handles the widest range of inputs.

Are any of these open source?

Qwen3.5 9B and Mistral Small 3.2 publishes its weights and can be self-hosted; GPT-5 Nano is proprietary.

Which is newer?

Qwen3.5 9B is the newest, released Feb 23, 2026. GPT-5 Nano came out Aug 7, 2025; Mistral Small 3.2 came out Jun 20, 2025. Knowledge cutoff: Mistral Small 3.2 Mar 2025, GPT-5 Nano May 30, 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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