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Comparison · 2 models · Updated Nov 2, 2026

Qwen2.5-Coder-32B-Instruct vs Mistral Large

Mistral Large comes out ahead, 45 to 39 on our weighted score.

  1. Alibaba (Qwen)

    Qwen2.5-Coder-32B-Instruct

    Released Nov 12, 2024

    39/100
    • ECI—
    • Price$0.66 / $1.00
    • Context131K
    Full specs Remove Qwen2.5-Coder-32B-Instruct
  2. Our pick

    Mistral AI

    Mistral Large

    Released Feb 26, 2024

    45/100
    • ECI122.0
    • Price$0.50 / $1.50
    • Context128K
    Full specs Remove Mistral Large

  3. Add a model

    Make it a three-way comparison.

01 — Verdict

Mistral Large is our pick

Mistral Large is the better all-round choice, scoring 45/100 against Qwen2.5-Coder-32B-Instruct (39). It leads on inputs & features. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.

  • CapabilityNot enough dataNo independent benchmark covers every model here yet
  • Lowest priceQwen2.5-Coder-32B-InstructQwen2.5-Coder-32B-Instruct $0.745 · Mistral Large $0.75 per 1M tokens (3:1 blend)
  • Longest contextQwen2.5-Coder-32B-InstructQwen2.5-Coder-32B-Instruct 131,072 · Mistral Large 128,000 tokens
  • Widest inputsMistral LargeQwen2.5-Coder-32B-Instruct: Text · Mistral Large: Text, PDFs
  • Self-hostingQwen2.5-Coder-32B-InstructPublishes downloadable weights
How the score is built
MeasureWeightQwen2.5-Coder-32B-InstructMistral Large
Price50%5453
Inputs & features30%2545
Context window20%2524
Overall100%39/10045/100

Left out because at least one model lacks the data: capability. The remaining weights were rescaled.

02 — Side by side

Every spec in one table

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

Qwen2.5-Coder-32B-Instruct vs Mistral Large specifications side by side
SpecificationQwen2.5-Coder-32B-InstructAlibaba (Qwen)Mistral LargeMistral AI
Capability
Capabilities Index (ECI)—122.0
ECI rank—#153 of 165
GPQA DiamondGraduate-level science questions—38.8%
OTIS Mock AIME 2024–2025Competition mathematics—1.9%
Price per million tokens
Input$0.66$0.50 (best)
Output$1.00 (best)$1.50
Cached input—$0.05
Blended (3:1)$0.745 (best)$0.75
Long-context rateSame rateSame rate
Price sourceMedian of 7 providersOfficial Mistral API
Limits
Context window131,072 tokens (best)128,000 tokens
Max output8,192 tokens102,400 tokens (best)
Inputs and features
TextYesYes
ImagesNoNo
PDFsNoYes
AudioNoNo
VideoNoNo
ReasoningNoNo
Tool callingYesYes
Structured outputNoYes
Availability
WeightsOpenProprietary
API model ID—mistral-large-latest
API providers711 (best)
ReleasedNov 12, 2024Feb 26, 2024
Knowledge cutoff—Nov 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.

  • Qwen2.5-Coder-32B-Instruct$8.60
  • Mistral Large$8.00
04 — Questions

Which should you choose?

Which is better: Qwen2.5-Coder-32B-Instruct or Mistral Large?

Mistral Large is the better all-round choice, scoring 45/100 against Qwen2.5-Coder-32B-Instruct (39). It leads on inputs & features. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.

Which is cheaper, Qwen2.5-Coder-32B-Instruct or Mistral Large?

Qwen2.5-Coder-32B-Instruct is cheaper at $0.66 input / $1.00 output per million tokens (median across 7 API providers). Mistral Large costs $0.50 input / $1.50 output per million tokens (official Mistral API price). At a typical mix of three input tokens to one output token, that is $0.745 per million tokens for Qwen2.5-Coder-32B-Instruct versus $0.75 for Mistral Large (1× as much).

Which scores higher on benchmarks?

There is no independent benchmark that covers both models yet. Qwen2.5-Coder-32B-Instruct has not been scored yet and Mistral Large has an ECI of 122.0.

Which is better for coding?

There are no published SWE-bench Verified results for Qwen2.5-Coder-32B-Instruct and Mistral Large yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Both support tool calling for agent workflows.

Which has the bigger context window?

Qwen2.5-Coder-32B-Instruct has the largest context window at 131,072 tokens, against 128,000 for Mistral Large. Maximum output per response: Qwen2.5-Coder-32B-Instruct up to 8,192, Mistral Large up to 102,400 tokens.

Which can read images, PDFs, audio or video?

Qwen2.5-Coder-32B-Instruct accepts text; Mistral Large accepts text and PDFs. Mistral Large handles the widest range of inputs.

Are any of these open source?

Qwen2.5-Coder-32B-Instruct publishes its weights and can be self-hosted; Mistral Large is proprietary.

Which is newer?

Qwen2.5-Coder-32B-Instruct is the newest, released Nov 12, 2024. Mistral Large came out Feb 26, 2024. Knowledge cutoff: Mistral Large Nov 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 Nov 2, 2026.

05 — Keep comparing

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