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Comparison · 3 models · Updated Oct 4, 2026

Qwen2.5 32B Instruct vs Nova Pro vs Mistral Large 2.1

Nova Pro comes out ahead, 48 to 43 and 38 on our weighted score, though Qwen2.5 32B Instruct is 13% cheaper per token.

  1. Alibaba (Qwen)

    Qwen2.5 32B Instruct

    Released Sep 17, 2024

    43/100
    • ECI128.5
    • Price$0.70 / $2.80
    • Context131K
    Full specs Remove Qwen2.5 32B Instruct
  2. Our pick

    Amazon

    Nova Pro

    Released Dec 3, 2024

    48/100
    • ECI123.8
    • Price$0.80 / $3.20
    • Context300K
    Full specs Remove Nova Pro

  3. Mistral AI

    Mistral Large 2.1

    Released Nov 18, 2024

    38/100
    • ECI128.5
    • Price$2.00 / $6.00
    • Context131K
    Full specs Remove Mistral Large 2.1
01 — Verdict

Nova Pro is our pick

Nova Pro is the better all-round choice, scoring 48/100 against Qwen2.5 32B Instruct (43) and Mistral Large 2.1 (38). It leads on inputs & features and context window. Qwen2.5 32B Instruct wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityQwen2.5 32B Instruct and Mistral Large 2.1Capabilities Index (ECI): Qwen2.5 32B Instruct 128.5 · Mistral Large 2.1 128.5 · Nova Pro 123.8
  • Lowest priceQwen2.5 32B InstructQwen2.5 32B Instruct $1.23 · Nova Pro $1.40 · Mistral Large 2.1 $3.00 per 1M tokens (3:1 blend)
  • Longest contextNova ProNova Pro 300,000 · Qwen2.5 32B Instruct 131,072 · Mistral Large 2.1 131,072 tokens
  • Widest inputsNova ProQwen2.5 32B Instruct: Text · Nova Pro: Text, Images, PDFs, Video · Mistral Large 2.1: Text
  • Self-hostingQwen2.5 32B Instruct and Mistral Large 2.1Publishes downloadable weights
How the score is built
MeasureWeightQwen2.5 32B InstructNova ProMistral Large 2.1
CapabilityCapabilities Index (ECI)50%514551
Price25%464327
Inputs & features15%257025
Context window10%243924
Overall100%43/10048/10038/100
02 — Side by side

Every spec in one table

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

Qwen2.5 32B Instruct vs Nova Pro vs Mistral Large 2.1 specifications side by side
SpecificationQwen2.5 32B InstructAlibaba (Qwen)Nova ProAmazonMistral Large 2.1Mistral AI
Capability
Capabilities Index (ECI)128.5 (best)123.8128.5 (best)
ECI rank#131 of 148#137 of 148#130 of 148 (best)
GPQA DiamondGraduate-level science questions46.1%—51.3% (best)
OTIS Mock AIME 2024–2025Competition mathematics7.4%—7.8% (best)
Price per million tokens
Input$0.70 (best)$0.80$2.00
Output$2.80 (best)$3.20$6.00
Cached input—$0.20—
Blended (3:1)$1.23 (best)$1.40$3.00
Long-context rateSame rateSame rateSame rate
Price sourceOfficial Alibaba APIOfficial Amazon Bedrock APIOfficial Mistral API
Limits
Context window131,072 tokens300,000 tokens (best)131,072 tokens
Max output8,192 tokens10,000 tokens16,384 tokens (best)
Inputs and features
TextYesYesYes
ImagesNoYesNo
PDFsNoYesNo
AudioNoNoNo
VideoNoYesNo
ReasoningNoNoNo
Tool callingYesYesYes
Structured outputNoNoNo
Availability
WeightsOpenProprietaryOpen
API model IDqwen2-5-32b-instructamazon.nova-pro-v1:0mistral-large-2411
API providers13 (best)2
ReleasedSep 17, 2024Dec 3, 2024Nov 18, 2024
Knowledge cutoffApr 2024Oct 2024Nov 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 32B Instruct$12.60
  • Nova Pro$14.40
  • Mistral Large 2.1$32.00
04 — Questions

Which should you choose?

Which is better: Qwen2.5 32B Instruct, Nova Pro or Mistral Large 2.1?

Nova Pro is the better all-round choice, scoring 48/100 against Qwen2.5 32B Instruct (43) and Mistral Large 2.1 (38). It leads on inputs & features and context window. Qwen2.5 32B Instruct wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Qwen2.5 32B Instruct, Nova Pro or Mistral Large 2.1?

Qwen2.5 32B Instruct is cheaper at $0.70 input / $2.80 output per million tokens (official Alibaba API price). Nova Pro costs $0.80 input / $3.20 output per million tokens (official Amazon Bedrock API price); Mistral Large 2.1 costs $2.00 input / $6.00 output per million tokens (official Mistral API price). At a typical mix of three input tokens to one output token, that is $1.23 per million tokens for Qwen2.5 32B Instruct versus $1.40 for Nova Pro (1.1× as much) and $3.00 for Mistral Large 2.1 (2.4× as much).

Which scores higher on benchmarks?

Qwen2.5 32B Instruct scores higher on the Capabilities Index (ECI): Qwen2.5 32B Instruct 128.5 (#131 of 148), Mistral Large 2.1 128.5 (#130 of 148) and Nova Pro 123.8 (#137 of 148). The confidence ranges of the top two overlap (123.5–130.0 vs 123.8–130.8), so treat the gap as small.

Which is better for coding?

There are no published SWE-bench Verified results for Qwen2.5 32B Instruct, Nova Pro and Mistral Large 2.1 yet, so there is no like-for-like coding score. On overall capability, Qwen2.5 32B Instruct 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?

Nova Pro has the largest context window at 300,000 tokens, against 131,072 for Qwen2.5 32B Instruct and 131,072 for Mistral Large 2.1. Maximum output per response: Qwen2.5 32B Instruct up to 8,192, Nova Pro up to 10,000, Mistral Large 2.1 up to 16,384 tokens.

Which can read images, PDFs, audio or video?

Qwen2.5 32B Instruct accepts text; Nova Pro accepts text, images, PDFs and video; Mistral Large 2.1 accepts text. Nova Pro handles the widest range of inputs.

Are any of these open source?

Qwen2.5 32B Instruct and Mistral Large 2.1 publishes its weights and can be self-hosted; Nova Pro is proprietary.

Which is newer?

Nova Pro is the newest, released Dec 3, 2024. Mistral Large 2.1 came out Nov 18, 2024; Qwen2.5 32B Instruct came out Sep 17, 2024. Knowledge cutoff: Qwen2.5 32B Instruct Apr 2024, Nova Pro Oct 2024, Mistral Large 2.1 Nov 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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