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Comparison · 3 models · Updated Oct 4, 2026

Qwen2.5 7B Instruct vs GPT-4o mini vs Mistral Nemo

GPT-4o mini comes out ahead, 56 to 48 and 44 on our weighted score, though Mistral Nemo is 43% cheaper per token.

  1. Alibaba (Qwen)

    Qwen2.5 7B Instruct

    Released Sep 19, 2024

    44/100
    • ECI118.5
    • Price$0.175 / $0.70
    • Context131K
    Full specs Remove Qwen2.5 7B Instruct
  2. Our pick

    OpenAI

    GPT-4o mini

    Released Jul 18, 2024

    56/100
    • ECI126.6
    • Price$0.15 / $0.60
    • Context128K
    Full specs Remove GPT-4o mini

  3. Mistral AI

    Mistral Nemo

    Released Jul 1, 2024

    48/100
    • ECI118.7
    • Price$0.15 / $0.15
    • Context128K
    Full specs Remove Mistral Nemo
01 — Verdict

GPT-4o mini is our pick

GPT-4o mini is the better all-round choice, scoring 56/100 against Mistral Nemo (48) and Qwen2.5 7B Instruct (44). It leads on capability and inputs & features. Mistral Nemo wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityGPT-4o miniCapabilities Index (ECI): GPT-4o mini 126.6 · Mistral Nemo 118.7 · Qwen2.5 7B Instruct 118.5
  • Lowest priceMistral NemoMistral Nemo $0.15 · GPT-4o mini $0.263 · Qwen2.5 7B Instruct $0.306 per 1M tokens (3:1 blend)
  • Longest contextQwen2.5 7B InstructQwen2.5 7B Instruct 131,072 · GPT-4o mini 128,000 · Mistral Nemo 128,000 tokens
  • Widest inputsGPT-4o miniQwen2.5 7B Instruct: Text · GPT-4o mini: Text, Images, PDFs · Mistral Nemo: Text
  • Self-hostingQwen2.5 7B Instruct and Mistral NemoPublishes downloadable weights
How the score is built
MeasureWeightQwen2.5 7B InstructGPT-4o miniMistral Nemo
CapabilityCapabilities Index (ECI)50%384939
Price25%747789
Inputs & features15%257025
Context window10%242424
Overall100%44/10056/10048/100
02 — Side by side

Every spec in one table

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

Qwen2.5 7B Instruct vs GPT-4o mini vs Mistral Nemo specifications side by side
SpecificationQwen2.5 7B InstructAlibaba (Qwen)GPT-4o miniOpenAIMistral NemoMistral AI
Capability
Capabilities Index (ECI)118.5126.6 (best)118.7
ECI rank#141 of 148#135 of 148 (best)#140 of 148
GPQA DiamondGraduate-level science questions35.5%37.7% (best)29.9%
FrontierMath Tiers 1–3Research-level mathematics—0.7%—
OTIS Mock AIME 2024–2025Competition mathematics2.5%6.9% (best)—
SimpleQA VerifiedShort factual questions—8.3%—
Price per million tokens
Input$0.175$0.15 (best)$0.15 (best)
Output$0.70$0.60$0.15 (best)
Cached input—$0.075—
Blended (3:1)$0.306$0.263$0.15 (best)
Long-context rateSame rateSame rateSame rate
Price sourceOfficial Alibaba APIOfficial OpenAI APIOfficial Mistral API
Limits
Context window131,072 tokens (best)128,000 tokens128,000 tokens
Max output8,192 tokens16,384 tokens128,000 tokens (best)
Inputs and features
TextYesYesYes
ImagesNoYesNo
PDFsNoYesNo
AudioNoNoNo
VideoNoNoNo
ReasoningNoNoNo
Tool callingYesYesYes
Structured outputNoYesNo
Availability
WeightsOpenProprietaryOpen
API model IDqwen2-5-7b-instructgpt-4o-minimistral-nemo
API providers121 (best)5
ReleasedSep 19, 2024Jul 18, 2024Jul 1, 2024
Knowledge cutoffApr 2024Sep 2023Jul 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 7B Instruct$3.15
  • GPT-4o mini$2.70
  • Mistral Nemo$1.80
04 — Questions

Which should you choose?

Which is better: Qwen2.5 7B Instruct, GPT-4o mini or Mistral Nemo?

GPT-4o mini is the better all-round choice, scoring 56/100 against Mistral Nemo (48) and Qwen2.5 7B Instruct (44). It leads on capability and inputs & features. Mistral Nemo wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Qwen2.5 7B Instruct, GPT-4o mini or Mistral Nemo?

Mistral Nemo is cheaper at $0.15 input / $0.15 output per million tokens (official Mistral API price). GPT-4o mini costs $0.15 input / $0.60 output per million tokens (official OpenAI API price); Qwen2.5 7B Instruct costs $0.175 input / $0.70 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.15 per million tokens for Mistral Nemo versus $0.263 for GPT-4o mini (1.8× as much) and $0.306 for Qwen2.5 7B Instruct (2× as much).

Which scores higher on benchmarks?

GPT-4o mini scores higher on the Capabilities Index (ECI): GPT-4o mini 126.6 (#135 of 148), Mistral Nemo 118.7 (#140 of 148) and Qwen2.5 7B Instruct 118.5 (#141 of 148). The confidence ranges of the top two overlap (120.5–128.5 vs 111.3–121.5), so treat the gap as small. On individual benchmarks: GPQA Diamond — GPT-4o mini 37.7%, Qwen2.5 7B Instruct 35.5%, Mistral Nemo 29.9%.

Which is better for coding?

There are no published SWE-bench Verified results for Qwen2.5 7B Instruct, GPT-4o mini and Mistral Nemo yet, so there is no like-for-like coding score. On overall capability, GPT-4o mini 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?

Qwen2.5 7B Instruct has the largest context window at 131,072 tokens, against 128,000 for GPT-4o mini and 128,000 for Mistral Nemo. Maximum output per response: Qwen2.5 7B Instruct up to 8,192, GPT-4o mini up to 16,384, Mistral Nemo up to 128,000 tokens.

Which can read images, PDFs, audio or video?

Qwen2.5 7B Instruct accepts text; GPT-4o mini accepts text, images and PDFs; Mistral Nemo accepts text. GPT-4o mini handles the widest range of inputs.

Are any of these open source?

Qwen2.5 7B Instruct and Mistral Nemo publishes its weights and can be self-hosted; GPT-4o mini is proprietary.

Which is newer?

Qwen2.5 7B Instruct is the newest, released Sep 19, 2024. GPT-4o mini came out Jul 18, 2024; Mistral Nemo came out Jul 1, 2024. Knowledge cutoff: Qwen2.5 7B Instruct Apr 2024, GPT-4o mini Sep 2023, Mistral Nemo Jul 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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