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Comparison · 3 models · Updated Oct 4, 2026

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

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

  1. 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

  2. Mistral AI

    Mistral Nemo

    Released Jul 1, 2024

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

  3. 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
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 miniGPT-4o mini: Text, Images, PDFs · Mistral Nemo: Text · Qwen2.5 7B Instruct: Text
  • Self-hostingMistral Nemo and Qwen2.5 7B InstructPublishes downloadable weights
How the score is built
MeasureWeightGPT-4o miniMistral NemoQwen2.5 7B Instruct
CapabilityCapabilities Index (ECI)50%493938
Price25%778974
Inputs & features15%702525
Context window10%242424
Overall100%56/10048/10044/100
02 — Side by side

Every spec in one table

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

GPT-4o mini vs Mistral Nemo vs Qwen2.5 7B Instruct specifications side by side
SpecificationGPT-4o miniOpenAIMistral NemoMistral AIQwen2.5 7B InstructAlibaba (Qwen)
Capability
Capabilities Index (ECI)126.6 (best)118.7118.5
ECI rank#135 of 148 (best)#140 of 148#141 of 148
GPQA DiamondGraduate-level science questions37.7% (best)29.9%35.5%
FrontierMath Tiers 1–3Research-level mathematics0.7%——
OTIS Mock AIME 2024–2025Competition mathematics6.9% (best)—2.5%
SimpleQA VerifiedShort factual questions8.3%——
Price per million tokens
Input$0.15 (best)$0.15 (best)$0.175
Output$0.60$0.15 (best)$0.70
Cached input$0.075——
Blended (3:1)$0.263$0.15 (best)$0.306
Long-context rateSame rateSame rateSame rate
Price sourceOfficial OpenAI APIOfficial Mistral APIOfficial Alibaba API
Limits
Context window128,000 tokens128,000 tokens131,072 tokens (best)
Max output16,384 tokens128,000 tokens (best)8,192 tokens
Inputs and features
TextYesYesYes
ImagesYesNoNo
PDFsYesNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningNoNoNo
Tool callingYesYesYes
Structured outputYesNoNo
Availability
WeightsProprietaryOpenOpen
API model IDgpt-4o-minimistral-nemoqwen2-5-7b-instruct
API providers21 (best)51
ReleasedJul 18, 2024Jul 1, 2024Sep 19, 2024
Knowledge cutoffSep 2023Jul 2024Apr 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.

  • GPT-4o mini$2.70
  • Mistral Nemo$1.80
  • Qwen2.5 7B Instruct$3.15
04 — Questions

Which should you choose?

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

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

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 GPT-4o mini, Mistral Nemo and Qwen2.5 7B Instruct 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: GPT-4o mini up to 16,384, Mistral Nemo up to 128,000, Qwen2.5 7B Instruct up to 8,192 tokens.

Which can read images, PDFs, audio or video?

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

Are any of these open source?

Mistral Nemo and Qwen2.5 7B Instruct 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: GPT-4o mini Sep 2023, Mistral Nemo Jul 2024, Qwen2.5 7B Instruct Apr 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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