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Comparison · 3 models · Updated Oct 4, 2026

GPT-4o vs Mixtral 8x7B vs Qwen2.5 72B Instruct

GPT-4o comes out ahead, 43 to 40 and 37 on our weighted score, though Mixtral 8x7B is 6.3× cheaper per token.

  1. Our pick

    OpenAI

    GPT-4o

    Released May 13, 2024

    43/100
    • ECI129.0
    • Price$2.50 / $10.00
    • Context128K
    Full specs Remove GPT-4o

  2. Mistral AI

    Mixtral 8x7B

    Released Dec 11, 2023

    37/100
    • ECI118.5
    • Price$0.70 / $0.70
    • Context32K
    Full specs Remove Mixtral 8x7B

  3. Alibaba (Qwen)

    Qwen2.5 72B Instruct

    Released Sep 19, 2024

    40/100
    • ECI129.0
    • Price$1.40 / $5.60
    • Context131K
    Full specs Remove Qwen2.5 72B Instruct
01 — Verdict

GPT-4o is our pick

GPT-4o is the better all-round choice, scoring 43/100 against Qwen2.5 72B Instruct (40) and Mixtral 8x7B (37). It leads on inputs & features. Mixtral 8x7B wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityQwen2.5 72B InstructCapabilities Index (ECI): Qwen2.5 72B Instruct 129.0 · GPT-4o 129.0 · Mixtral 8x7B 118.5
  • Lowest priceMixtral 8x7BMixtral 8x7B $0.70 · Qwen2.5 72B Instruct $2.45 · GPT-4o $4.38 per 1M tokens (3:1 blend)
  • Longest contextQwen2.5 72B InstructQwen2.5 72B Instruct 131,072 · GPT-4o 128,000 · Mixtral 8x7B 32,000 tokens
  • Widest inputsGPT-4oGPT-4o: Text, Images, PDFs · Mixtral 8x7B: Text · Qwen2.5 72B Instruct: Text
  • Self-hostingMixtral 8x7B and Qwen2.5 72B InstructPublishes downloadable weights
How the score is built
MeasureWeightGPT-4oMixtral 8x7BQwen2.5 72B Instruct
CapabilityCapabilities Index (ECI)50%523852
Price25%195731
Inputs & features15%702525
Context window10%24024
Overall100%43/10037/10040/100
02 — Side by side

Every spec in one table

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

GPT-4o vs Mixtral 8x7B vs Qwen2.5 72B Instruct specifications side by side
SpecificationGPT-4oOpenAIMixtral 8x7BMistral AIQwen2.5 72B InstructAlibaba (Qwen)
Capability
Capabilities Index (ECI)129.0118.5129.0 (best)
ECI rank#129 of 148#142 of 148#128 of 148 (best)
GPQA DiamondGraduate-level science questions48.9%30.6%49.2% (best)
OTIS Mock AIME 2024–2025Competition mathematics6.3%—8.1% (best)
Price per million tokens
Input$2.50$0.70 (best)$1.40
Output$10.00$0.70 (best)$5.60
Cached input$1.25——
Blended (3:1)$4.38$0.70 (best)$2.45
Long-context rateSame rateSame rateSame rate
Price sourceOfficial OpenAI APIOfficial Mistral APIOfficial Alibaba API
Limits
Context window128,000 tokens32,000 tokens131,072 tokens (best)
Max output16,384 tokens32,000 tokens (best)8,192 tokens
Inputs and features
TextYesYesYes
ImagesYesNoNo
PDFsYesNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningNoNoNo
Tool callingYesYesYes
Structured outputYesNoNo
Availability
WeightsProprietaryOpenOpen
API model IDgpt-4oopen-mixtral-8x7bqwen2-5-72b-instruct
API providers19 (best)11
ReleasedMay 13, 2024Dec 11, 2023Sep 19, 2024
Knowledge cutoffSep 2023Jan 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$45.00
  • Mixtral 8x7B$8.40
  • Qwen2.5 72B Instruct$25.20
04 — Questions

Which should you choose?

Which is better: GPT-4o, Mixtral 8x7B or Qwen2.5 72B Instruct?

GPT-4o is the better all-round choice, scoring 43/100 against Qwen2.5 72B Instruct (40) and Mixtral 8x7B (37). It leads on inputs & features. Mixtral 8x7B wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, GPT-4o, Mixtral 8x7B or Qwen2.5 72B Instruct?

Mixtral 8x7B is cheaper at $0.70 input / $0.70 output per million tokens (official Mistral API price). Qwen2.5 72B Instruct costs $1.40 input / $5.60 output per million tokens (official Alibaba API price); GPT-4o costs $2.50 input / $10.00 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $0.70 per million tokens for Mixtral 8x7B versus $2.45 for Qwen2.5 72B Instruct (3.5× as much) and $4.38 for GPT-4o (6.3× as much).

Which scores higher on benchmarks?

Qwen2.5 72B Instruct scores higher on the Capabilities Index (ECI): Qwen2.5 72B Instruct 129.0 (#128 of 148), GPT-4o 129.0 (#129 of 148) and Mixtral 8x7B 118.5 (#142 of 148). The confidence ranges of the top two overlap (123.8–130.7 vs 124.2–131.5), so treat the gap as small. On individual benchmarks: GPQA Diamond — Qwen2.5 72B Instruct 49.2%, GPT-4o 48.9%, Mixtral 8x7B 30.6%.

Which is better for coding?

There are no published SWE-bench Verified results for GPT-4o, Mixtral 8x7B and Qwen2.5 72B Instruct yet, so there is no like-for-like coding score. On overall capability, Qwen2.5 72B 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?

Qwen2.5 72B Instruct has the largest context window at 131,072 tokens, against 128,000 for GPT-4o and 32,000 for Mixtral 8x7B. Maximum output per response: GPT-4o up to 16,384, Mixtral 8x7B up to 32,000, Qwen2.5 72B Instruct up to 8,192 tokens.

Which can read images, PDFs, audio or video?

GPT-4o accepts text, images and PDFs; Mixtral 8x7B accepts text; Qwen2.5 72B Instruct accepts text. GPT-4o handles the widest range of inputs.

Are any of these open source?

Mixtral 8x7B and Qwen2.5 72B Instruct publishes its weights and can be self-hosted; GPT-4o is proprietary.

Which is newer?

Qwen2.5 72B Instruct is the newest, released Sep 19, 2024. GPT-4o came out May 13, 2024; Mixtral 8x7B came out Dec 11, 2023. Knowledge cutoff: GPT-4o Sep 2023, Mixtral 8x7B Jan 2024, Qwen2.5 72B 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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