Model comparison

GPT-5 Pro vs Mixtral 8x7B

GPT-5 Pro is the stronger model overall, scoring 46.2 to 26.8 on the Noometry Index. Mixtral 8x7B costs 59× less per token, which makes it the better buy when GPT-5 Pro's lead doesn't matter for your workload.

Last verified . 1 shared benchmarks.

GPT-5 Pro OpenAI

46.2

Rank #58 Confirmed

Mixtral 8x7B Mistral AI

26.8

Rank #328 Confirmed

Summary

  • They share 1 benchmark with published results for both. GPT-5 Pro scores higher in 4 categories and Mixtral 8x7B in 0 categories; 4 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where GPT-5 Pro leads 56.2 to 11.0.
  • Mixtral 8x7B is cheaper at $0.70 / $0.70 per million input/output tokens, against $15 / $120 for GPT-5 Pro.
  • GPT-5 Pro accepts more context: 400K tokens versus 32K.
  • Mixtral 8x7B has downloadable open weights; the other is API-only.

Side by side

GPT-5 Pro and Mixtral 8x7B specifications
GPT-5 ProMixtral 8x7B
ProviderOpenAIMistral AI
Noometry Index46.226.8
Released2025-10-062023-12-11
WeightsProprietaryOpen
Context window400K32K
Max output272K32K
Input $ / M tokens$15$0.70
Output $ / M tokens$120$0.70
Results tracked1238

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Category by category

Coding GPT-5 Pro leads

GPT-5 Pro: 43.4 (#77), Mixtral 8x7B: 32.2 (#265)

Coding benchmarks
BenchmarkGPT-5 ProMixtral 8x7B
WeirdML60.4%—
LMArena Coding—1127
AlgoTune1.31—
HumanEval+—39.6%
MBPP+—49.7%

Reasoning GPT-5 Pro leads

GPT-5 Pro: 39.2 (#52), Mixtral 8x7B: 17.4 (#283)

Reasoning benchmarks
BenchmarkGPT-5 ProMixtral 8x7B
Epoch Capabilities Index150.26118.38
ARC-AGI-218.3%—
SimpleBench61.6%—
Kagi LLM Benchmark76.8%—
ARC-AGI-170.2%—
EnigmaEval18.8%—
LMArena Hard Prompts—1115
DTBench—49.6%
Adversarial NLI—55.2%
ForecastBench—56.3
HellaSwag—86.7%
PIQA—83.6%
WinoGrande—77.2%

Math GPT-5 Pro leads

GPT-5 Pro: 48.0 (#53), Mixtral 8x7B: 17.2 (#284)

Math benchmarks
BenchmarkGPT-5 ProMixtral 8x7B
FrontierMath (Tiers 1-3)55.8%—
FrontierMath Tier 419.5%—
Omni-MATH—10.5%
LMArena Math—1147
MATH Level 5—10%
FrontierMath Tier 4 (v1)14.6%—
GSM8K—74.4%

Knowledge GPT-5 Pro leads

GPT-5 Pro: 56.2 (#39), Mixtral 8x7B: 11.0 (#296)

Knowledge benchmarks
BenchmarkGPT-5 ProMixtral 8x7B
GPQA Diamond—30.6%
Humanity's Last Exam31.6%—
MMLU-Pro—33.5%
GPQA (HELM)—29.6%
LMArena Expert—1088
ARC (AI2) Challenge—87.3%
MMLU—70.6%
OpenBookQA—85.8%
TriviaQA—82.2%

Multilingual Not comparable

GPT-5 Pro: —, Mixtral 8x7B: 29.8 (#261)

Multilingual benchmarks
BenchmarkGPT-5 ProMixtral 8x7B
LMArena Non-English—1077
LMArena Chinese—1055
LMArena French—1165
LMArena German—1114
LMArena Japanese—931
LMArena Korean—968
LMArena Russian—1090
LMArena Spanish—1110

Instruction Following Not comparable

GPT-5 Pro: —, Mixtral 8x7B: 51.2 (#292)

Instruction Following benchmarks
BenchmarkGPT-5 ProMixtral 8x7B
IFEval—57.5%
LMArena Instruction Following—1110

Long Context Not comparable

GPT-5 Pro: —, Mixtral 8x7B: 33.4 (#255)

Long Context benchmarks
BenchmarkGPT-5 ProMixtral 8x7B
LMArena Longer Query—1103

Writing & Preference Not comparable

GPT-5 Pro: —, Mixtral 8x7B: 34.4 (#265)

Writing & Preference benchmarks
BenchmarkGPT-5 ProMixtral 8x7B
LMArena Text—1132
LMArena Creative Writing—1109
WildBench—67.3%
LMArena Multi-Turn—1115

Frequently asked questions

Is GPT-5 Pro better than Mixtral 8x7B?

GPT-5 Pro is the stronger model overall, scoring 46.2 to 26.8 on the Noometry Index. Mixtral 8x7B costs 59× less per token, which makes it the better buy when GPT-5 Pro's lead doesn't matter for your workload.

Which is cheaper, GPT-5 Pro or Mixtral 8x7B?

Mixtral 8x7B is cheaper. It lists at $0.70 per million input tokens and $0.70 per million output tokens; GPT-5 Pro lists at $15 and $120.

Is GPT-5 Pro or Mixtral 8x7B better for coding?

GPT-5 Pro scores higher on coding benchmarks: 43.4 versus 32.2 in the Noometry coding category.

Which has the bigger context window?

GPT-5 Pro does, with 400K tokens against 32K.

How many benchmarks do GPT-5 Pro and Mixtral 8x7B share?

1 benchmark has published results for both models. GPT-5 Pro has 12 scored results on Noometry and Mixtral 8x7B has 38.

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