Noometry

Model comparison

GPT-5-Codex vs Mixtral 8x22B

GPT-5-Codex is the stronger model overall, scoring 37.9 to 27.1 on the Noometry Index.

Last verified . 1 shared benchmarks.

GPT-5-Codex OpenAI

37.9

Rank #192 Reported

Mixtral 8x22B Mistral AI

27.1

Rank #333 Confirmed

Summary

  • They share 1 benchmark with published results for both. GPT-5-Codex scores higher in 3 categories and Mixtral 8x22B in 0 categories; 3 gaps are clear of the uncertainty.
  • The widest gap is in coding, where GPT-5-Codex leads 42.4 to 24.2.
  • The biggest single-benchmark swing is WeirdML: 54.5% for GPT-5-Codex and 3.2% for Mixtral 8x22B.
  • Mixtral 8x22B is cheaper at $2 / $6 per million input/output tokens, against $1.25 / $10 for GPT-5-Codex.
  • GPT-5-Codex accepts more context: 400K tokens versus 64K.
  • Mixtral 8x22B has downloadable open weights; the other is API-only.

Side by side

GPT-5-CodexMixtral 8x22B
ProviderOpenAIMistral AI
Noometry Index37.927.1
Released2025-09-152024-04-17
WeightsProprietaryOpen
Context window400K64K
Max output128K64K
Input $ / M tokens$1.25$2
Output $ / M tokens$10$6
Results tracked334

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Category by category

Coding GPT-5-Codex leads

GPT-5-Codex: 42.4 (#103), Mixtral 8x22B: 24.2 (#329)

BenchmarkGPT-5-CodexMixtral 8x22B
WeirdML54.5%3.2%
BigCodeBench Instruct—40.6%
LMArena Coding—1166
BigCodeBench Complete—50.2%
HumanEval+—72%
MBPP+—64.3%

Agentic & Tool Use GPT-5-Codex leads

GPT-5-Codex: 31.0 (#72), Mixtral 8x22B: 23.1 (#127)

BenchmarkGPT-5-CodexMixtral 8x22B
Terminal-Bench44.3%—
Cybench—7.5%

Reasoning GPT-5-Codex leads

GPT-5-Codex: 30.9 (#83), Mixtral 8x22B: 19.9 (#248)

BenchmarkGPT-5-CodexMixtral 8x22B
Kagi LLM Benchmark70.3%—
LMArena Hard Prompts—1150
DTBench—55.1%
Epoch Capabilities Index—122.03
ForecastBench—56.3

Math Not comparable

GPT-5-Codex: —, Mixtral 8x22B: 22.9 (#275)

BenchmarkGPT-5-CodexMixtral 8x22B
Omni-MATH—16.3%
LMArena Math—1184
MATH Level 5—24.2%

Knowledge Not comparable

GPT-5-Codex: —, Mixtral 8x22B: 15.1 (#293)

BenchmarkGPT-5-CodexMixtral 8x22B
GPQA Diamond—34.1%
MMLU-Pro—46%
GPQA (HELM)—33.4%
LMArena Expert—1113
MMLU—77.8%

Multilingual Not comparable

GPT-5-Codex: —, Mixtral 8x22B: 32.8 (#255)

BenchmarkGPT-5-CodexMixtral 8x22B
LMArena Non-English—1128
LMArena Chinese—1116
LMArena French—1166
LMArena German—1141
LMArena Japanese—1037
LMArena Korean—1057
LMArena Russian—1158
LMArena Spanish—1151

Instruction Following Not comparable

GPT-5-Codex: —, Mixtral 8x22B: 57.7 (#266)

BenchmarkGPT-5-CodexMixtral 8x22B
IFEval—72.4%
LMArena Instruction Following—1147

Long Context Not comparable

GPT-5-Codex: —, Mixtral 8x22B: 34.7 (#247)

BenchmarkGPT-5-CodexMixtral 8x22B
LMArena Longer Query—1144

Writing & Preference Not comparable

GPT-5-Codex: —, Mixtral 8x22B: 36.9 (#262)

BenchmarkGPT-5-CodexMixtral 8x22B
LMArena Text—1162
LMArena Creative Writing—1141
WildBench—71.1%
LMArena Multi-Turn—1130

Frequently asked questions

Is GPT-5-Codex better than Mixtral 8x22B?

GPT-5-Codex is the stronger model overall, scoring 37.9 to 27.1 on the Noometry Index.

Which is cheaper, GPT-5-Codex or Mixtral 8x22B?

Mixtral 8x22B is cheaper. It lists at $2 per million input tokens and $6 per million output tokens; GPT-5-Codex lists at $1.25 and $10.

Is GPT-5-Codex or Mixtral 8x22B better for coding?

GPT-5-Codex scores higher on coding benchmarks: 42.4 versus 24.2 in the Noometry coding category.

Which has the bigger context window?

GPT-5-Codex does, with 400K tokens against 64K.

How many benchmarks do GPT-5-Codex and Mixtral 8x22B share?

1 benchmark has published results for both models. GPT-5-Codex has 3 scored results on Noometry and Mixtral 8x22B has 34.

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