# GPT-5-Codex vs Mixtral 8x22B

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

- Canonical page: https://noometry.com/compare/gpt-5-codex-vs-mixtral-8x22b
- Last updated: 2026-10-10
- Shared benchmarks: 1

## 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.

## Snapshot

| | GPT-5-Codex | Mixtral 8x22B |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 37.9 | 27.1 |
| Rank | 192 | 333 |
| Context | 400K | 64K |
| Input $/M | $1.25 | $2 |
| Output $/M | $10 | $6 |
| Weights | Proprietary | Open |

## Coding

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

| Benchmark | GPT-5-Codex | Mixtral 8x22B |
|---|---|---|
| WeirdML | 54.5% | 3.2% |
| BigCodeBench Instruct | — | 40.6% |
| LMArena Coding | — | 1166 |
| BigCodeBench Complete | — | 50.2% |
| HumanEval+ | — | 72% |
| MBPP+ | — | 64.3% |

## Agentic & Tool Use

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

| Benchmark | GPT-5-Codex | Mixtral 8x22B |
|---|---|---|
| Terminal-Bench | 44.3% | — |
| Cybench | — | 7.5% |

## Reasoning

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

| Benchmark | GPT-5-Codex | Mixtral 8x22B |
|---|---|---|
| Kagi LLM Benchmark | 70.3% | — |
| LMArena Hard Prompts | — | 1150 |
| DTBench | — | 55.1% |
| Epoch Capabilities Index | — | 122.03 |
| ForecastBench | — | 56.3 |

## Math

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

| Benchmark | GPT-5-Codex | Mixtral 8x22B |
|---|---|---|
| Omni-MATH | — | 16.3% |
| LMArena Math | — | 1184 |
| MATH Level 5 | — | 24.2% |

## Knowledge

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

| Benchmark | GPT-5-Codex | Mixtral 8x22B |
|---|---|---|
| GPQA Diamond | — | 34.1% |
| MMLU-Pro | — | 46% |
| GPQA (HELM) | — | 33.4% |
| LMArena Expert | — | 1113 |
| MMLU | — | 77.8% |

## Multilingual

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

| Benchmark | GPT-5-Codex | Mixtral 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

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

| Benchmark | GPT-5-Codex | Mixtral 8x22B |
|---|---|---|
| IFEval | — | 72.4% |
| LMArena Instruction Following | — | 1147 |

## Long Context

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

| Benchmark | GPT-5-Codex | Mixtral 8x22B |
|---|---|---|
| LMArena Longer Query | — | 1144 |

## Writing & Preference

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

| Benchmark | GPT-5-Codex | Mixtral 8x22B |
|---|---|---|
| LMArena Text | — | 1162 |
| LMArena Creative Writing | — | 1141 |
| WildBench | — | 71.1% |
| LMArena Multi-Turn | — | 1130 |

## FAQ

### 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.