# GPT-5-Codex vs Mixtral 8x7B

> GPT-5-Codex is the stronger model overall, scoring 37.9 to 27.1 on the Noometry Index. Mixtral 8x7B costs 4.9× less per token, which makes it the better buy when GPT-5-Codex's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gpt-5-codex-vs-mixtral-8x7b
- Last updated: 2026-10-10
- Shared benchmarks: 0

## Summary

- The widest gap is in reasoning, where GPT-5-Codex leads 30.9 to 18.2.
- Mixtral 8x7B is cheaper at $0.70 / $0.70 per million input/output tokens, against $1.25 / $10 for GPT-5-Codex.
- GPT-5-Codex accepts more context: 400K tokens versus 32K.
- Mixtral 8x7B has downloadable open weights; the other is API-only.

## Snapshot

| | GPT-5-Codex | Mixtral 8x7B |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 37.9 | 27.1 |
| Rank | 192 | 334 |
| Context | 400K | 32K |
| Input $/M | $1.25 | $0.70 |
| Output $/M | $10 | $0.70 |
| Weights | Proprietary | Open |

## Coding

- GPT-5-Codex: 42.4 (#103)
- Mixtral 8x7B: 32.8 (#269)

| Benchmark | GPT-5-Codex | Mixtral 8x7B |
|---|---|---|
| WeirdML | 54.5% | — |
| LMArena Coding | — | 1126 |
| HumanEval+ | — | 39.6% |
| MBPP+ | — | 49.7% |

## Agentic & Tool Use

- GPT-5-Codex: 31.0 (#72)
- Mixtral 8x7B: —

| Benchmark | GPT-5-Codex | Mixtral 8x7B |
|---|---|---|
| Terminal-Bench | 44.3% | — |

## Reasoning

- GPT-5-Codex: 30.9 (#83)
- Mixtral 8x7B: 18.2 (#285)

| Benchmark | GPT-5-Codex | Mixtral 8x7B |
|---|---|---|
| Kagi LLM Benchmark | 70.3% | — |
| LMArena Hard Prompts | — | 1115 |
| DTBench | — | 49.6% |
| Adversarial NLI | — | 55.2% |
| Epoch Capabilities Index | — | 118.47 |
| ForecastBench | — | 56.3 |
| HellaSwag | — | 86.7% |
| PIQA | — | 83.6% |
| WinoGrande | — | 77.2% |

## Math

- GPT-5-Codex: —
- Mixtral 8x7B: 18.8 (#289)

| Benchmark | GPT-5-Codex | Mixtral 8x7B |
|---|---|---|
| Omni-MATH | — | 10.5% |
| LMArena Math | — | 1147 |
| MATH Level 5 | — | 10% |
| GSM8K | — | 74.4% |

## Knowledge

- GPT-5-Codex: —
- Mixtral 8x7B: 11.0 (#301)

| Benchmark | GPT-5-Codex | Mixtral 8x7B |
|---|---|---|
| GPQA Diamond | — | 30.6% |
| 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

- GPT-5-Codex: —
- Mixtral 8x7B: 29.6 (#266)

| Benchmark | GPT-5-Codex | Mixtral 8x7B |
|---|---|---|
| LMArena Non-English | — | 1077 |
| LMArena Chinese | — | 1055 |
| LMArena French | — | 1166 |
| LMArena German | — | 1114 |
| LMArena Japanese | — | 931 |
| LMArena Korean | — | 968 |
| LMArena Russian | — | 1090 |
| LMArena Spanish | — | 1111 |

## Instruction Following

- GPT-5-Codex: —
- Mixtral 8x7B: 51.0 (#297)

| Benchmark | GPT-5-Codex | Mixtral 8x7B |
|---|---|---|
| IFEval | — | 57.5% |
| LMArena Instruction Following | — | 1109 |

## Long Context

- GPT-5-Codex: —
- Mixtral 8x7B: 33.4 (#260)

| Benchmark | GPT-5-Codex | Mixtral 8x7B |
|---|---|---|
| LMArena Longer Query | — | 1103 |

## Writing & Preference

- GPT-5-Codex: —
- Mixtral 8x7B: 34.2 (#270)

| Benchmark | GPT-5-Codex | Mixtral 8x7B |
|---|---|---|
| LMArena Text | — | 1132 |
| LMArena Creative Writing | — | 1109 |
| WildBench | — | 67.3% |
| LMArena Multi-Turn | — | 1115 |

## FAQ

### Is GPT-5-Codex better than Mixtral 8x7B?

GPT-5-Codex is the stronger model overall, scoring 37.9 to 27.1 on the Noometry Index. Mixtral 8x7B costs 4.9× less per token, which makes it the better buy when GPT-5-Codex's lead doesn't matter for your workload.

### Which is cheaper, GPT-5-Codex 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-Codex lists at $1.25 and $10.

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

GPT-5-Codex scores higher on coding benchmarks: 42.4 versus 32.8 in the Noometry coding category.

### Which has the bigger context window?

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

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

0 benchmarks have published results for both models. GPT-5-Codex has 3 scored results on Noometry and Mixtral 8x7B has 38.