Model comparison
GPT-5.3 Codex vs Mixtral 8x22B
GPT-5.3 Codex is the stronger model overall, scoring 45.5 to 26.7 on the Noometry Index. Mixtral 8x22B costs 1.6× less per token, which makes it the better buy when GPT-5.3 Codex's lead doesn't matter for your workload.
Last verified . 2 shared benchmarks.
Mistral AISummary
- They share 2 benchmarks with published results for both. GPT-5.3 Codex scores higher in 2 categories and Mixtral 8x22B in 0 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where GPT-5.3 Codex leads 47.8 to 22.8.
- The biggest single-benchmark swing is WeirdML: 79.3% for GPT-5.3 Codex and 3.2% for Mixtral 8x22B.
- Mixtral 8x22B is cheaper at $2 / $6 per million input/output tokens, against $1.75 / $14 for GPT-5.3 Codex.
- GPT-5.3 Codex accepts more context: 400K tokens versus 64K.
- Mixtral 8x22B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.3 Codex | Mixtral 8x22B | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 45.5 | 26.7 |
| Released | 2026-02-05 | 2024-04-17 |
| Weights | Proprietary | Open |
| Context window | 400K | 64K |
| Max output | 128K | 64K |
| Input $ / M tokens | $1.75 | $2 |
| Output $ / M tokens | $14 | $6 |
| Results tracked | 8 | 34 |
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Category by category
Coding GPT-5.3 Codex leads
GPT-5.3 Codex: 48.5 (#51), Mixtral 8x22B: 23.8 (#324)
| Benchmark | GPT-5.3 Codex | Mixtral 8x22B |
|---|---|---|
| WeirdML | 79.3% | 3.2% |
| SWE-bench Verified | 74.8% | — |
| LMArena WebDev | 1408 | — |
| BigCodeBench Instruct | — | 40.6% |
| LMArena Coding | — | 1166 |
| BigCodeBench Complete | — | 50.2% |
| ALE-Bench | 1,655 | — |
| HumanEval+ | — | 72% |
| MBPP+ | — | 64.3% |
Agentic & Tool Use GPT-5.3 Codex leads
GPT-5.3 Codex: 47.8 (#9), Mixtral 8x22B: 22.8 (#119)
| Benchmark | GPT-5.3 Codex | Mixtral 8x22B |
|---|---|---|
| Terminal-Bench | 78.4% | — |
| Cybench | — | 7.5% |
| METR Time Horizons | 74.5% | — |
| Vending-Bench 2 | 5,940 | — |
Reasoning Not comparable
GPT-5.3 Codex: —, Mixtral 8x22B: 19.1 (#246)
| Benchmark | GPT-5.3 Codex | Mixtral 8x22B |
|---|---|---|
| Epoch Capabilities Index | 156.84 | 122 |
| LMArena Hard Prompts | — | 1150 |
| DTBench | — | 55.1% |
| ForecastBench | — | 56.3 |
Math Not comparable
GPT-5.3 Codex: —, Mixtral 8x22B: 21.2 (#271)
| Benchmark | GPT-5.3 Codex | Mixtral 8x22B |
|---|---|---|
| Omni-MATH | — | 16.3% |
| LMArena Math | — | 1184 |
| MATH Level 5 | — | 24.2% |
Knowledge Not comparable
GPT-5.3 Codex: —, Mixtral 8x22B: 15.1 (#288)
| Benchmark | GPT-5.3 Codex | Mixtral 8x22B |
|---|---|---|
| GPQA Diamond | — | 34.1% |
| MMLU-Pro | — | 46% |
| GPQA (HELM) | — | 33.4% |
| LMArena Expert | — | 1113 |
| MMLU | — | 77.8% |
Multilingual Not comparable
GPT-5.3 Codex: —, Mixtral 8x22B: 32.9 (#250)
| Benchmark | GPT-5.3 Codex | Mixtral 8x22B |
|---|---|---|
| LMArena Non-English | — | 1128 |
| LMArena Chinese | — | 1116 |
| LMArena French | — | 1165 |
| LMArena German | — | 1141 |
| LMArena Japanese | — | 1037 |
| LMArena Korean | — | 1056 |
| LMArena Russian | — | 1158 |
| LMArena Spanish | — | 1150 |
Instruction Following Not comparable
GPT-5.3 Codex: —, Mixtral 8x22B: 57.9 (#261)
| Benchmark | GPT-5.3 Codex | Mixtral 8x22B |
|---|---|---|
| IFEval | — | 72.4% |
| LMArena Instruction Following | — | 1147 |
Long Context Not comparable
GPT-5.3 Codex: —, Mixtral 8x22B: 34.6 (#242)
| Benchmark | GPT-5.3 Codex | Mixtral 8x22B |
|---|---|---|
| LMArena Longer Query | — | 1144 |
Writing & Preference Not comparable
GPT-5.3 Codex: —, Mixtral 8x22B: 37.1 (#257)
| Benchmark | GPT-5.3 Codex | Mixtral 8x22B |
|---|---|---|
| LMArena Text | — | 1162 |
| LMArena Creative Writing | — | 1141 |
| WildBench | — | 71.1% |
| LMArena Multi-Turn | — | 1130 |
Frequently asked questions
Is GPT-5.3 Codex better than Mixtral 8x22B?
GPT-5.3 Codex is the stronger model overall, scoring 45.5 to 26.7 on the Noometry Index. Mixtral 8x22B costs 1.6× less per token, which makes it the better buy when GPT-5.3 Codex's lead doesn't matter for your workload.
Which is cheaper, GPT-5.3 Codex or Mixtral 8x22B?
Mixtral 8x22B is cheaper. It lists at $2 per million input tokens and $6 per million output tokens; GPT-5.3 Codex lists at $1.75 and $14.
Is GPT-5.3 Codex or Mixtral 8x22B better for coding?
GPT-5.3 Codex scores higher on coding benchmarks: 48.5 versus 23.8 in the Noometry coding category.
Which has the bigger context window?
GPT-5.3 Codex does, with 400K tokens against 64K.
How many benchmarks do GPT-5.3 Codex and Mixtral 8x22B share?
2 benchmarks have published results for both models. GPT-5.3 Codex has 8 scored results on Noometry and Mixtral 8x22B has 34.