Model comparison
GPT-5 Mini vs Mixtral 8x7B
GPT-5 Mini is the stronger model overall, scoring 41.8 to 27.1 on the Noometry Index.
Last verified . 27 shared benchmarks.
Mistral AISummary
- They share 27 benchmarks with published results for both. GPT-5 Mini scores higher in 8 categories and Mixtral 8x7B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GPT-5 Mini leads 45.6 to 11.0.
- The biggest single-benchmark swing is MATH Level 5: 97.8% for GPT-5 Mini and 10% for Mixtral 8x7B.
- Both cost about the same: $0.25 input and $2 output per million tokens.
- GPT-5 Mini accepts more context: 400K tokens versus 32K.
- Mixtral 8x7B has downloadable open weights; the other is API-only.
Side by side
| GPT-5 Mini | Mixtral 8x7B | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 41.8 | 27.1 |
| Released | 2025-08-07 | 2023-12-11 |
| Weights | Proprietary | Open |
| Context window | 400K | 32K |
| Max output | 128K | 32K |
| Input $ / M tokens | $0.25 | $0.70 |
| Output $ / M tokens | $2 | $0.70 |
| Results tracked | 60 | 38 |
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Category by category
Coding GPT-5 Mini leads
GPT-5 Mini: 40.1 (#146), Mixtral 8x7B: 32.8 (#269)
| Benchmark | GPT-5 Mini | Mixtral 8x7B |
|---|---|---|
| LMArena Coding | 1406 | 1126 |
| SWE-bench Verified | 64.7% | — |
| SWE-bench Verified (bash only) | 59.8% | — |
| SWE-bench Multilingual | 39.7% | — |
| SciCode | 39.2% | — |
| WeirdML | 52.7% | — |
| ALE-Bench | 799.77 | — |
| AlgoTune | 1.38 | — |
| HumanEval+ | — | 39.6% |
| MBPP+ | — | 49.7% |
Agentic & Tool Use Not comparable
GPT-5 Mini: 31.1 (#70), Mixtral 8x7B: —
| Benchmark | GPT-5 Mini | Mixtral 8x7B |
|---|---|---|
| Terminal-Bench | 34.8% | — |
| Berkeley Function Calling Leaderboard | 55.5% | — |
| Vending-Bench 2 | -31.18 | — |
Reasoning GPT-5 Mini leads
GPT-5 Mini: 23.9 (#168), Mixtral 8x7B: 18.2 (#285)
| Benchmark | GPT-5 Mini | Mixtral 8x7B |
|---|---|---|
| LMArena Hard Prompts | 1380 | 1115 |
| DTBench | 80.5% | 49.6% |
| Epoch Capabilities Index | 145.52 | 118.47 |
| ForecastBench | 61 | 56.3 |
| ARC-AGI-2 | 4.4% | — |
| Kagi LLM Benchmark | 70.3% | — |
| ARC-AGI-1 | 54.3% | — |
| CritPt | 0% | — |
| Chess Puzzles | 30% | — |
| EnigmaEval | 8.2% | — |
| Mystery Game Puzzles | 10% | — |
| LMCA | 34.2% | — |
| Adversarial NLI | — | 55.2% |
| HellaSwag | — | 86.7% |
| PIQA | — | 83.6% |
| WinoGrande | — | 77.2% |
Math GPT-5 Mini leads
GPT-5 Mini: 46.7 (#69), Mixtral 8x7B: 18.8 (#289)
| Benchmark | GPT-5 Mini | Mixtral 8x7B |
|---|---|---|
| Omni-MATH | 72.2% | 10.5% |
| LMArena Math | 1378 | 1147 |
| MATH Level 5 | 97.8% | 10% |
| FrontierMath (Tiers 1-3) | 46.7% | — |
| FrontierMath Tier 4 | 12.2% | — |
| OTIS Mock AIME 2024-2025 | 86.7% | — |
| ProofBench | 9% | — |
| FrontierMath (Feb 2025 set) | 27.2% | — |
| FrontierMath Tier 4 (v1) | 6.3% | — |
| GSM8K | — | 74.4% |
Knowledge GPT-5 Mini leads
GPT-5 Mini: 45.6 (#86), Mixtral 8x7B: 11.0 (#301)
| Benchmark | GPT-5 Mini | Mixtral 8x7B |
|---|---|---|
| GPQA Diamond | 75% | 30.6% |
| MMLU-Pro | 83.5% | 33.5% |
| GPQA (HELM) | 75.6% | 29.6% |
| LMArena Expert | 1379 | 1088 |
| Humanity's Last Exam | 19.4% | — |
| SimpleQA Verified | 21.6% | — |
| Confabulations | 13.3% | — |
| Vectara Hallucination Rate | 12.9% | — |
| ARC (AI2) Challenge | — | 87.3% |
| MMLU | — | 70.6% |
| OpenBookQA | — | 85.8% |
| TriviaQA | — | 82.2% |
Multimodal Not comparable
GPT-5 Mini: 35.6 (#85), Mixtral 8x7B: —
| Benchmark | GPT-5 Mini | Mixtral 8x7B |
|---|---|---|
| LMArena Vision | 1202 | — |
| VPCT | 40.2% | — |
Multilingual GPT-5 Mini leads
GPT-5 Mini: 48.9 (#137), Mixtral 8x7B: 29.6 (#266)
| Benchmark | GPT-5 Mini | Mixtral 8x7B |
|---|---|---|
| LMArena Non-English | 1363 | 1077 |
| LMArena Chinese | 1385 | 1055 |
| LMArena French | 1386 | 1166 |
| LMArena German | 1366 | 1114 |
| LMArena Japanese | 1341 | 931 |
| LMArena Korean | 1308 | 968 |
| LMArena Russian | 1362 | 1090 |
| LMArena Spanish | 1355 | 1111 |
Instruction Following GPT-5 Mini leads
GPT-5 Mini: 76.2 (#46), Mixtral 8x7B: 51.0 (#297)
| Benchmark | GPT-5 Mini | Mixtral 8x7B |
|---|---|---|
| IFEval | 92.7% | 57.5% |
| LMArena Instruction Following | 1357 | 1109 |
Long Context GPT-5 Mini leads
GPT-5 Mini: 41.9 (#132), Mixtral 8x7B: 33.4 (#260)
| Benchmark | GPT-5 Mini | Mixtral 8x7B |
|---|---|---|
| LMArena Longer Query | 1355 | 1103 |
| Fiction.LiveBench | 69.4% | — |
Writing & Preference GPT-5 Mini leads
GPT-5 Mini: 55.2 (#148), Mixtral 8x7B: 34.2 (#270)
| Benchmark | GPT-5 Mini | Mixtral 8x7B |
|---|---|---|
| LMArena Text | 1373 | 1132 |
| LMArena Creative Writing | 1325 | 1109 |
| WildBench | 85.5% | 67.3% |
| LMArena Multi-Turn | 1363 | 1115 |
| Short-Story Creative Writing | 83.1% | — |
| EQ-Bench Creative Writing | 1313 | — |
Frequently asked questions
Is GPT-5 Mini better than Mixtral 8x7B?
GPT-5 Mini is the stronger model overall, scoring 41.8 to 27.1 on the Noometry Index.
Which is cheaper, GPT-5 Mini or Mixtral 8x7B?
GPT-5 Mini is cheaper. It lists at $0.25 per million input tokens and $2 per million output tokens; Mixtral 8x7B lists at $0.70 and $0.70.
Is GPT-5 Mini or Mixtral 8x7B better for coding?
GPT-5 Mini scores higher on coding benchmarks: 40.1 versus 32.8 in the Noometry coding category.
Which has the bigger context window?
GPT-5 Mini does, with 400K tokens against 32K.
How many benchmarks do GPT-5 Mini and Mixtral 8x7B share?
27 benchmarks have published results for both models. GPT-5 Mini has 60 scored results on Noometry and Mixtral 8x7B has 38.