Model comparison
GPT-5.2 vs Mixtral 8x7B
GPT-5.2 is the stronger model overall, scoring 54.1 to 26.8 on the Noometry Index. Mixtral 8x7B costs 6.9× less per token, which makes it the better buy when GPT-5.2's lead doesn't matter for your workload.
Last verified . 21 shared benchmarks.
Mistral AISummary
- They share 21 benchmarks with published results for both. GPT-5.2 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-5.2 leads 59.2 to 11.0.
- The biggest single-benchmark swing is GPQA Diamond: 91.4% for GPT-5.2 and 30.6% for Mixtral 8x7B.
- Mixtral 8x7B is cheaper at $0.70 / $0.70 per million input/output tokens, against $1.75 / $14 for GPT-5.2.
- GPT-5.2 accepts more context: 400K tokens versus 32K.
- Mixtral 8x7B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.2 | Mixtral 8x7B | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 54.1 | 26.8 |
| Released | 2025-12-11 | 2023-12-11 |
| Weights | Proprietary | Open |
| Context window | 400K | 32K |
| Max output | 128K | 32K |
| Input $ / M tokens | $1.75 | $0.70 |
| Output $ / M tokens | $14 | $0.70 |
| Results tracked | 67 | 38 |
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Category by category
Coding GPT-5.2 leads
GPT-5.2: 52.2 (#32), Mixtral 8x7B: 32.2 (#265)
| Benchmark | GPT-5.2 | Mixtral 8x7B |
|---|---|---|
| LMArena Coding | 1447 | 1127 |
| SWE-bench Verified | 73.8% | — |
| SWE-bench Verified (bash only) | 72.8% | — |
| LMArena WebDev | 1415 | — |
| SWE-bench Multilingual | 66.7% | — |
| GSO | 27.4% | — |
| WeirdML | 72.2% | — |
| ALE-Bench | 1,294 | — |
| AlgoTune | 2.05 | — |
| HumanEval+ | — | 39.6% |
| MBPP+ | — | 49.7% |
Agentic & Tool Use Not comparable
GPT-5.2: 40.0 (#24), Mixtral 8x7B: —
| Benchmark | GPT-5.2 | Mixtral 8x7B |
|---|---|---|
| Terminal-Bench | 64.9% | — |
| Berkeley Function Calling Leaderboard | 55.9% | — |
| GDPval | 49.7% | — |
| Remote Labor Index | 2.5% | — |
| τ²-bench Airline | 83% | — |
| τ²-bench Banking | 32.2% | — |
| τ²-bench Retail | 81.6% | — |
| τ²-bench Telecom | 89.7% | — |
| DeepResearch Bench | 41.1% | — |
| LMArena Search | 1207 | — |
| METR Time Horizons | 75.3% | — |
| Vending-Bench 2 | 3,591 | — |
Reasoning GPT-5.2 leads
GPT-5.2: 50.1 (#33), Mixtral 8x7B: 17.4 (#283)
| Benchmark | GPT-5.2 | Mixtral 8x7B |
|---|---|---|
| LMArena Hard Prompts | 1445 | 1115 |
| DTBench | 90.9% | 49.6% |
| Epoch Capabilities Index | 153.45 | 118.38 |
| ForecastBench | 60.1 | 56.3 |
| ARC-AGI-2 | 52.9% | — |
| SimpleBench | 45.8% | — |
| Kagi LLM Benchmark | 73.3% | — |
| NYT Connections (extended) | 83.6% | — |
| ARC-AGI-1 | 86.2% | — |
| Chess Puzzles | 49% | — |
| EnigmaEval | 10.4% | — |
| EBR-Bench | 23% | — |
| Mystery Game Puzzles | 23% | — |
| LMCA | 43.9% | — |
| Adversarial NLI | — | 55.2% |
| HellaSwag | — | 86.7% |
| PIQA | — | 83.6% |
| WinoGrande | — | 77.2% |
Math GPT-5.2 leads
GPT-5.2: 59.8 (#32), Mixtral 8x7B: 17.2 (#284)
| Benchmark | GPT-5.2 | Mixtral 8x7B |
|---|---|---|
| LMArena Math | 1439 | 1147 |
| FrontierMath (Tiers 1-3) | 67.4% | — |
| FrontierMath Tier 4 | 31.7% | — |
| MathArena Final-Answer Competitions | 72% | — |
| OTIS Mock AIME 2024-2025 | 96.1% | — |
| ProofBench | 15% | — |
| Omni-MATH | — | 10.5% |
| MATH Level 5 | — | 10% |
| FrontierMath (Feb 2025 set) | 40.7% | — |
| FrontierMath Tier 4 (v1) | 18.8% | — |
| GSM8K | — | 74.4% |
Knowledge GPT-5.2 leads
GPT-5.2: 59.2 (#29), Mixtral 8x7B: 11.0 (#296)
| Benchmark | GPT-5.2 | Mixtral 8x7B |
|---|---|---|
| GPQA Diamond | 91.4% | 30.6% |
| LMArena Expert | 1445 | 1088 |
| Humanity's Last Exam | 27.8% | — |
| SimpleQA Verified | 37.1% | — |
| MMLU-Pro | — | 33.5% |
| Vectara Hallucination Rate | 8.4% | — |
| GPQA (HELM) | — | 29.6% |
| ARC (AI2) Challenge | — | 87.3% |
| MMLU | — | 70.6% |
| OpenBookQA | — | 85.8% |
| TriviaQA | — | 82.2% |
Multimodal Not comparable
GPT-5.2: 50.9 (#7), Mixtral 8x7B: —
| Benchmark | GPT-5.2 | Mixtral 8x7B |
|---|---|---|
| LMArena Vision | 1268 | — |
| VPCT | 84% | — |
| Furniture Assembly | 38.3% | — |
| LMArena Document | 1405 | — |
Multilingual GPT-5.2 leads
GPT-5.2: 53.6 (#63), Mixtral 8x7B: 29.8 (#261)
| Benchmark | GPT-5.2 | Mixtral 8x7B |
|---|---|---|
| LMArena Non-English | 1425 | 1077 |
| LMArena Chinese | 1460 | 1055 |
| LMArena French | 1456 | 1165 |
| LMArena German | 1448 | 1114 |
| LMArena Japanese | 1421 | 931 |
| LMArena Korean | 1390 | 968 |
| LMArena Russian | 1440 | 1090 |
| LMArena Spanish | 1433 | 1110 |
Instruction Following GPT-5.2 leads
GPT-5.2: 74.9 (#83), Mixtral 8x7B: 51.2 (#292)
| Benchmark | GPT-5.2 | Mixtral 8x7B |
|---|---|---|
| LMArena Instruction Following | 1417 | 1110 |
| IFEval | — | 57.5% |
Long Context GPT-5.2 leads
GPT-5.2: 44.0 (#74), Mixtral 8x7B: 33.4 (#255)
| Benchmark | GPT-5.2 | Mixtral 8x7B |
|---|---|---|
| LMArena Longer Query | 1428 | 1103 |
| CL-bench | 18.2% | — |
Writing & Preference GPT-5.2 leads
GPT-5.2: 67.0 (#30), Mixtral 8x7B: 34.4 (#265)
| Benchmark | GPT-5.2 | Mixtral 8x7B |
|---|---|---|
| LMArena Text | 1439 | 1132 |
| LMArena Creative Writing | 1402 | 1109 |
| LMArena Multi-Turn | 1458 | 1115 |
| EQ-Bench Creative Writing | 1703 | — |
| WildBench | — | 67.3% |
Frequently asked questions
Is GPT-5.2 better than Mixtral 8x7B?
GPT-5.2 is the stronger model overall, scoring 54.1 to 26.8 on the Noometry Index. Mixtral 8x7B costs 6.9× less per token, which makes it the better buy when GPT-5.2's lead doesn't matter for your workload.
Which is cheaper, GPT-5.2 or Mixtral 8x7B?
Mixtral 8x7B is cheaper. It lists at $0.70 per million input tokens and $0.70 per million output tokens; GPT-5.2 lists at $1.75 and $14.
Is GPT-5.2 or Mixtral 8x7B better for coding?
GPT-5.2 scores higher on coding benchmarks: 52.2 versus 32.2 in the Noometry coding category.
Which has the bigger context window?
GPT-5.2 does, with 400K tokens against 32K.
How many benchmarks do GPT-5.2 and Mixtral 8x7B share?
21 benchmarks have published results for both models. GPT-5.2 has 67 scored results on Noometry and Mixtral 8x7B has 38.