Model comparison
GPT-4.1 vs Mixtral 8x7B
GPT-4.1 is the stronger model overall, scoring 35.9 to 27.1 on the Noometry Index. Mixtral 8x7B costs 5.0× less per token, which makes it the better buy when GPT-4.1's lead doesn't matter for your workload.
Last verified . 27 shared benchmarks.
Mistral AISummary
- They share 27 benchmarks with published results for both. GPT-4.1 scores higher in 7 categories and Mixtral 8x7B in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GPT-4.1 leads 37.1 to 11.0.
- The biggest single-benchmark swing is MATH Level 5: 83% for GPT-4.1 and 10% for Mixtral 8x7B.
- Mixtral 8x7B is cheaper at $0.70 / $0.70 per million input/output tokens, against $2 / $8 for GPT-4.1.
- GPT-4.1 accepts more context: 1.05M tokens versus 32K.
- Mixtral 8x7B has downloadable open weights; the other is API-only.
Side by side
| GPT-4.1 | Mixtral 8x7B | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 35.9 | 27.1 |
| Released | 2025-04-14 | 2023-12-11 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 32K |
| Max output | 33K | 32K |
| Input $ / M tokens | $2 | $0.70 |
| Output $ / M tokens | $8 | $0.70 |
| Results tracked | 52 | 38 |
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Category by category
Coding GPT-4.1 leads
GPT-4.1: 34.4 (#238), Mixtral 8x7B: 32.8 (#269)
| Benchmark | GPT-4.1 | Mixtral 8x7B |
|---|---|---|
| LMArena Coding | 1391 | 1126 |
| SWE-bench Verified | 48.5% | — |
| SWE-bench Verified (bash only) | 39.6% | — |
| Aider Polyglot | 52.4% | — |
| WeirdML | 39% | — |
| CadEval | 42% | — |
| ALE-Bench | 558.1 | — |
| HumanEval+ | — | 39.6% |
| MBPP+ | — | 49.7% |
Agentic & Tool Use Not comparable
GPT-4.1: 34.7 (#43), Mixtral 8x7B: —
| Benchmark | GPT-4.1 | Mixtral 8x7B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 54% | — |
Reasoning Mixtral 8x7B leads
GPT-4.1: 11.7 (#339), Mixtral 8x7B: 18.2 (#285)
| Benchmark | GPT-4.1 | Mixtral 8x7B |
|---|---|---|
| LMArena Hard Prompts | 1384 | 1115 |
| DTBench | 68.3% | 49.6% |
| Epoch Capabilities Index | 136.78 | 118.47 |
| ForecastBench | 61.5 | 56.3 |
| ARC-AGI-2 | 0.4% | — |
| SimpleBench | 27% | — |
| Kagi LLM Benchmark | 52.3% | — |
| ARC-AGI-1 | 5.5% | — |
| Chess Puzzles | 6% | — |
| EnigmaEval | 2.2% | — |
| LMCA | 25.6% | — |
| Adversarial NLI | — | 55.2% |
| HellaSwag | — | 86.7% |
| PIQA | — | 83.6% |
| WinoGrande | — | 77.2% |
Math GPT-4.1 leads
GPT-4.1: 22.3 (#280), Mixtral 8x7B: 18.8 (#289)
| Benchmark | GPT-4.1 | Mixtral 8x7B |
|---|---|---|
| Omni-MATH | 47.1% | 10.5% |
| LMArena Math | 1370 | 1147 |
| MATH Level 5 | 83% | 10% |
| FrontierMath (Tiers 1-3) | 6% | — |
| OTIS Mock AIME 2024-2025 | 38.3% | — |
| FrontierMath (Feb 2025 set) | 5.5% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
| GSM8K | — | 74.4% |
Knowledge GPT-4.1 leads
GPT-4.1: 37.1 (#160), Mixtral 8x7B: 11.0 (#301)
| Benchmark | GPT-4.1 | Mixtral 8x7B |
|---|---|---|
| GPQA Diamond | 66.9% | 30.6% |
| MMLU-Pro | 81.1% | 33.5% |
| GPQA (HELM) | 65.9% | 29.6% |
| LMArena Expert | 1364 | 1088 |
| Humanity's Last Exam | 5.4% | — |
| SimpleQA Verified | 31.1% | — |
| Vectara Hallucination Rate | 5.6% | — |
| ARC (AI2) Challenge | — | 87.3% |
| MMLU | — | 70.6% |
| OpenBookQA | — | 85.8% |
| TriviaQA | — | 82.2% |
Multimodal Not comparable
GPT-4.1: 38.2 (#67), Mixtral 8x7B: —
| Benchmark | GPT-4.1 | Mixtral 8x7B |
|---|---|---|
| LMArena Vision | 1211 | — |
| GeoBench | 72% | — |
Multilingual GPT-4.1 leads
GPT-4.1: 49.4 (#133), Mixtral 8x7B: 29.6 (#266)
| Benchmark | GPT-4.1 | Mixtral 8x7B |
|---|---|---|
| LMArena Non-English | 1370 | 1077 |
| LMArena Chinese | 1382 | 1055 |
| LMArena French | 1382 | 1166 |
| LMArena German | 1381 | 1114 |
| LMArena Japanese | 1319 | 931 |
| LMArena Korean | 1339 | 968 |
| LMArena Russian | 1377 | 1090 |
| LMArena Spanish | 1376 | 1111 |
Instruction Following GPT-4.1 leads
GPT-4.1: 71.3 (#153), Mixtral 8x7B: 51.0 (#297)
| Benchmark | GPT-4.1 | Mixtral 8x7B |
|---|---|---|
| IFEval | 83.8% | 57.5% |
| LMArena Instruction Following | 1367 | 1109 |
Long Context GPT-4.1 leads
GPT-4.1: 40.0 (#163), Mixtral 8x7B: 33.4 (#260)
| Benchmark | GPT-4.1 | Mixtral 8x7B |
|---|---|---|
| LMArena Longer Query | 1385 | 1103 |
| Fiction.LiveBench | 63.9% | — |
Writing & Preference GPT-4.1 leads
GPT-4.1: 57.6 (#125), Mixtral 8x7B: 34.2 (#270)
| Benchmark | GPT-4.1 | Mixtral 8x7B |
|---|---|---|
| LMArena Text | 1383 | 1132 |
| LMArena Creative Writing | 1363 | 1109 |
| WildBench | 85.4% | 67.3% |
| LMArena Multi-Turn | 1398 | 1115 |
| EQ-Bench Creative Writing | 1420 | — |
Frequently asked questions
Is GPT-4.1 better than Mixtral 8x7B?
GPT-4.1 is the stronger model overall, scoring 35.9 to 27.1 on the Noometry Index. Mixtral 8x7B costs 5.0× less per token, which makes it the better buy when GPT-4.1's lead doesn't matter for your workload.
Which is cheaper, GPT-4.1 or Mixtral 8x7B?
Mixtral 8x7B is cheaper. It lists at $0.70 per million input tokens and $0.70 per million output tokens; GPT-4.1 lists at $2 and $8.
Is GPT-4.1 or Mixtral 8x7B better for coding?
GPT-4.1 scores higher on coding benchmarks: 34.4 versus 32.8 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 does, with 1.05M tokens against 32K.
How many benchmarks do GPT-4.1 and Mixtral 8x7B share?
27 benchmarks have published results for both models. GPT-4.1 has 52 scored results on Noometry and Mixtral 8x7B has 38.