Model comparison
GPT-4.1 vs Mistral 7B
GPT-4.1 is the stronger model overall, scoring 35.9 to 23.0 on the Noometry Index. Mistral 7B costs 14× less per token, which makes it the better buy when GPT-4.1's lead doesn't matter for your workload.
Last verified . 22 shared benchmarks.
Mistral AISummary
- They share 22 benchmarks with published results for both. GPT-4.1 scores higher in 7 categories and Mistral 7B in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GPT-4.1 leads 37.1 to 7.4.
- The biggest single-benchmark swing is MATH Level 5: 83% for GPT-4.1 and 3.7% for Mistral 7B.
- Mistral 7B is cheaper at $0.25 / $0.25 per million input/output tokens, against $2 / $8 for GPT-4.1.
- GPT-4.1 accepts more context: 1.05M tokens versus 8K.
- Mistral 7B has downloadable open weights; the other is API-only.
Side by side
| GPT-4.1 | Mistral 7B | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 35.9 | 23.0 |
| Released | 2025-04-14 | 2023-09-27 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 8K |
| Max output | 33K | 8K |
| Input $ / M tokens | $2 | $0.25 |
| Output $ / M tokens | $8 | $0.25 |
| Results tracked | 52 | 37 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GPT-4.1 leads
GPT-4.1: 34.4 (#238), Mistral 7B: 26.4 (#326)
| Benchmark | GPT-4.1 | Mistral 7B |
|---|---|---|
| LMArena Coding | 1391 | 1082 |
| SWE-bench Verified | 48.5% | — |
| SWE-bench Verified (bash only) | 39.6% | — |
| Aider Polyglot | 52.4% | — |
| WeirdML | 39% | — |
| BigCodeBench Instruct | — | 19.5% |
| BigCodeBench Complete | — | 27.3% |
| CadEval | 42% | — |
| ALE-Bench | 558.1 | — |
| HumanEval+ | — | 36% |
| MBPP+ | — | 42.1% |
Agentic & Tool Use Not comparable
GPT-4.1: 34.7 (#43), Mistral 7B: —
| Benchmark | GPT-4.1 | Mistral 7B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 54% | — |
Reasoning Mistral 7B leads
GPT-4.1: 11.7 (#339), Mistral 7B: 13.1 (#336)
| Benchmark | GPT-4.1 | Mistral 7B |
|---|---|---|
| Chess Puzzles | 6% | 0% |
| LMArena Hard Prompts | 1384 | 1067 |
| DTBench | 68.3% | 42.5% |
| Epoch Capabilities Index | 136.78 | 112.21 |
| ARC-AGI-2 | 0.4% | — |
| SimpleBench | 27% | — |
| Kagi LLM Benchmark | 52.3% | — |
| ARC-AGI-1 | 5.5% | — |
| EnigmaEval | 2.2% | — |
| LMCA | 25.6% | — |
| Adversarial NLI | — | 47.1% |
| BIG-Bench Hard | — | 56.1% |
| ForecastBench | 61.5 | — |
| HellaSwag | — | 81% |
| PIQA | — | 83% |
| WinoGrande | — | 75.3% |
Math GPT-4.1 leads
GPT-4.1: 22.3 (#280), Mistral 7B: 8.1 (#325)
| Benchmark | GPT-4.1 | Mistral 7B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 38.3% | 0.3% |
| LMArena Math | 1370 | 1085 |
| MATH Level 5 | 83% | 3.7% |
| FrontierMath (Tiers 1-3) | 6% | — |
| Omni-MATH | 47.1% | — |
| FrontierMath (Feb 2025 set) | 5.5% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
| GSM8K | — | 54.4% |
Knowledge GPT-4.1 leads
GPT-4.1: 37.1 (#160), Mistral 7B: 7.4 (#311)
| Benchmark | GPT-4.1 | Mistral 7B |
|---|---|---|
| GPQA Diamond | 66.9% | 15.2% |
| LMArena Expert | 1364 | 1036 |
| Humanity's Last Exam | 5.4% | — |
| SimpleQA Verified | 31.1% | — |
| MMLU-Pro | 81.1% | — |
| Vectara Hallucination Rate | 5.6% | — |
| GPQA (HELM) | 65.9% | — |
| ARC (AI2) Challenge | — | 78.6% |
| BoolQ | — | 87.4% |
| MMLU | — | 62.5% |
| OpenBookQA | — | 79.8% |
| TriviaQA | — | 75.2% |
Multimodal Not comparable
GPT-4.1: 38.2 (#67), Mistral 7B: —
| Benchmark | GPT-4.1 | Mistral 7B |
|---|---|---|
| LMArena Vision | 1211 | — |
| GeoBench | 72% | — |
Multilingual GPT-4.1 leads
GPT-4.1: 49.4 (#133), Mistral 7B: 25.8 (#283)
| Benchmark | GPT-4.1 | Mistral 7B |
|---|---|---|
| LMArena Non-English | 1370 | 1012 |
| LMArena Chinese | 1382 | 1009 |
| LMArena French | 1382 | 1037 |
| LMArena German | 1381 | 987 |
| LMArena Japanese | 1319 | 878 |
| LMArena Russian | 1377 | 1018 |
| LMArena Spanish | 1376 | 1026 |
| LMArena Korean | 1339 | — |
Instruction Following GPT-4.1 leads
GPT-4.1: 71.3 (#153), Mistral 7B: 54.2 (#280)
| Benchmark | GPT-4.1 | Mistral 7B |
|---|---|---|
| LMArena Instruction Following | 1367 | 1060 |
| IFEval | 83.8% | — |
Long Context GPT-4.1 leads
GPT-4.1: 40.0 (#163), Mistral 7B: 32.2 (#271)
| Benchmark | GPT-4.1 | Mistral 7B |
|---|---|---|
| LMArena Longer Query | 1385 | 1060 |
| Fiction.LiveBench | 63.9% | — |
Writing & Preference GPT-4.1 leads
GPT-4.1: 57.6 (#125), Mistral 7B: 30.7 (#286)
| Benchmark | GPT-4.1 | Mistral 7B |
|---|---|---|
| LMArena Text | 1383 | 1090 |
| LMArena Creative Writing | 1363 | 1068 |
| LMArena Multi-Turn | 1398 | 1062 |
| EQ-Bench Creative Writing | 1420 | — |
| WildBench | 85.4% | — |
Frequently asked questions
Is GPT-4.1 better than Mistral 7B?
GPT-4.1 is the stronger model overall, scoring 35.9 to 23.0 on the Noometry Index. Mistral 7B costs 14× 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 Mistral 7B?
Mistral 7B is cheaper. It lists at $0.25 per million input tokens and $0.25 per million output tokens; GPT-4.1 lists at $2 and $8.
Is GPT-4.1 or Mistral 7B better for coding?
GPT-4.1 scores higher on coding benchmarks: 34.4 versus 26.4 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 does, with 1.05M tokens against 8K.
How many benchmarks do GPT-4.1 and Mistral 7B share?
22 benchmarks have published results for both models. GPT-4.1 has 52 scored results on Noometry and Mistral 7B has 37.