Model comparison
GPT-5 Mini vs Mistral Large 4
Mistral Large 4 is the stronger model overall, scoring 43.1 to 41.8 on the Noometry Index. GPT-5 Mini costs 1.5× less per token, which makes it the better buy when Mistral Large 4's lead doesn't matter for your workload.
Last verified . 13 shared benchmarks.
Mistral AISummary
- They share 13 benchmarks with published results for both. GPT-5 Mini scores higher in 4 categories and Mistral Large 4 in 4 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GPT-5 Mini leads 45.6 to 36.6.
- GPT-5 Mini is cheaper at $0.25 / $2 per million input/output tokens, against $0.68 / $2.09 for Mistral Large 4.
- Mistral Large 4 accepts more context: 1.05M tokens versus 400K.
Side by side
| GPT-5 Mini | Mistral Large 4 | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 41.8 | 43.1 |
| Released | 2025-08-07 | 2026-10-06 |
| Weights | Proprietary | Proprietary |
| Context window | 400K | 1.05M |
| Max output | 128K | 262K |
| Input $ / M tokens | $0.25 | $0.68 |
| Output $ / M tokens | $2 | $2.09 |
| Results tracked | 60 | 15 |
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Category by category
Coding Mistral Large 4 leads
GPT-5 Mini: 40.1 (#146), Mistral Large 4: 48.6 (#57)
| Benchmark | GPT-5 Mini | Mistral Large 4 |
|---|---|---|
| LMArena Coding | 1406 | 1475 |
| SWE-bench Verified | 64.7% | — |
| SWE-bench Verified (bash only) | 59.8% | — |
| LMArena WebDev | — | 1541 |
| SWE-bench Multilingual | 39.7% | — |
| SciCode | 39.2% | — |
| WeirdML | 52.7% | — |
| ALE-Bench | 799.77 | — |
| AlgoTune | 1.38 | — |
Agentic & Tool Use Not comparable
GPT-5 Mini: 31.1 (#70), Mistral Large 4: —
| Benchmark | GPT-5 Mini | Mistral Large 4 |
|---|---|---|
| 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), Mistral Large 4: 22.5 (#192)
| Benchmark | GPT-5 Mini | Mistral Large 4 |
|---|---|---|
| LMArena Hard Prompts | 1380 | 1444 |
| ARC-AGI-2 | 4.4% | — |
| Kagi LLM Benchmark | 70.3% | — |
| NYT Connections (extended) | — | 27.4% |
| ARC-AGI-1 | 54.3% | — |
| CritPt | 0% | — |
| Chess Puzzles | 30% | — |
| EnigmaEval | 8.2% | — |
| Mystery Game Puzzles | 10% | — |
| DTBench | 80.5% | — |
| LMCA | 34.2% | — |
| Epoch Capabilities Index | 145.52 | — |
| ForecastBench | 61 | — |
Math GPT-5 Mini leads
GPT-5 Mini: 46.7 (#69), Mistral Large 4: 40.4 (#91)
| Benchmark | GPT-5 Mini | Mistral Large 4 |
|---|---|---|
| LMArena Math | 1378 | 1488 |
| FrontierMath (Tiers 1-3) | 46.7% | — |
| FrontierMath Tier 4 | 12.2% | — |
| OTIS Mock AIME 2024-2025 | 86.7% | — |
| ProofBench | 9% | — |
| Omni-MATH | 72.2% | — |
| MATH Level 5 | 97.8% | — |
| FrontierMath (Feb 2025 set) | 27.2% | — |
| FrontierMath Tier 4 (v1) | 6.3% | — |
Knowledge GPT-5 Mini leads
GPT-5 Mini: 45.6 (#86), Mistral Large 4: 36.6 (#166)
| Benchmark | GPT-5 Mini | Mistral Large 4 |
|---|---|---|
| SimpleQA Verified | 21.6% | 20% |
| LMArena Expert | 1379 | 1447 |
| GPQA Diamond | 75% | — |
| Humanity's Last Exam | 19.4% | — |
| MMLU-Pro | 83.5% | — |
| Confabulations | 13.3% | — |
| Vectara Hallucination Rate | 12.9% | — |
| GPQA (HELM) | 75.6% | — |
Multimodal Not comparable
GPT-5 Mini: 35.6 (#85), Mistral Large 4: —
| Benchmark | GPT-5 Mini | Mistral Large 4 |
|---|---|---|
| LMArena Vision | 1202 | — |
| VPCT | 40.2% | — |
Multilingual Mistral Large 4 leads
GPT-5 Mini: 48.9 (#137), Mistral Large 4: 52.6 (#82)
| Benchmark | GPT-5 Mini | Mistral Large 4 |
|---|---|---|
| LMArena Non-English | 1363 | 1415 |
| LMArena Chinese | 1385 | 1491 |
| LMArena Russian | 1362 | 1414 |
| LMArena French | 1386 | — |
| LMArena German | 1366 | — |
| LMArena Japanese | 1341 | — |
| LMArena Korean | 1308 | — |
| LMArena Spanish | 1355 | — |
Instruction Following GPT-5 Mini leads
GPT-5 Mini: 76.2 (#46), Mistral Large 4: 75.0 (#76)
| Benchmark | GPT-5 Mini | Mistral Large 4 |
|---|---|---|
| LMArena Instruction Following | 1357 | 1424 |
| IFEval | 92.7% | — |
Long Context Mistral Large 4 leads
GPT-5 Mini: 41.9 (#132), Mistral Large 4: 43.6 (#89)
| Benchmark | GPT-5 Mini | Mistral Large 4 |
|---|---|---|
| LMArena Longer Query | 1355 | 1429 |
| Fiction.LiveBench | 69.4% | — |
Writing & Preference Mistral Large 4 leads
GPT-5 Mini: 55.2 (#148), Mistral Large 4: 60.4 (#97)
| Benchmark | GPT-5 Mini | Mistral Large 4 |
|---|---|---|
| LMArena Text | 1373 | 1427 |
| LMArena Creative Writing | 1325 | 1361 |
| LMArena Multi-Turn | 1363 | 1424 |
| Short-Story Creative Writing | 83.1% | — |
| EQ-Bench Creative Writing | 1313 | — |
| WildBench | 85.5% | — |
Frequently asked questions
Is GPT-5 Mini better than Mistral Large 4?
Mistral Large 4 is the stronger model overall, scoring 43.1 to 41.8 on the Noometry Index. GPT-5 Mini costs 1.5× less per token, which makes it the better buy when Mistral Large 4's lead doesn't matter for your workload.
Which is cheaper, GPT-5 Mini or Mistral Large 4?
GPT-5 Mini is cheaper. It lists at $0.25 per million input tokens and $2 per million output tokens; Mistral Large 4 lists at $0.68 and $2.09.
Is GPT-5 Mini or Mistral Large 4 better for coding?
Mistral Large 4 scores higher on coding benchmarks: 48.6 versus 40.1 in the Noometry coding category.
Which has the bigger context window?
Mistral Large 4 does, with 1.05M tokens against 400K.
How many benchmarks do GPT-5 Mini and Mistral Large 4 share?
13 benchmarks have published results for both models. GPT-5 Mini has 60 scored results on Noometry and Mistral Large 4 has 15.