Model comparison
GPT-4o mini vs Mistral Medium 3.5
Mistral Medium 3.5 is the stronger model overall, scoring 40.2 to 25.5 on the Noometry Index. GPT-4o mini costs 11× less per token, which makes it the better buy when Mistral Medium 3.5's lead doesn't matter for your workload.
Last verified . 19 shared benchmarks.
Mistral AISummary
- They share 19 benchmarks with published results for both. GPT-4o mini scores higher in 0 categories and Mistral Medium 3.5 in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Mistral Medium 3.5 leads 39.1 to 10.4.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 28.8% for GPT-4o mini and 41.4% for Mistral Medium 3.5.
- GPT-4o mini is cheaper at $0.15 / $0.60 per million input/output tokens, against $1.50 / $7.50 for Mistral Medium 3.5.
- Mistral Medium 3.5 accepts more context: 262K tokens versus 128K.
- Mistral Medium 3.5 has downloadable open weights; the other is API-only.
Side by side
| GPT-4o mini | Mistral Medium 3.5 | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 25.5 | 40.2 |
| Released | 2024-07-18 | — |
| Weights | Proprietary | Open |
| Context window | 128K | 262K |
| Max output | 16K | 210K |
| Input $ / M tokens | $0.15 | $1.50 |
| Output $ / M tokens | $0.60 | $7.50 |
| Results tracked | 60 | 22 |
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Category by category
Coding Mistral Medium 3.5 leads
GPT-4o mini: 22.0 (#335), Mistral Medium 3.5: 36.0 (#213)
| Benchmark | GPT-4o mini | Mistral Medium 3.5 |
|---|---|---|
| LMArena Coding | 1290 | 1461 |
| Aider Polyglot | 3.6% | — |
| LMArena WebDev | — | 1264 |
| WeirdML | 11.8% | — |
| BigCodeBench Instruct | 46.1% | — |
| LiveBench Coding | 43.1% | — |
| BigCodeBench Complete | 57.4% | — |
| HumanEval+ | 83.5% | — |
| MBPP+ | 72.2% | — |
Agentic & Tool Use Not comparable
GPT-4o mini: 27.5 (#101), Mistral Medium 3.5: —
| Benchmark | GPT-4o mini | Mistral Medium 3.5 |
|---|---|---|
| BALROG | 17.4% | — |
Reasoning Mistral Medium 3.5 leads
GPT-4o mini: 8.7 (#347), Mistral Medium 3.5: 17.3 (#295)
| Benchmark | GPT-4o mini | Mistral Medium 3.5 |
|---|---|---|
| Kagi LLM Benchmark | 28.8% | 41.4% |
| LMArena Hard Prompts | 1267 | 1436 |
| Epoch Capabilities Index | 126.56 | 141.35 |
| ARC-AGI-2 | 0% | — |
| SimpleBench | 10.7% | — |
| NYT Connections (extended) | — | 12.9% |
| Chess Puzzles | 0% | — |
| LiveBench Reasoning | 32.8% | — |
| Mystery Game Puzzles | 12% | — |
| DTBench | 54.4% | — |
| LiveBench Data Analysis | 50% | — |
| LMCA | 10.4% | — |
| LiveBench | 41.3% | — |
| PIQA | 88.7% | — |
Math Mistral Medium 3.5 leads
GPT-4o mini: 10.4 (#314), Mistral Medium 3.5: 39.1 (#113)
| Benchmark | GPT-4o mini | Mistral Medium 3.5 |
|---|---|---|
| LMArena Math | 1267 | 1431 |
| FrontierMath (Tiers 1-3) | 0.7% | — |
| OTIS Mock AIME 2024-2025 | 6.9% | — |
| Omni-MATH | 28% | — |
| LiveBench Math | 36.3% | — |
| MATH Level 5 | 52.6% | — |
| GSM8K | 91.3% | — |
Knowledge Mistral Medium 3.5 leads
GPT-4o mini: 17.7 (#284), Mistral Medium 3.5: 40.0 (#126)
| Benchmark | GPT-4o mini | Mistral Medium 3.5 |
|---|---|---|
| LMArena Expert | 1235 | 1432 |
| GPQA Diamond | 37.7% | — |
| SimpleQA Verified | 8.3% | — |
| MMLU-Pro | 60.3% | — |
| Confabulations | 37.2% | — |
| GPQA (HELM) | 36.8% | — |
| BoolQ | 88.7% | — |
| MMLU | 81.8% | — |
Multimodal Mistral Medium 3.5 leads
GPT-4o mini: 25.9 (#122), Mistral Medium 3.5: 38.3 (#65)
| Benchmark | GPT-4o mini | Mistral Medium 3.5 |
|---|---|---|
| LMArena Vision | 1066 | 1223 |
| Video-MME | 64.8% | — |
| GeoBench | 64% | — |
| VPCT | 34% | — |
Multilingual Mistral Medium 3.5 leads
GPT-4o mini: 42.0 (#199), Mistral Medium 3.5: 51.9 (#100)
| Benchmark | GPT-4o mini | Mistral Medium 3.5 |
|---|---|---|
| LMArena Non-English | 1266 | 1404 |
| LMArena Chinese | 1265 | 1442 |
| LMArena French | 1297 | 1448 |
| LMArena German | 1272 | 1451 |
| LMArena Korean | 1195 | 1385 |
| LMArena Russian | 1275 | 1395 |
| LMArena Spanish | 1276 | 1409 |
| LMArena Japanese | 1216 | — |
Instruction Following Mistral Medium 3.5 leads
GPT-4o mini: 61.9 (#239), Mistral Medium 3.5: 74.6 (#90)
| Benchmark | GPT-4o mini | Mistral Medium 3.5 |
|---|---|---|
| LMArena Instruction Following | 1258 | 1415 |
| LiveBench Instruction Following | 56.8% | — |
| IFEval | 78.2% | — |
Long Context Mistral Medium 3.5 leads
GPT-4o mini: 39.1 (#186), Mistral Medium 3.5: 43.2 (#103)
| Benchmark | GPT-4o mini | Mistral Medium 3.5 |
|---|---|---|
| LMArena Longer Query | 1289 | 1415 |
Writing & Preference Mistral Medium 3.5 leads
GPT-4o mini: 39.5 (#248), Mistral Medium 3.5: 58.5 (#117)
| Benchmark | GPT-4o mini | Mistral Medium 3.5 |
|---|---|---|
| LMArena Text | 1286 | 1421 |
| LMArena Creative Writing | 1268 | 1374 |
| LMArena Multi-Turn | 1285 | 1423 |
| Short-Story Creative Writing | 67.2% | — |
| EQ-Bench Creative Writing | 873 | — |
| WildBench | 79.1% | — |
| EQ-Bench 4 | — | 993 |
| LiveBench Language | 28.6% | — |
Frequently asked questions
Is GPT-4o mini better than Mistral Medium 3.5?
Mistral Medium 3.5 is the stronger model overall, scoring 40.2 to 25.5 on the Noometry Index. GPT-4o mini costs 11× less per token, which makes it the better buy when Mistral Medium 3.5's lead doesn't matter for your workload.
Which is cheaper, GPT-4o mini or Mistral Medium 3.5?
GPT-4o mini is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; Mistral Medium 3.5 lists at $1.50 and $7.50.
Is GPT-4o mini or Mistral Medium 3.5 better for coding?
Mistral Medium 3.5 scores higher on coding benchmarks: 36.0 versus 22.0 in the Noometry coding category.
Which has the bigger context window?
Mistral Medium 3.5 does, with 262K tokens against 128K.
How many benchmarks do GPT-4o mini and Mistral Medium 3.5 share?
19 benchmarks have published results for both models. GPT-4o mini has 60 scored results on Noometry and Mistral Medium 3.5 has 22.