Model comparison
GPT-5.4 mini vs Mistral 7B
GPT-5.4 mini is the stronger model overall, scoring 45.0 to 23.0 on the Noometry Index. Mistral 7B costs 6.8× less per token, which makes it the better buy when GPT-5.4 mini'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-5.4 mini scores higher in 8 categories and Mistral 7B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GPT-5.4 mini leads 51.5 to 7.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 88.9% for GPT-5.4 mini and 0.3% for Mistral 7B.
- Mistral 7B is cheaper at $0.25 / $0.25 per million input/output tokens, against $0.75 / $4.50 for GPT-5.4 mini.
- GPT-5.4 mini accepts more context: 400K tokens versus 8K.
- Mistral 7B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.4 mini | Mistral 7B | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 45.0 | 23.0 |
| Released | 2026-03-17 | 2023-09-27 |
| Weights | Proprietary | Open |
| Context window | 400K | 8K |
| Max output | 128K | 8K |
| Input $ / M tokens | $0.75 | $0.25 |
| Output $ / M tokens | $4.50 | $0.25 |
| Results tracked | 46 | 37 |
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Category by category
Coding GPT-5.4 mini leads
GPT-5.4 mini: 45.2 (#72), Mistral 7B: 26.4 (#326)
| Benchmark | GPT-5.4 mini | Mistral 7B |
|---|---|---|
| LMArena Coding | 1438 | 1082 |
| FrontierCode | 27% | — |
| LMArena WebDev | 1397 | — |
| SciCode | 49.9% | — |
| WeirdML | 60.3% | — |
| BigCodeBench Instruct | — | 19.5% |
| BigCodeBench Complete | — | 27.3% |
| ALE-Bench | 1,189 | — |
| HumanEval+ | — | 36% |
| MBPP+ | — | 42.1% |
Agentic & Tool Use Not comparable
GPT-5.4 mini: 29.9 (#81), Mistral 7B: —
| Benchmark | GPT-5.4 mini | Mistral 7B |
|---|---|---|
| DeepResearch Bench | 36.3% | — |
Reasoning GPT-5.4 mini leads
GPT-5.4 mini: 30.4 (#85), Mistral 7B: 13.1 (#336)
| Benchmark | GPT-5.4 mini | Mistral 7B |
|---|---|---|
| Chess Puzzles | 24% | 0% |
| LMArena Hard Prompts | 1424 | 1067 |
| DTBench | 80% | 42.5% |
| Epoch Capabilities Index | 148.84 | 112.21 |
| ARC-AGI-2 | 18.9% | — |
| Kagi LLM Benchmark | 37.9% | — |
| NYT Connections (extended) | 61.8% | — |
| ARC-AGI-1 | 63.7% | — |
| CritPt | 10% | — |
| Thematic Generalization | 61.7% | — |
| Mystery Game Puzzles | 11% | — |
| LMCA | 40.8% | — |
| Adversarial NLI | — | 47.1% |
| BIG-Bench Hard | — | 56.1% |
| ForecastBench | 57 | — |
| HellaSwag | — | 81% |
| PIQA | — | 83% |
| WinoGrande | — | 75.3% |
Math GPT-5.4 mini leads
GPT-5.4 mini: 45.5 (#75), Mistral 7B: 8.1 (#325)
| Benchmark | GPT-5.4 mini | Mistral 7B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 88.9% | 0.3% |
| LMArena Math | 1419 | 1085 |
| FrontierMath (Tiers 1-3) | 51.2% | — |
| FrontierMath Tier 4 | 9.8% | — |
| ProofBench | 21% | — |
| MATH Level 5 | — | 3.7% |
| FrontierMath (Feb 2025 set) | 28.3% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
| GSM8K | — | 54.4% |
Knowledge GPT-5.4 mini leads
GPT-5.4 mini: 51.5 (#67), Mistral 7B: 7.4 (#311)
| Benchmark | GPT-5.4 mini | Mistral 7B |
|---|---|---|
| GPQA Diamond | 86.9% | 15.2% |
| LMArena Expert | 1435 | 1036 |
| SimpleQA Verified | 29.4% | — |
| Vectara Hallucination Rate | 5.5% | — |
| ARC (AI2) Challenge | — | 78.6% |
| BoolQ | — | 87.4% |
| MMLU | — | 62.5% |
| OpenBookQA | — | 79.8% |
| TriviaQA | — | 75.2% |
Multimodal Not comparable
GPT-5.4 mini: 39.7 (#56), Mistral 7B: —
| Benchmark | GPT-5.4 mini | Mistral 7B |
|---|---|---|
| LMArena Vision | 1245 | — |
Multilingual GPT-5.4 mini leads
GPT-5.4 mini: 51.9 (#96), Mistral 7B: 25.8 (#283)
| Benchmark | GPT-5.4 mini | Mistral 7B |
|---|---|---|
| LMArena Non-English | 1405 | 1012 |
| LMArena Chinese | 1446 | 1009 |
| LMArena French | 1440 | 1037 |
| LMArena German | 1409 | 987 |
| LMArena Japanese | 1374 | 878 |
| LMArena Russian | 1417 | 1018 |
| LMArena Spanish | 1405 | 1026 |
| LMArena Korean | 1368 | — |
Instruction Following GPT-5.4 mini leads
GPT-5.4 mini: 74.1 (#102), Mistral 7B: 54.2 (#280)
| Benchmark | GPT-5.4 mini | Mistral 7B |
|---|---|---|
| LMArena Instruction Following | 1405 | 1060 |
Long Context GPT-5.4 mini leads
GPT-5.4 mini: 43.0 (#112), Mistral 7B: 32.2 (#271)
| Benchmark | GPT-5.4 mini | Mistral 7B |
|---|---|---|
| LMArena Longer Query | 1407 | 1060 |
Writing & Preference GPT-5.4 mini leads
GPT-5.4 mini: 64.0 (#58), Mistral 7B: 30.7 (#286)
| Benchmark | GPT-5.4 mini | Mistral 7B |
|---|---|---|
| LMArena Text | 1412 | 1090 |
| LMArena Creative Writing | 1370 | 1068 |
| LMArena Multi-Turn | 1429 | 1062 |
| EQ-Bench Creative Writing | 1665 | — |
Frequently asked questions
Is GPT-5.4 mini better than Mistral 7B?
GPT-5.4 mini is the stronger model overall, scoring 45.0 to 23.0 on the Noometry Index. Mistral 7B costs 6.8× less per token, which makes it the better buy when GPT-5.4 mini's lead doesn't matter for your workload.
Which is cheaper, GPT-5.4 mini or Mistral 7B?
Mistral 7B is cheaper. It lists at $0.25 per million input tokens and $0.25 per million output tokens; GPT-5.4 mini lists at $0.75 and $4.50.
Is GPT-5.4 mini or Mistral 7B better for coding?
GPT-5.4 mini scores higher on coding benchmarks: 45.2 versus 26.4 in the Noometry coding category.
Which has the bigger context window?
GPT-5.4 mini does, with 400K tokens against 8K.
How many benchmarks do GPT-5.4 mini and Mistral 7B share?
21 benchmarks have published results for both models. GPT-5.4 mini has 46 scored results on Noometry and Mistral 7B has 37.