Model comparison
GPT-4 Turbo vs Pixtral Large
Pixtral Large is the stronger model overall, scoring 31.7 to 30.3 on the Noometry Index.
Last verified . 1 shared benchmarks.
Mistral AISummary
- They share 1 benchmark with published results for both. GPT-4 Turbo scores higher in 2 categories and Pixtral Large in 1 category; 2 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GPT-4 Turbo leads 47.9 to 32.9.
- Pixtral Large is cheaper at $2 / $6 per million input/output tokens, against $10 / $30 for GPT-4 Turbo.
- Pixtral Large has downloadable open weights; the other is API-only.
Side by side
| GPT-4 Turbo | Pixtral Large | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 30.3 | 31.7 |
| Released | 2023-11-06 | 2024-11-01 |
| Weights | Proprietary | Open |
| Context window | 128K | 128K |
| Max output | 4K | 128K |
| Input $ / M tokens | $10 | $2 |
| Output $ / M tokens | $30 | $6 |
| Results tracked | 36 | 3 |
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Category by category
Coding Not comparable
GPT-4 Turbo: 33.4 (#247), Pixtral Large: —
| Benchmark | GPT-4 Turbo | Pixtral Large |
|---|---|---|
| WeirdML | 18% | — |
| BigCodeBench Instruct | 48.2% | — |
| LMArena Coding | 1268 | — |
| BigCodeBench Complete | 58.2% | — |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73.3% | — |
Agentic & Tool Use Not comparable
GPT-4 Turbo: —, Pixtral Large: —
| Benchmark | GPT-4 Turbo | Pixtral Large |
|---|---|---|
| METR Time Horizons | 36.7% | — |
Reasoning Pixtral Large leads
GPT-4 Turbo: 14.9 (#311), Pixtral Large: 20.9 (#208)
| Benchmark | GPT-4 Turbo | Pixtral Large |
|---|---|---|
| SimpleBench | 25.1% | — |
| Chess Puzzles | 6% | — |
| EnigmaEval | — | 0.8% |
| LMArena Hard Prompts | 1251 | — |
| DTBench | 61.6% | — |
| LMCA | 9.8% | — |
| Epoch Capabilities Index | 127.25 | — |
| ForecastBench | 59.4 | — |
Math Not comparable
GPT-4 Turbo: 8.6 (#316), Pixtral Large: —
| Benchmark | GPT-4 Turbo | Pixtral Large |
|---|---|---|
| FrontierMath (Tiers 1-3) | 0.7% | — |
| OTIS Mock AIME 2024-2025 | 6.7% | — |
| LMArena Math | 1272 | — |
| MATH Level 5 | 46.7% | — |
Knowledge Not comparable
GPT-4 Turbo: 24.3 (#263), Pixtral Large: —
| Benchmark | GPT-4 Turbo | Pixtral Large |
|---|---|---|
| GPQA Diamond | 46.6% | — |
| Confabulations | 28.4% | — |
| LMArena Expert | 1223 | — |
| MMLU | 81.3% | — |
Multimodal Too close to call
GPT-4 Turbo: 30.0 (#104), Pixtral Large: 30.0 (#105)
| Benchmark | GPT-4 Turbo | Pixtral Large |
|---|---|---|
| LMArena Vision | 1090 | 1089 |
Multilingual Not comparable
GPT-4 Turbo: 40.7 (#211), Pixtral Large: —
| Benchmark | GPT-4 Turbo | Pixtral Large |
|---|---|---|
| LMArena Non-English | 1245 | — |
| LMArena Chinese | 1242 | — |
| LMArena French | 1275 | — |
| LMArena German | 1259 | — |
| LMArena Japanese | 1194 | — |
| LMArena Korean | 1187 | — |
| LMArena Russian | 1259 | — |
| LMArena Spanish | 1260 | — |
Instruction Following Not comparable
GPT-4 Turbo: 66.0 (#211), Pixtral Large: —
| Benchmark | GPT-4 Turbo | Pixtral Large |
|---|---|---|
| LMArena Instruction Following | 1250 | — |
Long Context Not comparable
GPT-4 Turbo: 38.0 (#200), Pixtral Large: —
| Benchmark | GPT-4 Turbo | Pixtral Large |
|---|---|---|
| LMArena Longer Query | 1254 | — |
Writing & Preference GPT-4 Turbo leads
GPT-4 Turbo: 47.9 (#201), Pixtral Large: 32.9 (#273)
| Benchmark | GPT-4 Turbo | Pixtral Large |
|---|---|---|
| LMArena Text | 1272 | — |
| LMArena Creative Writing | 1269 | — |
| EQ-Bench Creative Writing | — | 988 |
| LMArena Multi-Turn | 1267 | — |
Frequently asked questions
Is GPT-4 Turbo better than Pixtral Large?
Pixtral Large is the stronger model overall, scoring 31.7 to 30.3 on the Noometry Index.
Which is cheaper, GPT-4 Turbo or Pixtral Large?
Pixtral Large is cheaper. It lists at $2 per million input tokens and $6 per million output tokens; GPT-4 Turbo lists at $10 and $30.
Which has the bigger context window?
Both accept 128K tokens.
How many benchmarks do GPT-4 Turbo and Pixtral Large share?
1 benchmark has published results for both models. GPT-4 Turbo has 36 scored results on Noometry and Pixtral Large has 3.