Model comparison
DeepSeek V4 Pro vs Pixtral Large
DeepSeek V4 Pro is the stronger model overall, scoring 54.3 to 32.2 on the Noometry Index.
Last verified . 1 shared benchmarks.
DeepSeek
Mistral AISummary
- They share 1 benchmark with published results for both. DeepSeek V4 Pro scores higher in 2 categories and Pixtral Large in 0 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek V4 Pro leads 56.5 to 21.7.
- DeepSeek V4 Pro is cheaper at $0.66 / $1.98 per million input/output tokens, against $2 / $6 for Pixtral Large.
- DeepSeek V4 Pro accepts more context: 1M tokens versus 128K.
Side by side
| DeepSeek V4 Pro | Pixtral Large | |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 54.3 | 32.2 |
| Released | 2026-04-24 | 2024-11-01 |
| Weights | Open | Open |
| Context window | 1M | 128K |
| Max output | 393K | 128K |
| Input $ / M tokens | $0.66 | $2 |
| Output $ / M tokens | $1.98 | $6 |
| Results tracked | 48 | 3 |
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Category by category
Coding Not comparable
DeepSeek V4 Pro: 52.4 (#34), Pixtral Large: —
| Benchmark | DeepSeek V4 Pro | Pixtral Large |
|---|---|---|
| SWE-bench Verified | 77.6% | — |
| FrontierCode | 28.6% | — |
| LMArena WebDev | 1582 | — |
| SciCode | 51% | — |
| WeirdML | 66.2% | — |
| LMArena Coding | 1470 | — |
| ALE-Bench | 1,403 | — |
Agentic & Tool Use Not comparable
DeepSeek V4 Pro: 32.8 (#58), Pixtral Large: —
| Benchmark | DeepSeek V4 Pro | Pixtral Large |
|---|---|---|
| APEX-Agents | 47.3% | — |
| Vending-Bench 2 | 3,285 | — |
Reasoning DeepSeek V4 Pro leads
DeepSeek V4 Pro: 56.5 (#24), Pixtral Large: 21.7 (#218)
| Benchmark | DeepSeek V4 Pro | Pixtral Large |
|---|---|---|
| ARC-AGI-2 | 61.3% | — |
| Kagi LLM Benchmark | 53.5% | — |
| NYT Connections (extended) | 91.3% | — |
| ARC-AGI-1 | 90.5% | — |
| CritPt | 18% | — |
| Chess Puzzles | 47% | — |
| EnigmaEval | — | 0.8% |
| LMArena Hard Prompts | 1461 | — |
| Mystery Game Puzzles | 43% | — |
| DTBench | 93.9% | — |
| LMCA | 45.5% | — |
| Surface Evolver Bench | 40% | — |
| Epoch Capabilities Index | 155.31 | — |
| ForecastBench | 56.1 | — |
Math Not comparable
DeepSeek V4 Pro: 64.8 (#30), Pixtral Large: —
| Benchmark | DeepSeek V4 Pro | Pixtral Large |
|---|---|---|
| FrontierMath (Tiers 1-3) | 64.6% | — |
| FrontierMath Tier 4 | 26.8% | — |
| MathArena Final-Answer Competitions | 76.6% | — |
| OTIS Mock AIME 2024-2025 | 98.6% | — |
| ProofBench | 50% | — |
| LMArena Math | 1455 | — |
Knowledge Not comparable
DeepSeek V4 Pro: 59.5 (#31), Pixtral Large: —
| Benchmark | DeepSeek V4 Pro | Pixtral Large |
|---|---|---|
| GPQA Diamond | 91.7% | — |
| SimpleQA Verified | 52.9% | — |
| Vectara Hallucination Rate | 8.6% | — |
| LMArena Expert | 1464 | — |
Multimodal Not comparable
DeepSeek V4 Pro: —, Pixtral Large: 30.6 (#111)
| Benchmark | DeepSeek V4 Pro | Pixtral Large |
|---|---|---|
| LMArena Vision | — | 1089 |
Multilingual Not comparable
DeepSeek V4 Pro: 54.4 (#45), Pixtral Large: —
| Benchmark | DeepSeek V4 Pro | Pixtral Large |
|---|---|---|
| LMArena Non-English | 1439 | — |
| LMArena Chinese | 1486 | — |
| LMArena French | 1472 | — |
| LMArena German | 1458 | — |
| LMArena Japanese | 1445 | — |
| LMArena Korean | 1447 | — |
| LMArena Russian | 1453 | — |
| LMArena Spanish | 1458 | — |
Instruction Following Not comparable
DeepSeek V4 Pro: 76.1 (#47), Pixtral Large: —
| Benchmark | DeepSeek V4 Pro | Pixtral Large |
|---|---|---|
| LMArena Instruction Following | 1448 | — |
Long Context Not comparable
DeepSeek V4 Pro: 45.0 (#51), Pixtral Large: —
| Benchmark | DeepSeek V4 Pro | Pixtral Large |
|---|---|---|
| CL-bench Life | 13.5% | — |
| LMArena Longer Query | 1458 | — |
Writing & Preference DeepSeek V4 Pro leads
DeepSeek V4 Pro: 65.5 (#46), Pixtral Large: 32.9 (#278)
| Benchmark | DeepSeek V4 Pro | Pixtral Large |
|---|---|---|
| EQ-Bench Creative Writing | 1553 | 988 |
| LMArena Text | 1451 | — |
| LMArena Creative Writing | 1446 | — |
| EQ-Bench 4 | 1166 | — |
| LMArena Multi-Turn | 1467 | — |
Frequently asked questions
Is DeepSeek V4 Pro better than Pixtral Large?
DeepSeek V4 Pro is the stronger model overall, scoring 54.3 to 32.2 on the Noometry Index.
Which is cheaper, DeepSeek V4 Pro or Pixtral Large?
DeepSeek V4 Pro is cheaper. It lists at $0.66 per million input tokens and $1.98 per million output tokens; Pixtral Large lists at $2 and $6.
Which has the bigger context window?
DeepSeek V4 Pro does, with 1M tokens against 128K.
How many benchmarks do DeepSeek V4 Pro and Pixtral Large share?
1 benchmark has published results for both models. DeepSeek V4 Pro has 48 scored results on Noometry and Pixtral Large has 3.