# DeepSeek-R1 vs Mistral Large 4

> DeepSeek-R1 and Mistral Large 4 score almost the same on the Noometry Index (42.3 vs 43.1), so choose on price, context window or the category you care about most.

- Canonical page: https://noometry.com/compare/deepseek-r1-vs-mistral-large-4
- Last updated: 2026-10-10
- Shared benchmarks: 12

## Summary

- They share 12 benchmarks with published results for both. DeepSeek-R1 scores higher in 4 categories and Mistral Large 4 in 4 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-R1 leads 44.5 to 36.6.
- DeepSeek-R1 is cheaper at $0.50 / $2.15 per million input/output tokens, against $0.68 / $2.09 for Mistral Large 4.
- Mistral Large 4 accepts more context: 1.05M tokens versus 164K.

## Snapshot

| | DeepSeek-R1 | Mistral Large 4 |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 42.3 | 43.1 |
| Rank | 115 | 99 |
| Context | 164K | 1.05M |
| Input $/M | $0.50 | $0.68 |
| Output $/M | $2.15 | $2.09 |
| Weights | Proprietary | Proprietary |

## Coding

- DeepSeek-R1: 46.3 (#68)
- Mistral Large 4: 48.6 (#57)

| Benchmark | DeepSeek-R1 | Mistral Large 4 |
|---|---|---|
| LMArena Coding | 1427 | 1475 |
| Aider Polyglot | 71.4% | — |
| LMArena WebDev | — | 1541 |
| SciCode | 35.7% | — |
| WeirdML | 41.6% | — |
| LiveBench Coding | 66.7% | — |
| ALE-Bench | 804.12 | — |
| AlgoTune | 1.7 | — |

## Agentic & Tool Use

- DeepSeek-R1: 30.7 (#75)
- Mistral Large 4: —

| Benchmark | DeepSeek-R1 | Mistral Large 4 |
|---|---|---|
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| METR Time Horizons | 53.8% | — |

## Reasoning

- DeepSeek-R1: 18.6 (#278)
- Mistral Large 4: 22.5 (#192)

| Benchmark | DeepSeek-R1 | Mistral Large 4 |
|---|---|---|
| LMArena Hard Prompts | 1416 | 1444 |
| ARC-AGI-2 | 1.3% | — |
| SimpleBench | 40.8% | — |
| Kagi LLM Benchmark | 69.4% | — |
| NYT Connections (extended) | — | 27.4% |
| ARC-AGI-1 | 21.2% | — |
| CritPt | 1.1% | — |
| LiveBench Reasoning | 83.2% | — |
| LiveBench Data Analysis | 69.8% | — |
| Epoch Capabilities Index | 141.29 | — |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |

## Math

- DeepSeek-R1: 43.8 (#79)
- Mistral Large 4: 40.4 (#91)

| Benchmark | DeepSeek-R1 | Mistral Large 4 |
|---|---|---|
| LMArena Math | 1400 | 1488 |
| OTIS Mock AIME 2024-2025 | 66.4% | — |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| MATH Level 5 | 96.6% | — |

## Knowledge

- DeepSeek-R1: 44.5 (#87)
- Mistral Large 4: 36.6 (#166)

| Benchmark | DeepSeek-R1 | Mistral Large 4 |
|---|---|---|
| LMArena Expert | 1394 | 1447 |
| GPQA Diamond | 76.3% | — |
| SimpleQA Verified | — | 20% |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| Vectara Hallucination Rate | 11.3% | — |
| GPQA (HELM) | 66.6% | — |

## Multilingual

- DeepSeek-R1: 52.4 (#85)
- Mistral Large 4: 52.6 (#82)

| Benchmark | DeepSeek-R1 | Mistral Large 4 |
|---|---|---|
| LMArena Non-English | 1412 | 1415 |
| LMArena Chinese | 1442 | 1491 |
| LMArena Russian | 1423 | 1414 |
| LMArena French | 1417 | — |
| LMArena German | 1404 | — |
| LMArena Japanese | 1391 | — |
| LMArena Korean | 1360 | — |
| LMArena Spanish | 1411 | — |

## Instruction Following

- DeepSeek-R1: 72.0 (#143)
- Mistral Large 4: 75.0 (#76)

| Benchmark | DeepSeek-R1 | Mistral Large 4 |
|---|---|---|
| LMArena Instruction Following | 1382 | 1424 |
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |

## Long Context

- DeepSeek-R1: 45.4 (#36)
- Mistral Large 4: 43.6 (#89)

| Benchmark | DeepSeek-R1 | Mistral Large 4 |
|---|---|---|
| LMArena Longer Query | 1391 | 1429 |
| Fiction.LiveBench | 75% | — |

## Writing & Preference

- DeepSeek-R1: 61.4 (#88)
- Mistral Large 4: 60.4 (#97)

| Benchmark | DeepSeek-R1 | Mistral Large 4 |
|---|---|---|
| LMArena Text | 1428 | 1427 |
| LMArena Creative Writing | 1405 | 1361 |
| LMArena Multi-Turn | 1405 | 1424 |
| Short-Story Creative Writing | 83% | — |
| EQ-Bench Creative Writing | 1500 | — |
| WildBench | 82.8% | — |
| LiveBench Language | 48.5% | — |

## FAQ

### Is DeepSeek-R1 better than Mistral Large 4?

DeepSeek-R1 and Mistral Large 4 score almost the same on the Noometry Index (42.3 vs 43.1), so choose on price, context window or the category you care about most.

### Which is cheaper, DeepSeek-R1 or Mistral Large 4?

DeepSeek-R1 is cheaper. It lists at $0.50 per million input tokens and $2.15 per million output tokens; Mistral Large 4 lists at $0.68 and $2.09.

### Is DeepSeek-R1 or Mistral Large 4 better for coding?

Mistral Large 4 scores higher on coding benchmarks: 48.6 versus 46.3 in the Noometry coding category.

### Which has the bigger context window?

Mistral Large 4 does, with 1.05M tokens against 164K.

### How many benchmarks do DeepSeek-R1 and Mistral Large 4 share?

12 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and Mistral Large 4 has 15.