Model comparison
DeepSeek-R1 vs Mistral 7B
DeepSeek-R1 is the stronger model overall, scoring 42.3 to 23.0 on the Noometry Index. Mistral 7B costs 3.6× less per token, which makes it the better buy when DeepSeek-R1's lead doesn't matter for your workload.
Last verified . 20 shared benchmarks.
DeepSeek
Mistral AISummary
- They share 20 benchmarks with published results for both. DeepSeek-R1 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 DeepSeek-R1 leads 44.5 to 7.4.
- The biggest single-benchmark swing is MATH Level 5: 96.6% for DeepSeek-R1 and 3.7% for Mistral 7B.
- Mistral 7B is cheaper at $0.25 / $0.25 per million input/output tokens, against $0.50 / $2.15 for DeepSeek-R1.
- DeepSeek-R1 accepts more context: 164K tokens versus 8K.
- Mistral 7B has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-R1 | Mistral 7B | |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 42.3 | 23.0 |
| Released | 2025-01-20 | 2023-09-27 |
| Weights | Proprietary | Open |
| Context window | 164K | 8K |
| Max output | 64K | 8K |
| Input $ / M tokens | $0.50 | $0.25 |
| Output $ / M tokens | $2.15 | $0.25 |
| Results tracked | 52 | 37 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding DeepSeek-R1 leads
DeepSeek-R1: 46.3 (#68), Mistral 7B: 26.4 (#326)
| Benchmark | DeepSeek-R1 | Mistral 7B |
|---|---|---|
| LMArena Coding | 1427 | 1082 |
| Aider Polyglot | 71.4% | — |
| SciCode | 35.7% | — |
| WeirdML | 41.6% | — |
| BigCodeBench Instruct | — | 19.5% |
| LiveBench Coding | 66.7% | — |
| BigCodeBench Complete | — | 27.3% |
| ALE-Bench | 804.12 | — |
| AlgoTune | 1.7 | — |
| HumanEval+ | — | 36% |
| MBPP+ | — | 42.1% |
Agentic & Tool Use Not comparable
DeepSeek-R1: 30.7 (#75), Mistral 7B: —
| Benchmark | DeepSeek-R1 | Mistral 7B |
|---|---|---|
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| METR Time Horizons | 53.8% | — |
Reasoning DeepSeek-R1 leads
DeepSeek-R1: 18.6 (#278), Mistral 7B: 13.1 (#336)
| Benchmark | DeepSeek-R1 | Mistral 7B |
|---|---|---|
| LMArena Hard Prompts | 1416 | 1067 |
| Epoch Capabilities Index | 141.29 | 112.21 |
| ARC-AGI-2 | 1.3% | — |
| SimpleBench | 40.8% | — |
| Kagi LLM Benchmark | 69.4% | — |
| ARC-AGI-1 | 21.2% | — |
| CritPt | 1.1% | — |
| Chess Puzzles | — | 0% |
| LiveBench Reasoning | 83.2% | — |
| DTBench | — | 42.5% |
| LiveBench Data Analysis | 69.8% | — |
| Adversarial NLI | — | 47.1% |
| BIG-Bench Hard | — | 56.1% |
| ForecastBench | 60 | — |
| HellaSwag | — | 81% |
| LiveBench | 71.6% | — |
| PIQA | — | 83% |
| WinoGrande | — | 75.3% |
Math DeepSeek-R1 leads
DeepSeek-R1: 43.8 (#79), Mistral 7B: 8.1 (#325)
| Benchmark | DeepSeek-R1 | Mistral 7B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.4% | 0.3% |
| LMArena Math | 1400 | 1085 |
| MATH Level 5 | 96.6% | 3.7% |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| GSM8K | — | 54.4% |
Knowledge DeepSeek-R1 leads
DeepSeek-R1: 44.5 (#87), Mistral 7B: 7.4 (#311)
| Benchmark | DeepSeek-R1 | Mistral 7B |
|---|---|---|
| GPQA Diamond | 76.3% | 15.2% |
| LMArena Expert | 1394 | 1036 |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| Vectara Hallucination Rate | 11.3% | — |
| GPQA (HELM) | 66.6% | — |
| ARC (AI2) Challenge | — | 78.6% |
| BoolQ | — | 87.4% |
| MMLU | — | 62.5% |
| OpenBookQA | — | 79.8% |
| TriviaQA | — | 75.2% |
Multilingual DeepSeek-R1 leads
DeepSeek-R1: 52.4 (#85), Mistral 7B: 25.8 (#283)
| Benchmark | DeepSeek-R1 | Mistral 7B |
|---|---|---|
| LMArena Non-English | 1412 | 1012 |
| LMArena Chinese | 1442 | 1009 |
| LMArena French | 1417 | 1037 |
| LMArena German | 1404 | 987 |
| LMArena Japanese | 1391 | 878 |
| LMArena Russian | 1423 | 1018 |
| LMArena Spanish | 1411 | 1026 |
| LMArena Korean | 1360 | — |
Instruction Following DeepSeek-R1 leads
DeepSeek-R1: 72.0 (#143), Mistral 7B: 54.2 (#280)
| Benchmark | DeepSeek-R1 | Mistral 7B |
|---|---|---|
| LMArena Instruction Following | 1382 | 1060 |
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |
Long Context DeepSeek-R1 leads
DeepSeek-R1: 45.4 (#36), Mistral 7B: 32.2 (#271)
| Benchmark | DeepSeek-R1 | Mistral 7B |
|---|---|---|
| LMArena Longer Query | 1391 | 1060 |
| Fiction.LiveBench | 75% | — |
Writing & Preference DeepSeek-R1 leads
DeepSeek-R1: 61.4 (#88), Mistral 7B: 30.7 (#286)
| Benchmark | DeepSeek-R1 | Mistral 7B |
|---|---|---|
| LMArena Text | 1428 | 1090 |
| LMArena Creative Writing | 1405 | 1068 |
| LMArena Multi-Turn | 1405 | 1062 |
| Short-Story Creative Writing | 83% | — |
| EQ-Bench Creative Writing | 1500 | — |
| WildBench | 82.8% | — |
| LiveBench Language | 48.5% | — |
Frequently asked questions
Is DeepSeek-R1 better than Mistral 7B?
DeepSeek-R1 is the stronger model overall, scoring 42.3 to 23.0 on the Noometry Index. Mistral 7B costs 3.6× less per token, which makes it the better buy when DeepSeek-R1's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-R1 or Mistral 7B?
Mistral 7B is cheaper. It lists at $0.25 per million input tokens and $0.25 per million output tokens; DeepSeek-R1 lists at $0.50 and $2.15.
Is DeepSeek-R1 or Mistral 7B better for coding?
DeepSeek-R1 scores higher on coding benchmarks: 46.3 versus 26.4 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-R1 does, with 164K tokens against 8K.
How many benchmarks do DeepSeek-R1 and Mistral 7B share?
20 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and Mistral 7B has 37.