Model comparison
DeepSeek-R1-Distill-Llama-70B vs Mistral Large
DeepSeek-R1-Distill-Llama-70B is the stronger model overall, scoring 37.8 to 31.9 on the Noometry Index.
Last verified . 12 shared benchmarks.
DeepSeek
Mistral AISummary
- They share 12 benchmarks with published results for both. DeepSeek-R1-Distill-Llama-70B scores higher in 6 categories and Mistral Large in 0 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek-R1-Distill-Llama-70B leads 36.0 to 18.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 51.4% for DeepSeek-R1-Distill-Llama-70B and 8.5% for Mistral Large.
Side by side
| DeepSeek-R1-Distill-Llama-70B | Mistral Large | |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Noometry Index | 37.8 | 31.9 |
| Released | 2025-01-20 | 2024-02-26 |
| Weights | Open | Open |
| Context window | — | 131K |
| Max output | — | 16K |
| Input $ / M tokens | — | $2 |
| Output $ / M tokens | — | $6 |
| Results tracked | 13 | 51 |
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Category by category
Coding DeepSeek-R1-Distill-Llama-70B leads
DeepSeek-R1-Distill-Llama-70B: 36.8 (#202), Mistral Large: 34.3 (#240)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | Mistral Large |
|---|---|---|
| BigCodeBench Instruct | 35.3% | 30% |
| LiveBench Coding | 51.6% | 47.1% |
| BigCodeBench Complete | 49.9% | 38.3% |
| SciCode | — | 36.2% |
| LMArena Coding | — | 1277 |
| ALE-Bench | — | 264.7 |
| HumanEval+ | — | 62.2% |
| MBPP+ | — | 59.5% |
Agentic & Tool Use Not comparable
DeepSeek-R1-Distill-Llama-70B: —, Mistral Large: 28.6 (#89)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | Mistral Large |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 38.4% |
Reasoning DeepSeek-R1-Distill-Llama-70B leads
DeepSeek-R1-Distill-Llama-70B: 24.9 (#156), Mistral Large: 15.8 (#310)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | Mistral Large |
|---|---|---|
| LiveBench Reasoning | 67.6% | 43.5% |
| LiveBench Data Analysis | 55.9% | 50.1% |
| LiveBench | 54.5% | 48.4% |
| SimpleBench | — | 22.5% |
| Kagi LLM Benchmark | 52.3% | — |
| CritPt | — | 0% |
| LMArena Hard Prompts | — | 1257 |
| DTBench | — | 65.1% |
| LMCA | — | 16.7% |
| Epoch Capabilities Index | — | 128.52 |
| ForecastBench | — | 57.1 |
Math DeepSeek-R1-Distill-Llama-70B leads
DeepSeek-R1-Distill-Llama-70B: 36.0 (#176), Mistral Large: 18.2 (#291)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | Mistral Large |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 51.4% | 8.5% |
| LiveBench Math | 58.1% | 42.5% |
| MATH Level 5 | 89.9% | 50.3% |
| Omni-MATH | — | 28.1% |
| LMArena Math | — | 1262 |
| FrontierMath (Feb 2025 set) | — | 0.3% |
Knowledge Too close to call
DeepSeek-R1-Distill-Llama-70B: 30.7 (#225), Mistral Large: 30.1 (#230)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | Mistral Large |
|---|---|---|
| GPQA Diamond | 55.7% | 51.3% |
| MMLU-Pro | — | 59.9% |
| Confabulations | — | 21.4% |
| Vectara Hallucination Rate | — | 4.5% |
| GPQA (HELM) | — | 43.5% |
| LMArena Expert | — | 1232 |
| MMLU | — | 80% |
Multilingual Not comparable
DeepSeek-R1-Distill-Llama-70B: —, Mistral Large: 40.0 (#219)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | Mistral Large |
|---|---|---|
| LMArena Non-English | — | 1237 |
| LMArena Chinese | — | 1240 |
| LMArena French | — | 1325 |
| LMArena German | — | 1254 |
| LMArena Japanese | — | 1188 |
| LMArena Korean | — | 1202 |
| LMArena Russian | — | 1257 |
| LMArena Spanish | — | 1268 |
Instruction Following Too close to call
DeepSeek-R1-Distill-Llama-70B: 68.2 (#190), Mistral Large: 67.9 (#191)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | Mistral Large |
|---|---|---|
| LiveBench Instruction Following | 69.9% | 67.9% |
| IFEval | — | 87.7% |
| LMArena Instruction Following | — | 1249 |
Long Context Not comparable
DeepSeek-R1-Distill-Llama-70B: —, Mistral Large: 38.3 (#199)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | Mistral Large |
|---|---|---|
| LMArena Longer Query | — | 1261 |
Writing & Preference DeepSeek-R1-Distill-Llama-70B leads
DeepSeek-R1-Distill-Llama-70B: 49.0 (#194), Mistral Large: 40.7 (#242)
| Benchmark | DeepSeek-R1-Distill-Llama-70B | Mistral Large |
|---|---|---|
| LiveBench Language | 23.8% | 39.4% |
| LMArena Text | — | 1266 |
| LMArena Creative Writing | — | 1243 |
| Short-Story Creative Writing | — | 69% |
| EQ-Bench Creative Writing | — | 985 |
| WildBench | — | 80.1% |
| LMArena Multi-Turn | — | 1260 |
Frequently asked questions
Is DeepSeek-R1-Distill-Llama-70B better than Mistral Large?
DeepSeek-R1-Distill-Llama-70B is the stronger model overall, scoring 37.8 to 31.9 on the Noometry Index.
Is DeepSeek-R1-Distill-Llama-70B or Mistral Large better for coding?
DeepSeek-R1-Distill-Llama-70B scores higher on coding benchmarks: 36.8 versus 34.3 in the Noometry coding category.
How many benchmarks do DeepSeek-R1-Distill-Llama-70B and Mistral Large share?
12 benchmarks have published results for both models. DeepSeek-R1-Distill-Llama-70B has 13 scored results on Noometry and Mistral Large has 51.