Model comparison
DeepSeek-R1 vs Llama 3.2 90B
DeepSeek-R1 is the stronger model overall, scoring 42.3 to 27.5 on the Noometry Index.
Last verified . 5 shared benchmarks.
DeepSeek
MetaSummary
- They share 5 benchmarks with published results for both. DeepSeek-R1 scores higher in 3 categories and Llama 3.2 90B in 1 category; 3 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek-R1 leads 43.8 to 11.1.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 66.4% for DeepSeek-R1 and 2.6% for Llama 3.2 90B.
- Llama 3.2 90B has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-R1 | Llama 3.2 90B | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 42.3 | 27.5 |
| Released | 2025-01-20 | 2024-09-24 |
| Weights | Proprietary | Open |
| Context window | 164K | — |
| Max output | 64K | — |
| Input $ / M tokens | $0.50 | — |
| Output $ / M tokens | $2.15 | — |
| Results tracked | 52 | 9 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Not comparable
DeepSeek-R1: 46.3 (#68), Llama 3.2 90B: —
| Benchmark | DeepSeek-R1 | Llama 3.2 90B |
|---|---|---|
| Aider Polyglot | 71.4% | — |
| SciCode | 35.7% | — |
| WeirdML | 41.6% | — |
| LiveBench Coding | 66.7% | — |
| LMArena Coding | 1427 | — |
| ALE-Bench | 804.12 | — |
| AlgoTune | 1.7 | — |
Agentic & Tool Use Too close to call
DeepSeek-R1: 30.7 (#75), Llama 3.2 90B: 30.0 (#80)
| Benchmark | DeepSeek-R1 | Llama 3.2 90B |
|---|---|---|
| BALROG | 34.9% | 27.3% |
| DeepResearch Bench | 35.1% | — |
| METR Time Horizons | 53.8% | — |
Reasoning Llama 3.2 90B leads
DeepSeek-R1: 18.6 (#278), Llama 3.2 90B: 21.7 (#217)
| Benchmark | DeepSeek-R1 | Llama 3.2 90B |
|---|---|---|
| Epoch Capabilities Index | 141.29 | 125.5 |
| ARC-AGI-2 | 1.3% | — |
| SimpleBench | 40.8% | — |
| Kagi LLM Benchmark | 69.4% | — |
| ARC-AGI-1 | 21.2% | — |
| CritPt | 1.1% | — |
| EnigmaEval | — | 0.4% |
| LiveBench Reasoning | 83.2% | — |
| LMArena Hard Prompts | 1416 | — |
| LiveBench Data Analysis | 69.8% | — |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |
Math DeepSeek-R1 leads
DeepSeek-R1: 43.8 (#79), Llama 3.2 90B: 11.1 (#308)
| Benchmark | DeepSeek-R1 | Llama 3.2 90B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.4% | 2.6% |
| MATH Level 5 | 96.6% | 39.4% |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| LMArena Math | 1400 | — |
Knowledge DeepSeek-R1 leads
DeepSeek-R1: 44.5 (#87), Llama 3.2 90B: 21.7 (#274)
| Benchmark | DeepSeek-R1 | Llama 3.2 90B |
|---|---|---|
| GPQA Diamond | 76.3% | 41% |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| Vectara Hallucination Rate | 11.3% | — |
| GPQA (HELM) | 66.6% | — |
| LMArena Expert | 1394 | — |
| MMLU | — | 80.3% |
Multimodal Not comparable
DeepSeek-R1: —, Llama 3.2 90B: 25.4 (#124)
| Benchmark | DeepSeek-R1 | Llama 3.2 90B |
|---|---|---|
| LMArena Vision | — | 1000 |
| GeoBench | — | 52% |
Multilingual Not comparable
DeepSeek-R1: 52.4 (#85), Llama 3.2 90B: —
| Benchmark | DeepSeek-R1 | Llama 3.2 90B |
|---|---|---|
| LMArena Non-English | 1412 | — |
| LMArena Chinese | 1442 | — |
| LMArena French | 1417 | — |
| LMArena German | 1404 | — |
| LMArena Japanese | 1391 | — |
| LMArena Korean | 1360 | — |
| LMArena Russian | 1423 | — |
| LMArena Spanish | 1411 | — |
Instruction Following Not comparable
DeepSeek-R1: 72.0 (#143), Llama 3.2 90B: —
| Benchmark | DeepSeek-R1 | Llama 3.2 90B |
|---|---|---|
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |
| LMArena Instruction Following | 1382 | — |
Long Context Not comparable
DeepSeek-R1: 45.4 (#36), Llama 3.2 90B: —
| Benchmark | DeepSeek-R1 | Llama 3.2 90B |
|---|---|---|
| Fiction.LiveBench | 75% | — |
| LMArena Longer Query | 1391 | — |
Writing & Preference Not comparable
DeepSeek-R1: 61.4 (#88), Llama 3.2 90B: —
| Benchmark | DeepSeek-R1 | Llama 3.2 90B |
|---|---|---|
| LMArena Text | 1428 | — |
| LMArena Creative Writing | 1405 | — |
| Short-Story Creative Writing | 83% | — |
| EQ-Bench Creative Writing | 1500 | — |
| WildBench | 82.8% | — |
| LMArena Multi-Turn | 1405 | — |
| LiveBench Language | 48.5% | — |
Frequently asked questions
Is DeepSeek-R1 better than Llama 3.2 90B?
DeepSeek-R1 is the stronger model overall, scoring 42.3 to 27.5 on the Noometry Index.
How many benchmarks do DeepSeek-R1 and Llama 3.2 90B share?
5 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and Llama 3.2 90B has 9.