Model comparison
DeepSeek-V3.1 vs Llama 3.2 90B
DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 27.1 on the Noometry Index.
Last verified . 1 shared benchmarks.
DeepSeek
MetaSummary
- They share 1 benchmark with published results for both. DeepSeek-V3.1 scores higher in 3 categories and Llama 3.2 90B in 0 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek-V3.1 leads 36.1 to 10.4.
Side by side
| DeepSeek-V3.1 | Llama 3.2 90B | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 42.2 | 27.1 |
| Released | 2025-08-21 | 2024-09-24 |
| Weights | Open | Open |
| Context window | 164K | — |
| Max output | 8K | — |
| Input $ / M tokens | $0.25 | — |
| Output $ / M tokens | $0.95 | — |
| Results tracked | 27 | 9 |
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Category by category
Coding Not comparable
DeepSeek-V3.1: 39.8 (#140), Llama 3.2 90B: —
| Benchmark | DeepSeek-V3.1 | Llama 3.2 90B |
|---|---|---|
| WeirdML | 38.4% | — |
| LMArena Coding | 1417 | — |
Agentic & Tool Use Not comparable
DeepSeek-V3.1: —, Llama 3.2 90B: 29.7 (#73)
| Benchmark | DeepSeek-V3.1 | Llama 3.2 90B |
|---|---|---|
| BALROG | — | 27.3% |
Reasoning DeepSeek-V3.1 leads
DeepSeek-V3.1: 27.3 (#109), Llama 3.2 90B: 20.9 (#207)
| Benchmark | DeepSeek-V3.1 | Llama 3.2 90B |
|---|---|---|
| Epoch Capabilities Index | 139.92 | 125.5 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| EnigmaEval | — | 0.4% |
| LMArena Hard Prompts | 1417 | — |
| DTBench | 82.7% | — |
| LMCA | 24.3% | — |
| ForecastBench | 58 | — |
Math DeepSeek-V3.1 leads
DeepSeek-V3.1: 36.1 (#125), Llama 3.2 90B: 10.4 (#302)
| Benchmark | DeepSeek-V3.1 | Llama 3.2 90B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 2.6% |
| LMArena Math | 1420 | — |
| MATH Level 5 | — | 39.4% |
Knowledge DeepSeek-V3.1 leads
DeepSeek-V3.1: 43.0 (#86), Llama 3.2 90B: 21.7 (#269)
| Benchmark | DeepSeek-V3.1 | Llama 3.2 90B |
|---|---|---|
| GPQA Diamond | — | 41% |
| Vectara Hallucination Rate | 5.5% | — |
| LMArena Expert | 1403 | — |
| MMLU | — | 80.3% |
Multimodal Not comparable
DeepSeek-V3.1: —, Llama 3.2 90B: 24.9 (#119)
| Benchmark | DeepSeek-V3.1 | Llama 3.2 90B |
|---|---|---|
| LMArena Vision | — | 1000 |
| GeoBench | — | 52% |
Multilingual Not comparable
DeepSeek-V3.1: 51.8 (#100), Llama 3.2 90B: —
| Benchmark | DeepSeek-V3.1 | Llama 3.2 90B |
|---|---|---|
| LMArena Non-English | 1400 | — |
| LMArena Chinese | 1470 | — |
| LMArena French | 1447 | — |
| LMArena German | 1412 | — |
| LMArena Japanese | 1374 | — |
| LMArena Korean | 1337 | — |
| LMArena Russian | 1405 | — |
| LMArena Spanish | 1429 | — |
Instruction Following Not comparable
DeepSeek-V3.1: 74.1 (#105), Llama 3.2 90B: —
| Benchmark | DeepSeek-V3.1 | Llama 3.2 90B |
|---|---|---|
| LMArena Instruction Following | 1400 | — |
Long Context Not comparable
DeepSeek-V3.1: 36.3 (#227), Llama 3.2 90B: —
| Benchmark | DeepSeek-V3.1 | Llama 3.2 90B |
|---|---|---|
| Fiction.LiveBench | 52.8% | — |
| LMArena Longer Query | 1422 | — |
Writing & Preference Not comparable
DeepSeek-V3.1: 60.5 (#93), Llama 3.2 90B: —
| Benchmark | DeepSeek-V3.1 | Llama 3.2 90B |
|---|---|---|
| LMArena Text | 1420 | — |
| LMArena Creative Writing | 1401 | — |
| EQ-Bench Creative Writing | 1436 | — |
| LMArena Multi-Turn | 1408 | — |
Frequently asked questions
Is DeepSeek-V3.1 better than Llama 3.2 90B?
DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 27.1 on the Noometry Index.
How many benchmarks do DeepSeek-V3.1 and Llama 3.2 90B share?
1 benchmark has published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Llama 3.2 90B has 9.