Model comparison
DeepSeek-R1-Distill-Qwen-1.5B vs Llama 3.1 Tulu 3 8b
Llama 3.1 Tulu 3 8b is the stronger model overall, scoring 35.7 to 26.1 on the Noometry Index.
Last verified . 0 shared benchmarks.
DeepSeek
Allen Institute for AI (Ai2)Summary
- The widest gap is in coding, where Llama 3.1 Tulu 3 8b leads 34.4 to 21.8.
Side by side
| DeepSeek-R1-Distill-Qwen-1.5B | Llama 3.1 Tulu 3 8b | |
|---|---|---|
| Provider | DeepSeek | Allen Institute for AI (Ai2) |
| Noometry Index | 26.1 | 35.7 |
| Released | 2025-01-20 | — |
| Weights | Open | Open |
| Context window | — | — |
| Max output | — | — |
| Input $ / M tokens | — | — |
| Output $ / M tokens | — | — |
| Results tracked | 5 | 11 |
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Category by category
Coding Llama 3.1 Tulu 3 8b leads
DeepSeek-R1-Distill-Qwen-1.5B: 21.8 (#336), Llama 3.1 Tulu 3 8b: 34.4 (#235)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Llama 3.1 Tulu 3 8b |
|---|---|---|
| BigCodeBench Instruct | 7% | — |
| LMArena Coding | — | 1183 |
| BigCodeBench Complete | 7.9% | — |
Reasoning Llama 3.1 Tulu 3 8b leads
DeepSeek-R1-Distill-Qwen-1.5B: 19.2 (#262), Llama 3.1 Tulu 3 8b: 22.8 (#188)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Llama 3.1 Tulu 3 8b |
|---|---|---|
| Chess Puzzles | 0% | — |
| LMArena Hard Prompts | — | 1174 |
Math Llama 3.1 Tulu 3 8b leads
DeepSeek-R1-Distill-Qwen-1.5B: 23.0 (#274), Llama 3.1 Tulu 3 8b: 33.9 (#198)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Llama 3.1 Tulu 3 8b |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 21.4% | — |
| LMArena Math | — | 1195 |
Knowledge Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: 16.0 (#290), Llama 3.1 Tulu 3 8b: —
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Llama 3.1 Tulu 3 8b |
|---|---|---|
| GPQA Diamond | 33.6% | — |
Multilingual Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: —, Llama 3.1 Tulu 3 8b: 35.4 (#246)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Llama 3.1 Tulu 3 8b |
|---|---|---|
| LMArena Non-English | — | 1169 |
| LMArena Chinese | — | 1176 |
| LMArena Russian | — | 1193 |
Instruction Following Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: —, Llama 3.1 Tulu 3 8b: 61.3 (#246)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Llama 3.1 Tulu 3 8b |
|---|---|---|
| LMArena Instruction Following | — | 1174 |
Long Context Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: —, Llama 3.1 Tulu 3 8b: 35.8 (#239)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Llama 3.1 Tulu 3 8b |
|---|---|---|
| LMArena Longer Query | — | 1181 |
Writing & Preference Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: —, Llama 3.1 Tulu 3 8b: 39.7 (#245)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Llama 3.1 Tulu 3 8b |
|---|---|---|
| LMArena Text | — | 1193 |
| LMArena Creative Writing | — | 1182 |
| LMArena Multi-Turn | — | 1154 |
Frequently asked questions
Is DeepSeek-R1-Distill-Qwen-1.5B better than Llama 3.1 Tulu 3 8b?
Llama 3.1 Tulu 3 8b is the stronger model overall, scoring 35.7 to 26.1 on the Noometry Index.
Is DeepSeek-R1-Distill-Qwen-1.5B or Llama 3.1 Tulu 3 8b better for coding?
Llama 3.1 Tulu 3 8b scores higher on coding benchmarks: 34.4 versus 21.8 in the Noometry coding category.
How many benchmarks do DeepSeek-R1-Distill-Qwen-1.5B and Llama 3.1 Tulu 3 8b share?
0 benchmarks have published results for both models. DeepSeek-R1-Distill-Qwen-1.5B has 5 scored results on Noometry and Llama 3.1 Tulu 3 8b has 11.