Model comparison
DeepSeek-R1-Distill-Qwen-1.5B vs Llama-3.3-70B-Instruct
Llama-3.3-70B-Instruct is the stronger model overall, scoring 30.6 to 26.1 on the Noometry Index.
Last verified . 4 shared benchmarks.
DeepSeek
MetaSummary
- They share 4 benchmarks with published results for both. DeepSeek-R1-Distill-Qwen-1.5B scores higher in 2 categories and Llama-3.3-70B-Instruct in 2 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Llama-3.3-70B-Instruct leads 30.6 to 16.0.
- The biggest single-benchmark swing is BigCodeBench Complete: 7.9% for DeepSeek-R1-Distill-Qwen-1.5B and 57.5% for Llama-3.3-70B-Instruct.
Side by side
| DeepSeek-R1-Distill-Qwen-1.5B | Llama-3.3-70B-Instruct | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 26.1 | 30.6 |
| Released | 2025-01-20 | 2024-12-06 |
| Weights | Open | Open |
| Context window | — | 128K |
| Max output | — | 4K |
| Input $ / M tokens | — | $0.10 |
| Output $ / M tokens | — | $0.32 |
| Results tracked | 5 | 43 |
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Category by category
Coding Llama-3.3-70B-Instruct leads
DeepSeek-R1-Distill-Qwen-1.5B: 21.8 (#336), Llama-3.3-70B-Instruct: 31.0 (#290)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Llama-3.3-70B-Instruct |
|---|---|---|
| BigCodeBench Instruct | 7% | 46.9% |
| BigCodeBench Complete | 7.9% | 57.5% |
| SciCode | — | 26% |
| WeirdML | — | 14.4% |
| LiveBench Coding | — | 36.6% |
| LMArena Coding | — | 1268 |
Agentic & Tool Use Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: —, Llama-3.3-70B-Instruct: 25.8 (#105)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Llama-3.3-70B-Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 31.9% |
| BALROG | — | 23% |
Reasoning DeepSeek-R1-Distill-Qwen-1.5B leads
DeepSeek-R1-Distill-Qwen-1.5B: 19.2 (#262), Llama-3.3-70B-Instruct: 14.1 (#327)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Llama-3.3-70B-Instruct |
|---|---|---|
| SimpleBench | — | 19.9% |
| CritPt | — | 0% |
| Chess Puzzles | 0% | — |
| LiveBench Reasoning | — | 50.8% |
| LMArena Hard Prompts | — | 1257 |
| DTBench | — | 59.5% |
| LiveBench Data Analysis | — | 49.5% |
| LMCA | — | 17.5% |
| Epoch Capabilities Index | — | 127.33 |
| ForecastBench | — | 58.6 |
| LiveBench | — | 50.2% |
Math DeepSeek-R1-Distill-Qwen-1.5B leads
DeepSeek-R1-Distill-Qwen-1.5B: 23.0 (#274), Llama-3.3-70B-Instruct: 15.3 (#298)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 21.4% | 5.1% |
| LiveBench Math | — | 42.2% |
| LMArena Math | — | 1267 |
| MATH Level 5 | — | 41.6% |
Knowledge Llama-3.3-70B-Instruct leads
DeepSeek-R1-Distill-Qwen-1.5B: 16.0 (#290), Llama-3.3-70B-Instruct: 30.6 (#226)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | 33.6% | 47.4% |
| Confabulations | — | 22.8% |
| Vectara Hallucination Rate | — | 4.1% |
| LMArena Expert | — | 1225 |
| MMLU | — | 86.3% |
Multilingual Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: —, Llama-3.3-70B-Instruct: 39.9 (#220)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | — | 1236 |
| LMArena Chinese | — | 1217 |
| LMArena French | — | 1281 |
| LMArena German | — | 1251 |
| LMArena Japanese | — | 1150 |
| LMArena Korean | — | 1143 |
| LMArena Russian | — | 1252 |
| LMArena Spanish | — | 1270 |
Instruction Following Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: —, Llama-3.3-70B-Instruct: 71.1 (#157)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Llama-3.3-70B-Instruct |
|---|---|---|
| LiveBench Instruction Following | — | 82.7% |
| LMArena Instruction Following | — | 1242 |
Long Context Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: —, Llama-3.3-70B-Instruct: 26.4 (#295)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Llama-3.3-70B-Instruct |
|---|---|---|
| Fiction.LiveBench | — | 33.3% |
| LMArena Longer Query | — | 1256 |
Writing & Preference Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: —, Llama-3.3-70B-Instruct: 47.6 (#207)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | — | 1274 |
| LMArena Creative Writing | — | 1250 |
| LMArena Multi-Turn | — | 1280 |
| LiveBench Language | — | 39.2% |
Frequently asked questions
Is DeepSeek-R1-Distill-Qwen-1.5B better than Llama-3.3-70B-Instruct?
Llama-3.3-70B-Instruct is the stronger model overall, scoring 30.6 to 26.1 on the Noometry Index.
Is DeepSeek-R1-Distill-Qwen-1.5B or Llama-3.3-70B-Instruct better for coding?
Llama-3.3-70B-Instruct scores higher on coding benchmarks: 31.0 versus 21.8 in the Noometry coding category.
How many benchmarks do DeepSeek-R1-Distill-Qwen-1.5B and Llama-3.3-70B-Instruct share?
4 benchmarks have published results for both models. DeepSeek-R1-Distill-Qwen-1.5B has 5 scored results on Noometry and Llama-3.3-70B-Instruct has 43.