Model comparison
Llama 3.1-8B vs Qwen3-1.7B
Qwen3-1.7B is the stronger model overall, scoring 26.6 to 23.0 on the Noometry Index.
Last verified . 4 shared benchmarks.
Meta
Alibaba (Qwen)Summary
- They share 4 benchmarks with published results for both. Llama 3.1-8B scores higher in 0 categories and Qwen3-1.7B in 4 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Qwen3-1.7B leads 19.6 to 8.0.
- The biggest single-benchmark swing is GPQA Diamond: 27% for Llama 3.1-8B and 38% for Qwen3-1.7B.
Side by side
| Llama 3.1-8B | Qwen3-1.7B | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 23.0 | 26.6 |
| Released | 2024-07-23 | 2025-04-29 |
| Weights | Open | Open |
| Context window | 128K | — |
| Max output | 4K | — |
| Input $ / M tokens | $0.05 | — |
| Output $ / M tokens | $0.08 | — |
| Results tracked | 43 | 4 |
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Category by category
Coding Not comparable
Llama 3.1-8B: 20.2 (#340), Qwen3-1.7B: —
| Benchmark | Llama 3.1-8B | Qwen3-1.7B |
|---|---|---|
| SciCode | 13.2% | — |
| WeirdML | 1.7% | — |
| BigCodeBench Instruct | 32.8% | — |
| LMArena Coding | 1195 | — |
| BigCodeBench Complete | 40.5% | — |
| HumanEval+ | 62.8% | — |
| MBPP+ | 55.6% | — |
Agentic & Tool Use Qwen3-1.7B leads
Llama 3.1-8B: 22.5 (#131), Qwen3-1.7B: 24.7 (#115)
| Benchmark | Llama 3.1-8B | Qwen3-1.7B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 25.8% | 28.4% |
| BALROG | 15.1% | — |
Reasoning Qwen3-1.7B leads
Llama 3.1-8B: 14.9 (#321), Qwen3-1.7B: 19.2 (#267)
| Benchmark | Llama 3.1-8B | Qwen3-1.7B |
|---|---|---|
| Chess Puzzles | 0% | 0% |
| CritPt | 0% | — |
| LMArena Hard Prompts | 1175 | — |
| DTBench | 50.9% | — |
| LMCA | 5.4% | — |
| Epoch Capabilities Index | 116.57 | — |
| PIQA | 81.2% | — |
Math Qwen3-1.7B leads
Llama 3.1-8B: 10.2 (#317), Qwen3-1.7B: 16.3 (#294)
| Benchmark | Llama 3.1-8B | Qwen3-1.7B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 1.7% | 8.1% |
| Omni-MATH | 13.7% | — |
| LMArena Math | 1179 | — |
| MATH Level 5 | 22.9% | — |
| GSM8K | 82.4% | — |
Knowledge Qwen3-1.7B leads
Llama 3.1-8B: 8.0 (#307), Qwen3-1.7B: 19.6 (#278)
| Benchmark | Llama 3.1-8B | Qwen3-1.7B |
|---|---|---|
| GPQA Diamond | 27% | 38% |
| MMLU-Pro | 40.6% | — |
| GPQA (HELM) | 24.7% | — |
| LMArena Expert | 1144 | — |
| BoolQ | 82.8% | — |
| MMLU | 56.1% | — |
Multilingual Not comparable
Llama 3.1-8B: 34.0 (#249), Qwen3-1.7B: —
| Benchmark | Llama 3.1-8B | Qwen3-1.7B |
|---|---|---|
| LMArena Non-English | 1148 | — |
| LMArena Chinese | 1151 | — |
| LMArena French | 1177 | — |
| LMArena German | 1144 | — |
| LMArena Japanese | 1061 | — |
| LMArena Korean | 1053 | — |
| LMArena Russian | 1158 | — |
| LMArena Spanish | 1169 | — |
Instruction Following Not comparable
Llama 3.1-8B: 58.9 (#258), Qwen3-1.7B: —
| Benchmark | Llama 3.1-8B | Qwen3-1.7B |
|---|---|---|
| IFEval | 74.3% | — |
| LMArena Instruction Following | 1159 | — |
Long Context Not comparable
Llama 3.1-8B: 35.8 (#238), Qwen3-1.7B: —
| Benchmark | Llama 3.1-8B | Qwen3-1.7B |
|---|---|---|
| LMArena Longer Query | 1182 | — |
Writing & Preference Not comparable
Llama 3.1-8B: 29.7 (#290), Qwen3-1.7B: —
| Benchmark | Llama 3.1-8B | Qwen3-1.7B |
|---|---|---|
| LMArena Text | 1187 | — |
| LMArena Creative Writing | 1154 | — |
| EQ-Bench Creative Writing | 713 | — |
| WildBench | 68.7% | — |
| LMArena Multi-Turn | 1172 | — |
Frequently asked questions
Is Llama 3.1-8B better than Qwen3-1.7B?
Qwen3-1.7B is the stronger model overall, scoring 26.6 to 23.0 on the Noometry Index.
How many benchmarks do Llama 3.1-8B and Qwen3-1.7B share?
4 benchmarks have published results for both models. Llama 3.1-8B has 43 scored results on Noometry and Qwen3-1.7B has 4.