Model comparison
Llama 3-8B vs Qwen3-1.7B
Qwen3-1.7B is the stronger model overall, scoring 26.6 to 25.5 on the Noometry Index.
Last verified . 3 shared benchmarks.
Meta
Alibaba (Qwen)Summary
- They share 3 benchmarks with published results for both. Llama 3-8B scores higher in 0 categories and Qwen3-1.7B in 3 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Qwen3-1.7B leads 19.6 to 7.8.
- The biggest single-benchmark swing is GPQA Diamond: 26.1% for Llama 3-8B and 38% for Qwen3-1.7B.
Side by side
| Llama 3-8B | Qwen3-1.7B | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 25.5 | 26.6 |
| Released | 2024-04-18 | 2025-04-29 |
| Weights | Open | Open |
| Context window | — | — |
| Max output | — | — |
| Input $ / M tokens | — | — |
| Output $ / M tokens | — | — |
| Results tracked | 34 | 4 |
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Category by category
Coding Not comparable
Llama 3-8B: 31.0 (#289), Qwen3-1.7B: —
| Benchmark | Llama 3-8B | Qwen3-1.7B |
|---|---|---|
| BigCodeBench Instruct | 31.9% | — |
| LMArena Coding | 1152 | — |
| BigCodeBench Complete | 36.9% | — |
| HumanEval+ | 56.7% | — |
| MBPP+ | 54.8% | — |
Agentic & Tool Use Not comparable
Llama 3-8B: —, Qwen3-1.7B: 24.7 (#115)
| Benchmark | Llama 3-8B | Qwen3-1.7B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 28.4% |
Reasoning Qwen3-1.7B leads
Llama 3-8B: 14.3 (#326), Qwen3-1.7B: 19.2 (#267)
| Benchmark | Llama 3-8B | Qwen3-1.7B |
|---|---|---|
| Chess Puzzles | 0% | 0% |
| LMArena Hard Prompts | 1133 | — |
| DTBench | 43.9% | — |
| Adversarial NLI | 57.3% | — |
| Epoch Capabilities Index | 116.45 | — |
| ForecastBench | 58.6 | — |
| WinoGrande | 75.7% | — |
Math Qwen3-1.7B leads
Llama 3-8B: 8.8 (#323), Qwen3-1.7B: 16.3 (#294)
| Benchmark | Llama 3-8B | Qwen3-1.7B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 1.9% | 8.1% |
| LMArena Math | 1151 | — |
| MATH Level 5 | 6.1% | — |
Knowledge Qwen3-1.7B leads
Llama 3-8B: 7.8 (#308), Qwen3-1.7B: 19.6 (#278)
| Benchmark | Llama 3-8B | Qwen3-1.7B |
|---|---|---|
| GPQA Diamond | 26.1% | 38% |
| LMArena Expert | 1113 | — |
| ARC (AI2) Challenge | 82.8% | — |
| MMLU | 68.8% | — |
| OpenBookQA | 82.6% | — |
| TriviaQA | 67.7% | — |
Multilingual Not comparable
Llama 3-8B: 30.8 (#261), Qwen3-1.7B: —
| Benchmark | Llama 3-8B | Qwen3-1.7B |
|---|---|---|
| LMArena Non-English | 1098 | — |
| LMArena Chinese | 1076 | — |
| LMArena French | 1159 | — |
| LMArena German | 1104 | — |
| LMArena Japanese | 967 | — |
| LMArena Korean | 1004 | — |
| LMArena Russian | 1109 | — |
| LMArena Spanish | 1173 | — |
Instruction Following Not comparable
Llama 3-8B: 58.4 (#260), Qwen3-1.7B: —
| Benchmark | Llama 3-8B | Qwen3-1.7B |
|---|---|---|
| LMArena Instruction Following | 1127 | — |
Long Context Not comparable
Llama 3-8B: 34.2 (#251), Qwen3-1.7B: —
| Benchmark | Llama 3-8B | Qwen3-1.7B |
|---|---|---|
| LMArena Longer Query | 1128 | — |
Writing & Preference Not comparable
Llama 3-8B: 37.5 (#256), Qwen3-1.7B: —
| Benchmark | Llama 3-8B | Qwen3-1.7B |
|---|---|---|
| LMArena Text | 1166 | — |
| LMArena Creative Writing | 1150 | — |
| LMArena Multi-Turn | 1152 | — |
Frequently asked questions
Is Llama 3-8B better than Qwen3-1.7B?
Qwen3-1.7B is the stronger model overall, scoring 26.6 to 25.5 on the Noometry Index.
How many benchmarks do Llama 3-8B and Qwen3-1.7B share?
3 benchmarks have published results for both models. Llama 3-8B has 34 scored results on Noometry and Qwen3-1.7B has 4.