Model comparison
Llama 3.1-8B vs Qwen1.5 4b Chat
Qwen1.5 4b Chat is the stronger model overall, scoring 28.8 to 23.0 on the Noometry Index.
Last verified . 13 shared benchmarks.
Meta
Alibaba (Qwen)Summary
- They share 13 benchmarks with published results for both. Llama 3.1-8B scores higher in 4 categories and Qwen1.5 4b Chat in 4 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen1.5 4b Chat leads 30.4 to 10.2.
Side by side
| Llama 3.1-8B | Qwen1.5 4b Chat | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 23.0 | 28.8 |
| Released | 2024-07-23 | — |
| Weights | Open | Open |
| Context window | 128K | — |
| Max output | 4K | — |
| Input $ / M tokens | $0.05 | — |
| Output $ / M tokens | $0.08 | — |
| Results tracked | 43 | 13 |
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Category by category
Coding Qwen1.5 4b Chat leads
Llama 3.1-8B: 20.2 (#340), Qwen1.5 4b Chat: 29.1 (#308)
| Benchmark | Llama 3.1-8B | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Coding | 1195 | 999 |
| SciCode | 13.2% | — |
| WeirdML | 1.7% | — |
| BigCodeBench Instruct | 32.8% | — |
| BigCodeBench Complete | 40.5% | — |
| HumanEval+ | 62.8% | — |
| MBPP+ | 55.6% | — |
Agentic & Tool Use Not comparable
Llama 3.1-8B: 22.5 (#131), Qwen1.5 4b Chat: —
| Benchmark | Llama 3.1-8B | Qwen1.5 4b Chat |
|---|---|---|
| Berkeley Function Calling Leaderboard | 25.8% | — |
| BALROG | 15.1% | — |
Reasoning Qwen1.5 4b Chat leads
Llama 3.1-8B: 14.9 (#321), Qwen1.5 4b Chat: 18.5 (#279)
| Benchmark | Llama 3.1-8B | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Hard Prompts | 1175 | 976 |
| CritPt | 0% | — |
| Chess Puzzles | 0% | — |
| DTBench | 50.9% | — |
| LMCA | 5.4% | — |
| Epoch Capabilities Index | 116.57 | — |
| PIQA | 81.2% | — |
Math Qwen1.5 4b Chat leads
Llama 3.1-8B: 10.2 (#317), Qwen1.5 4b Chat: 30.4 (#234)
| Benchmark | Llama 3.1-8B | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Math | 1179 | 1026 |
| OTIS Mock AIME 2024-2025 | 1.7% | — |
| Omni-MATH | 13.7% | — |
| MATH Level 5 | 22.9% | — |
| GSM8K | 82.4% | — |
Knowledge Qwen1.5 4b Chat leads
Llama 3.1-8B: 8.0 (#307), Qwen1.5 4b Chat: 26.7 (#255)
| Benchmark | Llama 3.1-8B | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Expert | 1144 | 980 |
| GPQA Diamond | 27% | — |
| MMLU-Pro | 40.6% | — |
| GPQA (HELM) | 24.7% | — |
| BoolQ | 82.8% | — |
| MMLU | 56.1% | — |
Multilingual Llama 3.1-8B leads
Llama 3.1-8B: 34.0 (#249), Qwen1.5 4b Chat: 24.1 (#290)
| Benchmark | Llama 3.1-8B | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Non-English | 1148 | 979 |
| LMArena Chinese | 1151 | 1024 |
| LMArena German | 1144 | 902 |
| LMArena Russian | 1158 | 952 |
| LMArena French | 1177 | — |
| LMArena Japanese | 1061 | — |
| LMArena Korean | 1053 | — |
| LMArena Spanish | 1169 | — |
Instruction Following Llama 3.1-8B leads
Llama 3.1-8B: 58.9 (#258), Qwen1.5 4b Chat: 49.0 (#300)
| Benchmark | Llama 3.1-8B | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Instruction Following | 1159 | 978 |
| IFEval | 74.3% | — |
Long Context Llama 3.1-8B leads
Llama 3.1-8B: 35.8 (#238), Qwen1.5 4b Chat: 30.1 (#290)
| Benchmark | Llama 3.1-8B | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Longer Query | 1182 | 988 |
Writing & Preference Llama 3.1-8B leads
Llama 3.1-8B: 29.7 (#290), Qwen1.5 4b Chat: 23.8 (#309)
| Benchmark | Llama 3.1-8B | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Text | 1187 | 997 |
| LMArena Creative Writing | 1154 | 969 |
| LMArena Multi-Turn | 1172 | 977 |
| EQ-Bench Creative Writing | 713 | — |
| WildBench | 68.7% | — |
Frequently asked questions
Is Llama 3.1-8B better than Qwen1.5 4b Chat?
Qwen1.5 4b Chat is the stronger model overall, scoring 28.8 to 23.0 on the Noometry Index.
Is Llama 3.1-8B or Qwen1.5 4b Chat better for coding?
Qwen1.5 4b Chat scores higher on coding benchmarks: 29.1 versus 20.2 in the Noometry coding category.
How many benchmarks do Llama 3.1-8B and Qwen1.5 4b Chat share?
13 benchmarks have published results for both models. Llama 3.1-8B has 43 scored results on Noometry and Qwen1.5 4b Chat has 13.