Model comparison
Llama 3.1-8B vs Qwen-14B
Qwen-14B is the stronger model overall, scoring 30.8 to 22.8 on the Noometry Index.
Last verified . 15 shared benchmarks.
Meta
Alibaba (Qwen)Summary
- They share 15 benchmarks with published results for both. Llama 3.1-8B scores higher in 4 categories and Qwen-14B in 3 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen-14B leads 28.7 to 9.5.
Side by side
| Llama 3.1-8B | Qwen-14B | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 22.8 | 30.8 |
| Released | 2024-07-23 | 2023-09-24 |
| Weights | Open | Open |
| Context window | 128K | — |
| Max output | 4K | — |
| Input $ / M tokens | $0.05 | — |
| Output $ / M tokens | $0.08 | — |
| Results tracked | 43 | 18 |
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Category by category
Coding Qwen-14B leads
Llama 3.1-8B: 19.9 (#335), Qwen-14B: 30.6 (#283)
| Benchmark | Llama 3.1-8B | Qwen-14B |
|---|---|---|
| LMArena Coding | 1195 | 1072 |
| 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: 22.3 (#123), Qwen-14B: —
| Benchmark | Llama 3.1-8B | Qwen-14B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 25.8% | — |
| BALROG | 15.1% | — |
Reasoning Qwen-14B leads
Llama 3.1-8B: 14.5 (#316), Qwen-14B: 18.7 (#255)
| Benchmark | Llama 3.1-8B | Qwen-14B |
|---|---|---|
| LMArena Hard Prompts | 1175 | 1027 |
| Epoch Capabilities Index | 116.48 | 112.8 |
| PIQA | 81.2% | 79.9% |
| CritPt | 0% | — |
| Chess Puzzles | 0% | — |
| DTBench | 50.9% | — |
| LMCA | 5.4% | — |
| BIG-Bench Hard | — | 55% |
| LAMBADA | — | 71.1% |
Math Qwen-14B leads
Llama 3.1-8B: 9.5 (#311), Qwen-14B: 28.7 (#224)
| Benchmark | Llama 3.1-8B | Qwen-14B |
|---|---|---|
| LMArena Math | 1179 | 1068 |
| GSM8K | 82.4% | 61.3% |
| OTIS Mock AIME 2024-2025 | 1.7% | — |
| Omni-MATH | 13.7% | — |
| MATH Level 5 | 22.9% | — |
Knowledge Not comparable
Llama 3.1-8B: 8.0 (#302), Qwen-14B: —
| Benchmark | Llama 3.1-8B | Qwen-14B |
|---|---|---|
| BoolQ | 82.8% | 86.2% |
| MMLU | 56.1% | 66.3% |
| GPQA Diamond | 27% | — |
| MMLU-Pro | 40.6% | — |
| GPQA (HELM) | 24.7% | — |
| LMArena Expert | 1144 | — |
| ARC (AI2) Challenge | — | 84.4% |
Multilingual Llama 3.1-8B leads
Llama 3.1-8B: 34.2 (#244), Qwen-14B: 27.7 (#270)
| Benchmark | Llama 3.1-8B | Qwen-14B |
|---|---|---|
| LMArena Non-English | 1148 | 1042 |
| LMArena Chinese | 1151 | 1077 |
| LMArena French | 1176 | — |
| LMArena German | 1144 | — |
| LMArena Japanese | 1061 | — |
| LMArena Korean | 1053 | — |
| LMArena Russian | 1158 | — |
| LMArena Spanish | 1169 | — |
Instruction Following Llama 3.1-8B leads
Llama 3.1-8B: 59.1 (#253), Qwen-14B: 52.6 (#284)
| Benchmark | Llama 3.1-8B | Qwen-14B |
|---|---|---|
| LMArena Instruction Following | 1159 | 1031 |
| IFEval | 74.3% | — |
Long Context Llama 3.1-8B leads
Llama 3.1-8B: 35.8 (#233), Qwen-14B: 31.3 (#276)
| Benchmark | Llama 3.1-8B | Qwen-14B |
|---|---|---|
| LMArena Longer Query | 1182 | 1029 |
Writing & Preference Llama 3.1-8B leads
Llama 3.1-8B: 29.9 (#285), Qwen-14B: 27.8 (#294)
| Benchmark | Llama 3.1-8B | Qwen-14B |
|---|---|---|
| LMArena Text | 1187 | 1051 |
| LMArena Creative Writing | 1154 | 1028 |
| LMArena Multi-Turn | 1172 | 1022 |
| EQ-Bench Creative Writing | 713 | — |
| WildBench | 68.7% | — |
Frequently asked questions
Is Llama 3.1-8B better than Qwen-14B?
Qwen-14B is the stronger model overall, scoring 30.8 to 22.8 on the Noometry Index.
Is Llama 3.1-8B or Qwen-14B better for coding?
Qwen-14B scores higher on coding benchmarks: 30.6 versus 19.9 in the Noometry coding category.
How many benchmarks do Llama 3.1-8B and Qwen-14B share?
15 benchmarks have published results for both models. Llama 3.1-8B has 43 scored results on Noometry and Qwen-14B has 18.