Model comparison
Llama 3.1-8B vs Yi-34B
Yi-34B is the stronger model overall, scoring 27.8 to 23.0 on the Noometry Index.
Last verified . 22 shared benchmarks.
MetaSummary
- They share 22 benchmarks with published results for both. Llama 3.1-8B scores higher in 4 categories and Yi-34B in 4 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in coding, where Yi-34B leads 32.3 to 20.2.
- The biggest single-benchmark swing is MATH Level 5: 22.9% for Llama 3.1-8B and 5.1% for Yi-34B.
Side by side
| Llama 3.1-8B | Yi-34B | |
|---|---|---|
| Provider | Meta | 01.AI |
| Noometry Index | 23.0 | 27.8 |
| Released | 2024-07-23 | 2023-11-02 |
| Weights | Open | Open |
| Context window | 128K | — |
| Max output | 4K | — |
| Input $ / M tokens | $0.05 | — |
| Output $ / M tokens | $0.08 | — |
| Results tracked | 43 | 23 |
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Category by category
Coding Yi-34B leads
Llama 3.1-8B: 20.2 (#340), Yi-34B: 32.3 (#274)
| Benchmark | Llama 3.1-8B | Yi-34B |
|---|---|---|
| LMArena Coding | 1195 | 1112 |
| 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), Yi-34B: —
| Benchmark | Llama 3.1-8B | Yi-34B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 25.8% | — |
| BALROG | 15.1% | — |
Reasoning Yi-34B leads
Llama 3.1-8B: 14.9 (#321), Yi-34B: 21.2 (#226)
| Benchmark | Llama 3.1-8B | Yi-34B |
|---|---|---|
| LMArena Hard Prompts | 1175 | 1104 |
| Epoch Capabilities Index | 116.57 | 117.39 |
| CritPt | 0% | — |
| Chess Puzzles | 0% | — |
| DTBench | 50.9% | — |
| LMCA | 5.4% | — |
| BIG-Bench Hard | — | 71.7% |
| PIQA | 81.2% | — |
Math Yi-34B leads
Llama 3.1-8B: 10.2 (#317), Yi-34B: 21.6 (#282)
| Benchmark | Llama 3.1-8B | Yi-34B |
|---|---|---|
| LMArena Math | 1179 | 1114 |
| MATH Level 5 | 22.9% | 5.1% |
| GSM8K | 82.4% | 76% |
| OTIS Mock AIME 2024-2025 | 1.7% | — |
| Omni-MATH | 13.7% | — |
Knowledge Too close to call
Llama 3.1-8B: 8.0 (#307), Yi-34B: 7.5 (#309)
| Benchmark | Llama 3.1-8B | Yi-34B |
|---|---|---|
| GPQA Diamond | 27% | 14.7% |
| LMArena Expert | 1144 | 1061 |
| MMLU | 56.1% | 76.3% |
| MMLU-Pro | 40.6% | — |
| GPQA (HELM) | 24.7% | — |
| BoolQ | 82.8% | — |
Multilingual Llama 3.1-8B leads
Llama 3.1-8B: 34.0 (#249), Yi-34B: 29.7 (#264)
| Benchmark | Llama 3.1-8B | Yi-34B |
|---|---|---|
| LMArena Non-English | 1148 | 1079 |
| LMArena Chinese | 1151 | 1176 |
| LMArena French | 1177 | 1081 |
| LMArena German | 1144 | 1042 |
| LMArena Japanese | 1061 | 993 |
| LMArena Korean | 1053 | 959 |
| LMArena Russian | 1158 | 1050 |
| LMArena Spanish | 1169 | 1070 |
Instruction Following Llama 3.1-8B leads
Llama 3.1-8B: 58.9 (#258), Yi-34B: 56.2 (#274)
| Benchmark | Llama 3.1-8B | Yi-34B |
|---|---|---|
| LMArena Instruction Following | 1159 | 1091 |
| IFEval | 74.3% | — |
Long Context Llama 3.1-8B leads
Llama 3.1-8B: 35.8 (#238), Yi-34B: 33.2 (#264)
| Benchmark | Llama 3.1-8B | Yi-34B |
|---|---|---|
| LMArena Longer Query | 1182 | 1094 |
Writing & Preference Yi-34B leads
Llama 3.1-8B: 29.7 (#290), Yi-34B: 34.1 (#273)
| Benchmark | Llama 3.1-8B | Yi-34B |
|---|---|---|
| LMArena Text | 1187 | 1129 |
| LMArena Creative Writing | 1154 | 1108 |
| LMArena Multi-Turn | 1172 | 1113 |
| EQ-Bench Creative Writing | 713 | — |
| WildBench | 68.7% | — |
Frequently asked questions
Is Llama 3.1-8B better than Yi-34B?
Yi-34B is the stronger model overall, scoring 27.8 to 23.0 on the Noometry Index.
Is Llama 3.1-8B or Yi-34B better for coding?
Yi-34B scores higher on coding benchmarks: 32.3 versus 20.2 in the Noometry coding category.
How many benchmarks do Llama 3.1-8B and Yi-34B share?
22 benchmarks have published results for both models. Llama 3.1-8B has 43 scored results on Noometry and Yi-34B has 23.