Model comparison
Llama 3.1-8B vs Mercury
Mercury is the stronger model overall, scoring 37.6 to 23.0 on the Noometry Index.
Last verified . 8 shared benchmarks.
MetaSummary
- They share 8 benchmarks with published results for both. Llama 3.1-8B scores higher in 0 categories and Mercury in 6 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in coding, where Mercury leads 38.7 to 20.2.
- Llama 3.1-8B has downloadable open weights; the other is API-only.
Side by side
| Llama 3.1-8B | Mercury | |
|---|---|---|
| Provider | Meta | Inception |
| Noometry Index | 23.0 | 37.6 |
| Released | 2024-07-23 | — |
| Weights | Open | Proprietary |
| Context window | 128K | — |
| Max output | 4K | — |
| Input $ / M tokens | $0.05 | — |
| Output $ / M tokens | $0.08 | — |
| Results tracked | 43 | 9 |
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Category by category
Coding Mercury leads
Llama 3.1-8B: 20.2 (#340), Mercury: 38.7 (#170)
| Benchmark | Llama 3.1-8B | Mercury |
|---|---|---|
| LMArena Coding | 1195 | 1322 |
| 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), Mercury: —
| Benchmark | Llama 3.1-8B | Mercury |
|---|---|---|
| Berkeley Function Calling Leaderboard | 25.8% | — |
| BALROG | 15.1% | — |
Reasoning Mercury leads
Llama 3.1-8B: 14.9 (#321), Mercury: 17.5 (#293)
| Benchmark | Llama 3.1-8B | Mercury |
|---|---|---|
| LMArena Hard Prompts | 1175 | 1285 |
| Kagi LLM Benchmark | — | 21.6% |
| CritPt | 0% | — |
| Chess Puzzles | 0% | — |
| DTBench | 50.9% | — |
| LMCA | 5.4% | — |
| Epoch Capabilities Index | 116.57 | — |
| PIQA | 81.2% | — |
Math Not comparable
Llama 3.1-8B: 10.2 (#317), Mercury: —
| Benchmark | Llama 3.1-8B | Mercury |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 1.7% | — |
| Omni-MATH | 13.7% | — |
| LMArena Math | 1179 | — |
| MATH Level 5 | 22.9% | — |
| GSM8K | 82.4% | — |
Knowledge Not comparable
Llama 3.1-8B: 8.0 (#307), Mercury: —
| Benchmark | Llama 3.1-8B | Mercury |
|---|---|---|
| GPQA Diamond | 27% | — |
| MMLU-Pro | 40.6% | — |
| GPQA (HELM) | 24.7% | — |
| LMArena Expert | 1144 | — |
| BoolQ | 82.8% | — |
| MMLU | 56.1% | — |
Multilingual Mercury leads
Llama 3.1-8B: 34.0 (#249), Mercury: 41.6 (#206)
| Benchmark | Llama 3.1-8B | Mercury |
|---|---|---|
| LMArena Non-English | 1148 | 1260 |
| LMArena Chinese | 1151 | — |
| LMArena French | 1177 | — |
| LMArena German | 1144 | — |
| LMArena Japanese | 1061 | — |
| LMArena Korean | 1053 | — |
| LMArena Russian | 1158 | — |
| LMArena Spanish | 1169 | — |
Instruction Following Mercury leads
Llama 3.1-8B: 58.9 (#258), Mercury: 65.2 (#224)
| Benchmark | Llama 3.1-8B | Mercury |
|---|---|---|
| LMArena Instruction Following | 1159 | 1239 |
| IFEval | 74.3% | — |
Long Context Mercury leads
Llama 3.1-8B: 35.8 (#238), Mercury: 38.4 (#198)
| Benchmark | Llama 3.1-8B | Mercury |
|---|---|---|
| LMArena Longer Query | 1182 | 1266 |
Writing & Preference Mercury leads
Llama 3.1-8B: 29.7 (#290), Mercury: 46.2 (#221)
| Benchmark | Llama 3.1-8B | Mercury |
|---|---|---|
| LMArena Text | 1187 | 1282 |
| LMArena Creative Writing | 1154 | 1191 |
| LMArena Multi-Turn | 1172 | 1282 |
| EQ-Bench Creative Writing | 713 | — |
| WildBench | 68.7% | — |
Frequently asked questions
Is Llama 3.1-8B better than Mercury?
Mercury is the stronger model overall, scoring 37.6 to 23.0 on the Noometry Index.
Is Llama 3.1-8B or Mercury better for coding?
Mercury scores higher on coding benchmarks: 38.7 versus 20.2 in the Noometry coding category.
How many benchmarks do Llama 3.1-8B and Mercury share?
8 benchmarks have published results for both models. Llama 3.1-8B has 43 scored results on Noometry and Mercury has 9.