Model comparison
Llama 3-8B vs Mercury 2
Mercury 2 is the stronger model overall, scoring 39.1 to 25.5 on the Noometry Index.
Last verified . 11 shared benchmarks.
MetaSummary
- They share 11 benchmarks with published results for both. Llama 3-8B scores higher in 0 categories and Mercury 2 in 7 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Mercury 2 leads 36.2 to 7.8.
- Llama 3-8B has downloadable open weights; the other is API-only.
Side by side
| Llama 3-8B | Mercury 2 | |
|---|---|---|
| Provider | Meta | Inception |
| Noometry Index | 25.5 | 39.1 |
| Released | 2024-04-18 | 2026-02-20 |
| Weights | Open | Proprietary |
| Context window | — | 128K |
| Max output | — | 50K |
| Input $ / M tokens | — | $0.25 |
| Output $ / M tokens | — | $0.75 |
| Results tracked | 34 | 17 |
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Category by category
Coding Mercury 2 leads
Llama 3-8B: 31.0 (#289), Mercury 2: 33.5 (#255)
| Benchmark | Llama 3-8B | Mercury 2 |
|---|---|---|
| LMArena Coding | 1152 | 1391 |
| LMArena WebDev | — | 1171 |
| SciCode | — | 38.7% |
| WeirdML | — | 43.2% |
| BigCodeBench Instruct | 31.9% | — |
| BigCodeBench Complete | 36.9% | — |
| ALE-Bench | — | 785.58 |
| HumanEval+ | 56.7% | — |
| MBPP+ | 54.8% | — |
Reasoning Mercury 2 leads
Llama 3-8B: 14.3 (#326), Mercury 2: 23.8 (#170)
| Benchmark | Llama 3-8B | Mercury 2 |
|---|---|---|
| LMArena Hard Prompts | 1133 | 1362 |
| CritPt | — | 0.8% |
| Chess Puzzles | 0% | — |
| DTBench | 43.9% | — |
| Adversarial NLI | 57.3% | — |
| Epoch Capabilities Index | 116.45 | — |
| ForecastBench | 58.6 | — |
| WinoGrande | 75.7% | — |
Math Not comparable
Llama 3-8B: 8.8 (#323), Mercury 2: —
| Benchmark | Llama 3-8B | Mercury 2 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 1.9% | — |
| LMArena Math | 1151 | — |
| MATH Level 5 | 6.1% | — |
Knowledge Mercury 2 leads
Llama 3-8B: 7.8 (#308), Mercury 2: 36.2 (#172)
| Benchmark | Llama 3-8B | Mercury 2 |
|---|---|---|
| LMArena Expert | 1113 | 1358 |
| GPQA Diamond | 26.1% | — |
| Vectara Hallucination Rate | — | 12.3% |
| ARC (AI2) Challenge | 82.8% | — |
| MMLU | 68.8% | — |
| OpenBookQA | 82.6% | — |
| TriviaQA | 67.7% | — |
Multilingual Mercury 2 leads
Llama 3-8B: 30.8 (#261), Mercury 2: 46.6 (#157)
| Benchmark | Llama 3-8B | Mercury 2 |
|---|---|---|
| LMArena Non-English | 1098 | 1331 |
| LMArena Chinese | 1076 | 1417 |
| LMArena Russian | 1109 | 1304 |
| LMArena French | 1159 | — |
| LMArena German | 1104 | — |
| LMArena Japanese | 967 | — |
| LMArena Korean | 1004 | — |
| LMArena Spanish | 1173 | — |
Instruction Following Mercury 2 leads
Llama 3-8B: 58.4 (#260), Mercury 2: 70.2 (#165)
| Benchmark | Llama 3-8B | Mercury 2 |
|---|---|---|
| LMArena Instruction Following | 1127 | 1329 |
Long Context Mercury 2 leads
Llama 3-8B: 34.2 (#251), Mercury 2: 40.5 (#154)
| Benchmark | Llama 3-8B | Mercury 2 |
|---|---|---|
| LMArena Longer Query | 1128 | 1330 |
Writing & Preference Mercury 2 leads
Llama 3-8B: 37.5 (#256), Mercury 2: 53.8 (#155)
| Benchmark | Llama 3-8B | Mercury 2 |
|---|---|---|
| LMArena Text | 1166 | 1355 |
| LMArena Creative Writing | 1150 | 1289 |
| LMArena Multi-Turn | 1152 | 1358 |
Frequently asked questions
Is Llama 3-8B better than Mercury 2?
Mercury 2 is the stronger model overall, scoring 39.1 to 25.5 on the Noometry Index.
Is Llama 3-8B or Mercury 2 better for coding?
Mercury 2 scores higher on coding benchmarks: 33.5 versus 31.0 in the Noometry coding category.
How many benchmarks do Llama 3-8B and Mercury 2 share?
11 benchmarks have published results for both models. Llama 3-8B has 34 scored results on Noometry and Mercury 2 has 17.