Model comparison
Llama 3.1-8B vs Mercury 2
Mercury 2 is the stronger model overall, scoring 39.1 to 23.0 on the Noometry Index. Llama 3.1-8B costs 6.5× less per token, which makes it the better buy when Mercury 2's lead doesn't matter for your workload.
Last verified . 14 shared benchmarks.
MetaSummary
- They share 14 benchmarks with published results for both. Llama 3.1-8B 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 8.0.
- The biggest single-benchmark swing is WeirdML: 1.7% for Llama 3.1-8B and 43.2% for Mercury 2.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $0.25 / $0.75 for Mercury 2.
- Llama 3.1-8B has downloadable open weights; the other is API-only.
Side by side
| Llama 3.1-8B | Mercury 2 | |
|---|---|---|
| Provider | Meta | Inception |
| Noometry Index | 23.0 | 39.1 |
| Released | 2024-07-23 | 2026-02-20 |
| Weights | Open | Proprietary |
| Context window | 128K | 128K |
| Max output | 4K | 50K |
| Input $ / M tokens | $0.05 | $0.25 |
| Output $ / M tokens | $0.08 | $0.75 |
| Results tracked | 43 | 17 |
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Category by category
Coding Mercury 2 leads
Llama 3.1-8B: 20.2 (#340), Mercury 2: 33.5 (#255)
| Benchmark | Llama 3.1-8B | Mercury 2 |
|---|---|---|
| SciCode | 13.2% | 38.7% |
| WeirdML | 1.7% | 43.2% |
| LMArena Coding | 1195 | 1391 |
| LMArena WebDev | — | 1171 |
| BigCodeBench Instruct | 32.8% | — |
| BigCodeBench Complete | 40.5% | — |
| ALE-Bench | — | 785.58 |
| HumanEval+ | 62.8% | — |
| MBPP+ | 55.6% | — |
Agentic & Tool Use Not comparable
Llama 3.1-8B: 22.5 (#131), Mercury 2: —
| Benchmark | Llama 3.1-8B | Mercury 2 |
|---|---|---|
| Berkeley Function Calling Leaderboard | 25.8% | — |
| BALROG | 15.1% | — |
Reasoning Mercury 2 leads
Llama 3.1-8B: 14.9 (#321), Mercury 2: 23.8 (#170)
| Benchmark | Llama 3.1-8B | Mercury 2 |
|---|---|---|
| CritPt | 0% | 0.8% |
| LMArena Hard Prompts | 1175 | 1362 |
| 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 2: —
| Benchmark | Llama 3.1-8B | Mercury 2 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 1.7% | — |
| Omni-MATH | 13.7% | — |
| LMArena Math | 1179 | — |
| MATH Level 5 | 22.9% | — |
| GSM8K | 82.4% | — |
Knowledge Mercury 2 leads
Llama 3.1-8B: 8.0 (#307), Mercury 2: 36.2 (#172)
| Benchmark | Llama 3.1-8B | Mercury 2 |
|---|---|---|
| LMArena Expert | 1144 | 1358 |
| GPQA Diamond | 27% | — |
| MMLU-Pro | 40.6% | — |
| Vectara Hallucination Rate | — | 12.3% |
| GPQA (HELM) | 24.7% | — |
| BoolQ | 82.8% | — |
| MMLU | 56.1% | — |
Multilingual Mercury 2 leads
Llama 3.1-8B: 34.0 (#249), Mercury 2: 46.6 (#157)
| Benchmark | Llama 3.1-8B | Mercury 2 |
|---|---|---|
| LMArena Non-English | 1148 | 1331 |
| LMArena Chinese | 1151 | 1417 |
| LMArena Russian | 1158 | 1304 |
| LMArena French | 1177 | — |
| LMArena German | 1144 | — |
| LMArena Japanese | 1061 | — |
| LMArena Korean | 1053 | — |
| LMArena Spanish | 1169 | — |
Instruction Following Mercury 2 leads
Llama 3.1-8B: 58.9 (#258), Mercury 2: 70.2 (#165)
| Benchmark | Llama 3.1-8B | Mercury 2 |
|---|---|---|
| LMArena Instruction Following | 1159 | 1329 |
| IFEval | 74.3% | — |
Long Context Mercury 2 leads
Llama 3.1-8B: 35.8 (#238), Mercury 2: 40.5 (#154)
| Benchmark | Llama 3.1-8B | Mercury 2 |
|---|---|---|
| LMArena Longer Query | 1182 | 1330 |
Writing & Preference Mercury 2 leads
Llama 3.1-8B: 29.7 (#290), Mercury 2: 53.8 (#155)
| Benchmark | Llama 3.1-8B | Mercury 2 |
|---|---|---|
| LMArena Text | 1187 | 1355 |
| LMArena Creative Writing | 1154 | 1289 |
| LMArena Multi-Turn | 1172 | 1358 |
| EQ-Bench Creative Writing | 713 | — |
| WildBench | 68.7% | — |
Frequently asked questions
Is Llama 3.1-8B better than Mercury 2?
Mercury 2 is the stronger model overall, scoring 39.1 to 23.0 on the Noometry Index. Llama 3.1-8B costs 6.5× less per token, which makes it the better buy when Mercury 2's lead doesn't matter for your workload.
Which is cheaper, Llama 3.1-8B or Mercury 2?
Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; Mercury 2 lists at $0.25 and $0.75.
Is Llama 3.1-8B or Mercury 2 better for coding?
Mercury 2 scores higher on coding benchmarks: 33.5 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
Both accept 128K tokens.
How many benchmarks do Llama 3.1-8B and Mercury 2 share?
14 benchmarks have published results for both models. Llama 3.1-8B has 43 scored results on Noometry and Mercury 2 has 17.