Model comparison
Llama 3.1-70B vs Mercury 2.5
Llama 3.1-70B has enough public results to be ranked (#299); Mercury 2.5 does not yet, so treat this comparison as directional.
Last verified . 0 shared benchmarks.
MetaSummary
- The widest gap is in math, where Mercury 2.5 leads 22.4 to 12.7.
- Mercury 2.5 is cheaper at $0.04 / $0.15 per million input/output tokens, against $0.40 / $0.40 for Llama 3.1-70B.
- Mercury 2.5 accepts more context: 260K tokens versus 128K.
- Llama 3.1-70B has downloadable open weights; the other is API-only.
Side by side
| Llama 3.1-70B | Mercury 2.5 | |
|---|---|---|
| Provider | Meta | Inception |
| Noometry Index | 29.4 | 30.2 |
| Released | 2024-07-23 | 2026-09-08 |
| Weights | Open | Proprietary |
| Context window | 128K | 260K |
| Max output | 4K | 66K |
| Input $ / M tokens | $0.40 | $0.04 |
| Output $ / M tokens | $0.40 | $0.15 |
| Results tracked | 35 | 2 |
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Category by category
Coding Not comparable
Llama 3.1-70B: 29.8 (#292), Mercury 2.5: —
| Benchmark | Llama 3.1-70B | Mercury 2.5 |
|---|---|---|
| WeirdML | 9% | — |
| BigCodeBench Instruct | 46.1% | — |
| LMArena Coding | 1260 | — |
| BigCodeBench Complete | 54.8% | — |
| ALE-Bench | — | 301.65 |
Agentic & Tool Use Not comparable
Llama 3.1-70B: 24.9 (#104), Mercury 2.5: —
| Benchmark | Llama 3.1-70B | Mercury 2.5 |
|---|---|---|
| TheAgentCompany | 6.9% | — |
| BALROG | 27.9% | — |
Reasoning Not comparable
Llama 3.1-70B: 20.8 (#215), Mercury 2.5: —
| Benchmark | Llama 3.1-70B | Mercury 2.5 |
|---|---|---|
| LMArena Hard Prompts | 1241 | — |
| DTBench | 60% | — |
| LMCA | 14.8% | — |
| Epoch Capabilities Index | 125.91 | — |
Math Mercury 2.5 leads
Llama 3.1-70B: 12.7 (#298), Mercury 2.5: 22.4
| Benchmark | Llama 3.1-70B | Mercury 2.5 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 3.6% | — |
| ProofBench | — | 3% |
| Omni-MATH | 21% | — |
| LMArena Math | 1252 | — |
| MATH Level 5 | 36.7% | — |
Knowledge Not comparable
Llama 3.1-70B: 24.1 (#264), Mercury 2.5: —
| Benchmark | Llama 3.1-70B | Mercury 2.5 |
|---|---|---|
| GPQA Diamond | 44.2% | — |
| MMLU-Pro | 65.3% | — |
| GPQA (HELM) | 42.6% | — |
| LMArena Expert | 1209 | — |
| MMLU | 80.1% | — |
Multilingual Not comparable
Llama 3.1-70B: 38.9 (#220), Mercury 2.5: —
| Benchmark | Llama 3.1-70B | Mercury 2.5 |
|---|---|---|
| LMArena Non-English | 1219 | — |
| LMArena Chinese | 1215 | — |
| LMArena French | 1260 | — |
| LMArena German | 1222 | — |
| LMArena Japanese | 1132 | — |
| LMArena Korean | 1140 | — |
| LMArena Russian | 1234 | — |
| LMArena Spanish | 1253 | — |
Instruction Following Not comparable
Llama 3.1-70B: 65.5 (#218), Mercury 2.5: —
| Benchmark | Llama 3.1-70B | Mercury 2.5 |
|---|---|---|
| IFEval | 82.1% | — |
| LMArena Instruction Following | 1231 | — |
Long Context Not comparable
Llama 3.1-70B: 37.6 (#208), Mercury 2.5: —
| Benchmark | Llama 3.1-70B | Mercury 2.5 |
|---|---|---|
| LMArena Longer Query | 1241 | — |
Writing & Preference Not comparable
Llama 3.1-70B: 35.6 (#262), Mercury 2.5: —
| Benchmark | Llama 3.1-70B | Mercury 2.5 |
|---|---|---|
| LMArena Text | 1261 | — |
| LMArena Creative Writing | 1232 | — |
| EQ-Bench Creative Writing | 784 | — |
| WildBench | 75.8% | — |
| LMArena Multi-Turn | 1256 | — |
Frequently asked questions
Is Llama 3.1-70B better than Mercury 2.5?
Llama 3.1-70B has enough public results to be ranked (#299); Mercury 2.5 does not yet, so treat this comparison as directional.
Which is cheaper, Llama 3.1-70B or Mercury 2.5?
Mercury 2.5 is cheaper. It lists at $0.04 per million input tokens and $0.15 per million output tokens; Llama 3.1-70B lists at $0.40 and $0.40.
Which has the bigger context window?
Mercury 2.5 does, with 260K tokens against 128K.
How many benchmarks do Llama 3.1-70B and Mercury 2.5 share?
0 benchmarks have published results for both models. Llama 3.1-70B has 35 scored results on Noometry and Mercury 2.5 has 2.