Model comparison
Llama-3.3-70B-Instruct vs Mercury 2
Mercury 2 is the stronger model overall, scoring 39.1 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 2.4× less per token, which makes it the better buy when Mercury 2's lead doesn't matter for your workload.
Last verified . 15 shared benchmarks.
MetaSummary
- They share 15 benchmarks with published results for both. Llama-3.3-70B-Instruct scores higher in 1 category and Mercury 2 in 6 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in long context, where Mercury 2 leads 40.5 to 26.4.
- The biggest single-benchmark swing is WeirdML: 14.4% for Llama-3.3-70B-Instruct and 43.2% for Mercury 2.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $0.25 / $0.75 for Mercury 2.
- Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.
Side by side
| Llama-3.3-70B-Instruct | Mercury 2 | |
|---|---|---|
| Provider | Meta | Inception |
| Noometry Index | 30.6 | 39.1 |
| Released | 2024-12-06 | 2026-02-20 |
| Weights | Open | Proprietary |
| Context window | 128K | 128K |
| Max output | 4K | 50K |
| Input $ / M tokens | $0.10 | $0.25 |
| Output $ / M tokens | $0.32 | $0.75 |
| Results tracked | 43 | 17 |
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Category by category
Coding Mercury 2 leads
Llama-3.3-70B-Instruct: 31.0 (#290), Mercury 2: 33.5 (#255)
| Benchmark | Llama-3.3-70B-Instruct | Mercury 2 |
|---|---|---|
| SciCode | 26% | 38.7% |
| WeirdML | 14.4% | 43.2% |
| LMArena Coding | 1268 | 1391 |
| LMArena WebDev | — | 1171 |
| BigCodeBench Instruct | 46.9% | — |
| LiveBench Coding | 36.6% | — |
| BigCodeBench Complete | 57.5% | — |
| ALE-Bench | — | 785.58 |
Agentic & Tool Use Not comparable
Llama-3.3-70B-Instruct: 25.8 (#105), Mercury 2: —
| Benchmark | Llama-3.3-70B-Instruct | Mercury 2 |
|---|---|---|
| Berkeley Function Calling Leaderboard | 31.9% | — |
| BALROG | 23% | — |
Reasoning Mercury 2 leads
Llama-3.3-70B-Instruct: 14.1 (#327), Mercury 2: 23.8 (#170)
| Benchmark | Llama-3.3-70B-Instruct | Mercury 2 |
|---|---|---|
| CritPt | 0% | 0.8% |
| LMArena Hard Prompts | 1257 | 1362 |
| SimpleBench | 19.9% | — |
| LiveBench Reasoning | 50.8% | — |
| DTBench | 59.5% | — |
| LiveBench Data Analysis | 49.5% | — |
| LMCA | 17.5% | — |
| Epoch Capabilities Index | 127.33 | — |
| ForecastBench | 58.6 | — |
| LiveBench | 50.2% | — |
Math Not comparable
Llama-3.3-70B-Instruct: 15.3 (#298), Mercury 2: —
| Benchmark | Llama-3.3-70B-Instruct | Mercury 2 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 5.1% | — |
| LiveBench Math | 42.2% | — |
| LMArena Math | 1267 | — |
| MATH Level 5 | 41.6% | — |
Knowledge Mercury 2 leads
Llama-3.3-70B-Instruct: 30.6 (#226), Mercury 2: 36.2 (#172)
| Benchmark | Llama-3.3-70B-Instruct | Mercury 2 |
|---|---|---|
| Vectara Hallucination Rate | 4.1% | 12.3% |
| LMArena Expert | 1225 | 1358 |
| GPQA Diamond | 47.4% | — |
| Confabulations | 22.8% | — |
| MMLU | 86.3% | — |
Multilingual Mercury 2 leads
Llama-3.3-70B-Instruct: 39.9 (#220), Mercury 2: 46.6 (#157)
| Benchmark | Llama-3.3-70B-Instruct | Mercury 2 |
|---|---|---|
| LMArena Non-English | 1236 | 1331 |
| LMArena Chinese | 1217 | 1417 |
| LMArena Russian | 1252 | 1304 |
| LMArena French | 1281 | — |
| LMArena German | 1251 | — |
| LMArena Japanese | 1150 | — |
| LMArena Korean | 1143 | — |
| LMArena Spanish | 1270 | — |
Instruction Following Too close to call
Llama-3.3-70B-Instruct: 71.1 (#157), Mercury 2: 70.2 (#165)
| Benchmark | Llama-3.3-70B-Instruct | Mercury 2 |
|---|---|---|
| LMArena Instruction Following | 1242 | 1329 |
| LiveBench Instruction Following | 82.7% | — |
Long Context Mercury 2 leads
Llama-3.3-70B-Instruct: 26.4 (#295), Mercury 2: 40.5 (#154)
| Benchmark | Llama-3.3-70B-Instruct | Mercury 2 |
|---|---|---|
| LMArena Longer Query | 1256 | 1330 |
| Fiction.LiveBench | 33.3% | — |
Writing & Preference Mercury 2 leads
Llama-3.3-70B-Instruct: 47.6 (#207), Mercury 2: 53.8 (#155)
| Benchmark | Llama-3.3-70B-Instruct | Mercury 2 |
|---|---|---|
| LMArena Text | 1274 | 1355 |
| LMArena Creative Writing | 1250 | 1289 |
| LMArena Multi-Turn | 1280 | 1358 |
| LiveBench Language | 39.2% | — |
Frequently asked questions
Is Llama-3.3-70B-Instruct better than Mercury 2?
Mercury 2 is the stronger model overall, scoring 39.1 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 2.4× less per token, which makes it the better buy when Mercury 2's lead doesn't matter for your workload.
Which is cheaper, Llama-3.3-70B-Instruct or Mercury 2?
Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; Mercury 2 lists at $0.25 and $0.75.
Is Llama-3.3-70B-Instruct or Mercury 2 better for coding?
Mercury 2 scores higher on coding benchmarks: 33.5 versus 31.0 in the Noometry coding category.
Which has the bigger context window?
Both accept 128K tokens.
How many benchmarks do Llama-3.3-70B-Instruct and Mercury 2 share?
15 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and Mercury 2 has 17.