Model comparison
Llama 4 Scout vs Mercury 2.5
Mercury 2.5 is the stronger model overall, scoring 33.5 to 27.7 on the Noometry Index.
Last verified . 2 shared benchmarks.
MetaSummary
- They share 2 benchmarks with published results for both. Llama 4 Scout scores higher in 0 categories and Mercury 2.5 in 3 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in coding, where Mercury 2.5 leads 39.5 to 20.2.
- The biggest single-benchmark swing is SciCode: 17% for Llama 4 Scout and 38.5% for Mercury 2.5.
- Mercury 2.5 is cheaper at $0.04 / $0.15 per million input/output tokens, against $0.10 / $0.30 for Llama 4 Scout.
- Mercury 2.5 accepts more context: 260K tokens versus 128K.
- Llama 4 Scout has downloadable open weights; the other is API-only.
Side by side
| Llama 4 Scout | Mercury 2.5 | |
|---|---|---|
| Provider | Meta | Inception |
| Noometry Index | 27.7 | 33.5 |
| Released | 2025-04-05 | 2026-09-08 |
| Weights | Open | Proprietary |
| Context window | 128K | 260K |
| Max output | 4K | 66K |
| Input $ / M tokens | $0.10 | $0.04 |
| Output $ / M tokens | $0.30 | $0.15 |
| Results tracked | 43 | 4 |
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Category by category
Coding Mercury 2.5 leads
Llama 4 Scout: 20.2 (#339), Mercury 2.5: 39.5 (#156)
| Benchmark | Llama 4 Scout | Mercury 2.5 |
|---|---|---|
| SciCode | 17% | 38.5% |
| SWE-bench Verified (bash only) | 9.1% | — |
| LMArena Coding | 1286 | — |
| BigCodeBench Complete | 43.1% | — |
| ALE-Bench | — | 301.65 |
Agentic & Tool Use Not comparable
Llama 4 Scout: 24.6 (#119), Mercury 2.5: —
| Benchmark | Llama 4 Scout | Mercury 2.5 |
|---|---|---|
| Berkeley Function Calling Leaderboard | 28.1% | — |
Reasoning Mercury 2.5 leads
Llama 4 Scout: 9.1 (#345), Mercury 2.5: 22.4 (#193)
| Benchmark | Llama 4 Scout | Mercury 2.5 |
|---|---|---|
| CritPt | 0% | 0% |
| ARC-AGI-2 | 0% | — |
| Kagi LLM Benchmark | 36.9% | — |
| ARC-AGI-1 | 0.5% | — |
| LMArena Hard Prompts | 1266 | — |
| DTBench | 57.9% | — |
| LMCA | 12% | — |
| Epoch Capabilities Index | 129.64 | — |
| ForecastBench | 57.5 | — |
Math Mercury 2.5 leads
Llama 4 Scout: 19.6 (#286), Mercury 2.5: 23.3 (#272)
| Benchmark | Llama 4 Scout | Mercury 2.5 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 7.8% | — |
| ProofBench | — | 3% |
| Omni-MATH | 37.3% | — |
| LMArena Math | 1287 | — |
| MATH Level 5 | 62.3% | — |
| FrontierMath (Feb 2025 set) | 0% | — |
Knowledge Not comparable
Llama 4 Scout: 31.9 (#217), Mercury 2.5: —
| Benchmark | Llama 4 Scout | Mercury 2.5 |
|---|---|---|
| GPQA Diamond | 51.8% | — |
| MMLU-Pro | 74.2% | — |
| Vectara Hallucination Rate | 7.7% | — |
| GPQA (HELM) | 50.7% | — |
| LMArena Expert | 1235 | — |
Multimodal Not comparable
Llama 4 Scout: 32.2 (#102), Mercury 2.5: —
| Benchmark | Llama 4 Scout | Mercury 2.5 |
|---|---|---|
| LMArena Vision | 1118 | — |
| SpatialViz-Bench | 34.2% | — |
Multilingual Not comparable
Llama 4 Scout: 41.0 (#212), Mercury 2.5: —
| Benchmark | Llama 4 Scout | Mercury 2.5 |
|---|---|---|
| LMArena Non-English | 1252 | — |
| LMArena Chinese | 1255 | — |
| LMArena French | 1282 | — |
| LMArena German | 1272 | — |
| LMArena Japanese | 1206 | — |
| LMArena Korean | 1207 | — |
| LMArena Russian | 1263 | — |
| LMArena Spanish | 1278 | — |
Instruction Following Not comparable
Llama 4 Scout: 65.8 (#217), Mercury 2.5: —
| Benchmark | Llama 4 Scout | Mercury 2.5 |
|---|---|---|
| IFEval | 81.8% | — |
| LMArena Instruction Following | 1248 | — |
Long Context Not comparable
Llama 4 Scout: 27.5 (#294), Mercury 2.5: —
| Benchmark | Llama 4 Scout | Mercury 2.5 |
|---|---|---|
| Fiction.LiveBench | 36% | — |
| LMArena Longer Query | 1265 | — |
Writing & Preference Not comparable
Llama 4 Scout: 37.0 (#261), Mercury 2.5: —
| Benchmark | Llama 4 Scout | Mercury 2.5 |
|---|---|---|
| LMArena Text | 1279 | — |
| LMArena Creative Writing | 1249 | — |
| EQ-Bench Creative Writing | 783 | — |
| WildBench | 78% | — |
| LMArena Multi-Turn | 1280 | — |
Frequently asked questions
Is Llama 4 Scout better than Mercury 2.5?
Mercury 2.5 is the stronger model overall, scoring 33.5 to 27.7 on the Noometry Index.
Which is cheaper, Llama 4 Scout 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 4 Scout lists at $0.10 and $0.30.
Is Llama 4 Scout or Mercury 2.5 better for coding?
Mercury 2.5 scores higher on coding benchmarks: 39.5 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
Mercury 2.5 does, with 260K tokens against 128K.
How many benchmarks do Llama 4 Scout and Mercury 2.5 share?
2 benchmarks have published results for both models. Llama 4 Scout has 43 scored results on Noometry and Mercury 2.5 has 4.