Mercury 2 vs Llama 4 Scout
Mercury 2 leads Llama 4 Scout on the Artificial Analysis Intelligence Index, 10.8 to 6.5, and Llama 4 Scout costs 1.5x less per output token ($0.50 vs $0.75 per million). Updated October 22, 2026; 34 benchmarks have a result for both.
Mercury 2 or Llama 4 Scout: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 34
- Mercury 2 only
- 8
- Llama 4 Scout only
- 48
- Like-for-like categories
- 7 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
Mercury 2
Mercury 2 leads, 10.8 against 6.5.
Same board, both measured
Agentic work
AA Agentic Index
Mercury 2
Mercury 2 leads, 1.6 against 0.5.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only Llama 4 Scout has a GPQA Diamond result, 47.0%.
Directional only
Math
AIME
No call
Only Llama 4 Scout has a AIME result, 19.0%.
Directional only
Knowledge
MMLU Pro
No call
Only Llama 4 Scout has a MMLU Pro result, 69.6%.
Directional only
Web design
Design Arena: websites
Mercury 2
Mercury 2 leads, 1036 against 750.
Same board, both measured
Long documents
Listed context window
Llama 4 Scout
Llama 4 Scout takes 1.31M tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 4 Scout
Mercury 2 $0.0006, Llama 4 Scout $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Llama 4 Scout has the number, $1.276.
Directional only
At a glance
- Coding index
- Mercury 2 not benchmarked, Llama 4 Scout not benchmarked.
- Widest category
- Agentic work, 28.2 points apart on average over 2 shared benchmarks.
- Widest single gap
- τ²-Bench Telecom: Mercury 2 70.8% against Llama 4 Scout 15.5%, 55.3 points apart on a 0 to 100 scale.
- Output price
- Mercury 2 $0.75 against Llama 4 Scout $0.50 per million output tokens, 1.5x apart.
- Context window
- Mercury 2 takes 128K tokens, Llama 4 Scout 1.31M.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | Mercury 2 | Bars | Llama 4 Scout | Ties |
|---|---|---|---|---|
| Coding | 5 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 2 | 0 | ||
| Reasoning | 5 | 0 | ||
| Knowledge | 3 | 0 | ||
| Web design | 5 | 0 | ||
| General | 13 | 0 | ||
| All shared boards | 34 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index50
| Benchmark | Mercury 2 | Llama 4 Scout | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 9.1% | Only Llama 4 Scout measured |
| LMArena WebDev | 1171 | Not measured | Only Mercury 2 measured |
| AA Coding Index | 31.1 | 8.2 | Mercury 2 |
| LiveCodeBench (Vals AI) | Not measured | 38.5% | Only Llama 4 Scout measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 29.9% | Only Llama 4 Scout measured |
| SciCode | 37.7% | 21.3% | Mercury 2 |
| Terminal-Bench Hard (Artificial Analysis run) | 26.5% | 1.5% | Mercury 2 |
| WeirdML | 43.2% | Not measured | Only Mercury 2 measured |
| ALE-Bench | 785.6 | Not measured | Only Mercury 2 measured |
| LMArena Text: coding | 1390 | 1286 | Mercury 2 |
| LMArena Text: software and IT | 1385 | 1286 | Mercury 2 |
| LMArena WebDev: React | 1160 | Not measured | Only Mercury 2 measured |
| LMArena WebDev: HTML | 1199 | Not measured | Only Mercury 2 measured |
| Neither model has a result on the other 26 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Mercury 2 | Llama 4 Scout | Leader |
|---|---|---|---|
| AA Intelligence Index | 10.8 | 6.5 | Mercury 2 |
| Epoch Capabilities Index | Not measured | 129.6 | Only Llama 4 Scout measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Mercury 2 | Llama 4 Scout | Leader |
|---|---|---|---|
| AA Agentic Index | 1.6 | 0.5 | Mercury 2 |
| τ²-Bench Telecom | 70.8% | 15.5% | Mercury 2 |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 28.1% | Only Llama 4 Scout measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | Mercury 2 | Llama 4 Scout | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 47.0% | Only Llama 4 Scout measured |
| MMMU Pro (Vals AI) | Not measured | 58.8% | Only Llama 4 Scout measured |
| GPQA Diamond (Artificial Analysis run) | 77.0% | 58.7% | Mercury 2 |
| Humanity's Last Exam (Artificial Analysis run) | 17.1% | 3.8% | Mercury 2 |
| GPQA Diamond (Epoch AI run) | Not measured | 51.8% | Only Llama 4 Scout measured |
| ARC-AGI | Not measured | 0.5% | Only Llama 4 Scout measured |
| ARC-AGI-2 | Not measured | 0.0% | Only Llama 4 Scout measured |
| LMArena Text: hard prompts | 1362 | 1266 | Mercury 2 |
| LMArena Text: expert | 1358 | 1235 | Mercury 2 |
| LMArena Text: hard prompts in English | 1374 | 1293 | Mercury 2 |
| LMArena Vision: diagrams | Not measured | 1125 | Only Llama 4 Scout measured |
| LMArena Vision: homework | Not measured | 1148 | Only Llama 4 Scout measured |
| Kagi LLM Benchmark | Not measured | 36.9% | Only Llama 4 Scout measured |
| Neither model has a result on the other 9 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | Mercury 2 | Llama 4 Scout | Leader |
|---|---|---|---|
| AIME | Not measured | 19.0% | Only Llama 4 Scout measured |
| MATH 500 (Vals AI) | Not measured | 79.2% | Only Llama 4 Scout measured |
| MGSM (Vals AI) | Not measured | 88.0% | Only Llama 4 Scout measured |
| AA Math Index | Not measured | 14.0 | Only Llama 4 Scout measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 14.0% | Only Llama 4 Scout measured |
| OTIS Mock AIME | Not measured | 7.8% | Only Llama 4 Scout measured |
| MATH level 5 | Not measured | 62.3% | Only Llama 4 Scout measured |
| LMArena Text: math | Not measured | 1287 | Only Llama 4 Scout measured |
| LMArena Text: mathematical work | Not measured | 1286 | Only Llama 4 Scout measured |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | Mercury 2 | Llama 4 Scout | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 69.6% | Only Llama 4 Scout measured |
| LegalBench (Vals AI) | Not measured | 72.0% | Only Llama 4 Scout measured |
| CorpFin (Vals AI) | Not measured | 46.8% | Only Llama 4 Scout measured |
| TaxEval (Vals AI) | Not measured | 55.2% | Only Llama 4 Scout measured |
| MortgageTax (Vals AI) | Not measured | 57.8% | Only Llama 4 Scout measured |
| MedQA (Vals AI) | Not measured | 50.9% | Only Llama 4 Scout measured |
| MedCode (Vals AI) | Not measured | 23.3% | Only Llama 4 Scout measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 75.2% | Only Llama 4 Scout measured |
| LMArena Text: science | 1343 | 1278 | Mercury 2 |
| LMArena Text: medicine and healthcare | 1363 | 1275 | Mercury 2 |
| LMArena Text: legal and government | Not measured | 1285 | Only Llama 4 Scout measured |
| LMArena Text: business and finance | 1349 | 1257 | Mercury 2 |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | Mercury 2 | Llama 4 Scout | Leader |
|---|---|---|---|
| Design Arena: websites | 1036 | 750 | Mercury 2 |
| Design Arena: UI components | 996 | 765 | Mercury 2 |
| Design Arena: game dev | 984 | 772 | Mercury 2 |
| Design Arena: data viz | 1009 | 896 | Mercury 2 |
| Design Arena: 3D | 982 | Not measured | Only Mercury 2 measured |
| Design Arena: all code categories | 1021 | 789 | Mercury 2 |
| Design Arena: SVG | 998 | Not measured | Only Mercury 2 measured |
| Design Arena: ASCII art | 988 | Not measured | Only Mercury 2 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index130
| Benchmark | Mercury 2 | Llama 4 Scout | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 34.8% | Only Llama 4 Scout measured |
| MedScribe (Vals AI) | Not measured | 50.6% | Only Llama 4 Scout measured |
| IFBench | 69.8% | 39.5% | Mercury 2 |
| Long context reasoning (AA-LCR) | 43.7% | 27.7% | Mercury 2 |
| DTBench | Not measured | 57.9% | Only Llama 4 Scout measured |
| LMCA | Not measured | 12.0% | Only Llama 4 Scout measured |
| Fiction.LiveBench | Not measured | 36.0% | Only Llama 4 Scout measured |
| LMArena Text | 1354 | 1279 | Mercury 2 |
| LMArena Text: instruction following | 1328 | 1248 | Mercury 2 |
| LMArena Text: creative writing | 1286 | 1249 | Mercury 2 |
| LMArena Vision | Not measured | 1118 | Only Llama 4 Scout measured |
| LMArena Text: English | 1378 | 1304 | Mercury 2 |
| LMArena Text: non-English | 1330 | 1252 | Mercury 2 |
| LMArena Text: Chinese | 1414 | 1255 | Mercury 2 |
| LMArena Text: German | Not measured | 1273 | Only Llama 4 Scout measured |
| LMArena Text: Japanese | Not measured | 1206 | Only Llama 4 Scout measured |
| LMArena Text: Korean | Not measured | 1207 | Only Llama 4 Scout measured |
| LMArena Text: French | Not measured | 1283 | Only Llama 4 Scout measured |
| LMArena Text: Spanish | Not measured | 1278 | Only Llama 4 Scout measured |
| LMArena Text: Russian | 1304 | 1263 | Mercury 2 |
| LMArena Text: Polish | Not measured | 1265 | Only Llama 4 Scout measured |
| LMArena Text: multi-turn | 1357 | 1280 | Mercury 2 |
| LMArena Text: longer queries | 1329 | 1265 | Mercury 2 |
| LMArena Text: writing and language | 1300 | 1269 | Mercury 2 |
| LMArena Text: entertainment and media | 1314 | 1242 | Mercury 2 |
| LMArena Vision: English | Not measured | 1143 | Only Llama 4 Scout measured |
| LMArena Vision: Chinese | Not measured | 1106 | Only Llama 4 Scout measured |
| LMArena Vision: reading text | Not measured | 1117 | Only Llama 4 Scout measured |
| LMArena Vision: humor | Not measured | 1102 | Only Llama 4 Scout measured |
| LMArena Vision: creative writing | Not measured | 1126 | Only Llama 4 Scout measured |
| Neither model has a result on the other 16 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mercury 2 | Llama 4 Scout | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Llama 4 Scout costs less
Repository review
50K input + 3K output tokens
Llama 4 Scout costs less
Agent loop
200K input + 10K output tokens
Llama 4 Scout costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Inception Mercury 2 | Meta Llama 4 Scout |
|---|---|---|
| Maker | Inception | Meta Llama |
| Weights | No public weights listed | Open weights |
| Released | March 4, 2026 | April 5, 2025 |
| Context window | 128K | 1.31M |
| Input $/Mtok | $0.25 | $0.175 |
| Output $/Mtok | $0.75 | $0.50 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | Not computable | $1.276 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Mercury 2 better than Llama 4 Scout for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, Mercury 2 or Llama 4 Scout?
Mercury 2 costs $0.25 per million input tokens and $0.75 per million output tokens; Llama 4 Scout costs $0.175 and $0.50. Cost per solved task is known for Llama 4 Scout only.
Which has the bigger context window, Mercury 2 or Llama 4 Scout?
Llama 4 Scout, with 1.31M tokens against 128K.
How many benchmarks is the Mercury 2 vs Llama 4 Scout comparison based on?
34 benchmarks have a result for both models as of October 22, 2026. Mercury 2's coding index rests on 1 independent benchmark and Llama 4 Scout's on 0. Where only one model has a result, the page says so instead of naming a winner.