GPT-4.1 Nano vs Llama 4 Scout
GPT-4.1 Nano leads Llama 4 Scout on the Artificial Analysis Intelligence Index, 7.8 to 6.5, and GPT-4.1 Nano costs 1.3x less per output token ($0.40 vs $0.50 per million). Updated October 22, 2026; 66 benchmarks have a result for both.
GPT-4.1 Nano or Llama 4 Scout: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-4.1 Nano for output tokens 1.3x cheaper ($0.40 vs $0.50 per million) and work measured by Epoch Capabilities Index or MMMU Pro (Vals AI), where it leads.
- Shared results
- 66
- GPT-4.1 Nano only
- 5
- Llama 4 Scout only
- 16
- Like-for-like categories
- 8 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
GPT-4.1 Nano
GPT-4.1 Nano leads, 7.8 against 6.5.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Llama 4 Scout has a AA Agentic Index result, 0.5.
Directional only
Reasoning
GPQA Diamond
GPT-4.1 Nano
GPT-4.1 Nano leads, 50.8% against 47.0%.
Same board, both measured
Math
AIME
GPT-4.1 Nano
GPT-4.1 Nano leads, 26.5% against 19.0%.
Same board, both measured
Knowledge
MMLU Pro
Llama 4 Scout
Llama 4 Scout leads, 69.6% against 63.5%.
Same board, both measured
Web design
Design Arena: websites
GPT-4.1 Nano
GPT-4.1 Nano leads, 973 against 750.
Same board, both measured
Long documents
Listed context window
Llama 4 Scout
Llama 4 Scout takes 1.31M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-4.1 Nano
GPT-4.1 Nano $0.0003, 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
- GPT-4.1 Nano not benchmarked, Llama 4 Scout not benchmarked.
- Widest category
- Web design, 18.7 points apart on average over 5 shared benchmarks.
- Widest single gap
- Design Arena: websites: GPT-4.1 Nano 973 against Llama 4 Scout 750, 27.9 points apart on a 0 to 100 scale.
- Output price
- GPT-4.1 Nano $0.40 against Llama 4 Scout $0.50 per million output tokens, 1.3x apart.
- Context window
- GPT-4.1 Nano takes 1.05M 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 | GPT-4.1 Nano | Bars | Llama 4 Scout | Ties |
|---|---|---|---|---|
| Coding | 6 | 0 | ||
| Intelligence | 1 | 1 | ||
| Agentic work | 2 | 0 | ||
| Reasoning | 3 | 6 | 2 ties | |
| Math | 6 | 3 | ||
| Knowledge | 6 | 5 | ||
| Web design | 4 | 1 | ||
| General | 8 | 12 | ||
| All shared boards | 36 | 28 | 2 ties |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index60
| Benchmark | GPT-4.1 Nano | Llama 4 Scout | Leader |
|---|---|---|---|
| Aider polyglot | 8.9% | Not measured | Only GPT-4.1 Nano measured |
| SWE-bench Verified | Not measured | 9.1% | Only Llama 4 Scout measured |
| AA Coding Index | 11.1 | 8.2 | GPT-4.1 Nano |
| LiveCodeBench (Vals AI) | 42.7% | 38.5% | GPT-4.1 Nano |
| LiveCodeBench (Artificial Analysis run) | 32.6% | 29.9% | GPT-4.1 Nano |
| SciCode | Not measured | 21.3% | Only Llama 4 Scout measured |
| Terminal-Bench Hard (Artificial Analysis run) | 3.8% | 1.5% | GPT-4.1 Nano |
| WeirdML | 19.0% | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: coding | 1306 | 1286 | GPT-4.1 Nano |
| LMArena Text: software and IT | 1305 | 1286 | GPT-4.1 Nano |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GPT-4.1 Nano | Llama 4 Scout | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.8 | 6.5 | GPT-4.1 Nano |
| Epoch Capabilities Index | 129.5 | 129.6 | Llama 4 Scout |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-4.1 Nano | Llama 4 Scout | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 0.5 | Only Llama 4 Scout measured |
| τ²-Bench Telecom | 17.3% | 15.5% | GPT-4.1 Nano |
| Berkeley Function Calling Leaderboard (BFCL) | 33.1% | 28.1% | GPT-4.1 Nano |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index36
| Benchmark | GPT-4.1 Nano | Llama 4 Scout | Leader |
|---|---|---|---|
| GPQA Diamond | 50.8% | 47.0% | GPT-4.1 Nano |
| MMMU Pro (Vals AI) | 55.0% | 58.8% | Llama 4 Scout |
| GPQA Diamond (Artificial Analysis run) | 51.2% | 58.7% | Llama 4 Scout |
| Humanity's Last Exam (Artificial Analysis run) | 3.8% | 3.8% | Tie |
| GPQA Diamond (Epoch AI run) | 48.9% | 51.8% | Llama 4 Scout |
| ARC-AGI | 0.0% | 0.5% | Llama 4 Scout |
| ARC-AGI-2 | 0.0% | 0.0% | Tie |
| Chess puzzles | 0.0% | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: hard prompts | 1286 | 1266 | GPT-4.1 Nano |
| LMArena Text: expert | 1271 | 1235 | GPT-4.1 Nano |
| LMArena Text: hard prompts in English | 1293 | 1293 | Llama 4 Scout |
| 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 | 33.3% | 36.9% | Llama 4 Scout |
| Neither model has a result on the other 9 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index63
| Benchmark | GPT-4.1 Nano | Llama 4 Scout | Leader |
|---|---|---|---|
| AIME | 26.5% | 19.0% | GPT-4.1 Nano |
| MATH 500 (Vals AI) | 80.2% | 79.2% | GPT-4.1 Nano |
| MGSM (Vals AI) | 69.3% | 88.0% | Llama 4 Scout |
| AA Math Index | 24.0 | 14.0 | GPT-4.1 Nano |
| AIME 2025 (Artificial Analysis run) | 24.0% | 14.0% | GPT-4.1 Nano |
| OTIS Mock AIME | 28.9% | 7.8% | GPT-4.1 Nano |
| MATH level 5 | 70.0% | 62.3% | GPT-4.1 Nano |
| LMArena Text: math | 1274 | 1287 | Llama 4 Scout |
| LMArena Text: mathematical work | 1274 | 1286 | Llama 4 Scout |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index65
| Benchmark | GPT-4.1 Nano | Llama 4 Scout | Leader |
|---|---|---|---|
| MMLU Pro | 63.5% | 69.6% | Llama 4 Scout |
| LegalBench (Vals AI) | 61.1% | 72.0% | Llama 4 Scout |
| CorpFin (Vals AI) | 42.1% | 46.8% | Llama 4 Scout |
| TaxEval (Vals AI) | 60.8% | 55.2% | GPT-4.1 Nano |
| MortgageTax (Vals AI) | 52.8% | 57.8% | Llama 4 Scout |
| MedQA (Vals AI) | 68.2% | 50.9% | GPT-4.1 Nano |
| MedCode (Vals AI) | Not measured | 23.3% | Only Llama 4 Scout measured |
| MMLU Pro (Artificial Analysis run) | 65.7% | 75.2% | Llama 4 Scout |
| SimpleQA Verified | 6.0% | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: science | 1297 | 1278 | GPT-4.1 Nano |
| LMArena Text: medicine and healthcare | 1276 | 1275 | GPT-4.1 Nano |
| LMArena Text: legal and government | 1300 | 1285 | GPT-4.1 Nano |
| LMArena Text: business and finance | 1267 | 1257 | GPT-4.1 Nano |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | GPT-4.1 Nano | Llama 4 Scout | Leader |
|---|---|---|---|
| Design Arena: websites | 973 | 750 | GPT-4.1 Nano |
| Design Arena: UI components | 913 | 765 | GPT-4.1 Nano |
| Design Arena: game dev | 970 | 772 | GPT-4.1 Nano |
| Design Arena: data viz | 891 | 896 | Llama 4 Scout |
| Design Arena: 3D | 924 | Not measured | Only GPT-4.1 Nano measured |
| Design Arena: all code categories | 962 | 789 | GPT-4.1 Nano |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index812
| Benchmark | GPT-4.1 Nano | 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 | 32.0% | 39.5% | Llama 4 Scout |
| Long context reasoning (AA-LCR) | 20.3% | 27.7% | Llama 4 Scout |
| DTBench | 52.5% | 57.9% | Llama 4 Scout |
| LMCA | 5.5% | 12.0% | Llama 4 Scout |
| Fiction.LiveBench | 25.0% | 36.0% | Llama 4 Scout |
| LMArena Text | 1285 | 1279 | GPT-4.1 Nano |
| LMArena Text: instruction following | 1266 | 1248 | GPT-4.1 Nano |
| LMArena Text: creative writing | 1260 | 1249 | GPT-4.1 Nano |
| LMArena Vision | 1063 | 1118 | Llama 4 Scout |
| LMArena Text: English | 1303 | 1304 | Llama 4 Scout |
| LMArena Text: non-English | 1260 | 1252 | GPT-4.1 Nano |
| LMArena Text: Chinese | 1270 | 1255 | GPT-4.1 Nano |
| LMArena Text: German | 1288 | 1273 | GPT-4.1 Nano |
| LMArena Text: Japanese | 1198 | 1206 | Llama 4 Scout |
| 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 | 1261 | 1263 | Llama 4 Scout |
| LMArena Text: Polish | Not measured | 1265 | Only Llama 4 Scout measured |
| LMArena Text: multi-turn | 1277 | 1280 | Llama 4 Scout |
| LMArena Text: longer queries | 1283 | 1265 | GPT-4.1 Nano |
| LMArena Text: writing and language | 1261 | 1269 | Llama 4 Scout |
| LMArena Text: entertainment and media | 1243 | 1242 | GPT-4.1 Nano |
| LMArena Vision: English | 1101 | 1143 | Llama 4 Scout |
| 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 18 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4.1 Nano | 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
GPT-4.1 Nano costs less
Repository review
50K input + 3K output tokens
GPT-4.1 Nano costs less
Agent loop
200K input + 10K output tokens
GPT-4.1 Nano 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 | OpenAI GPT-4.1 Nano | Meta Llama 4 Scout |
|---|---|---|
| Maker | OpenAI | Meta Llama |
| Weights | No public weights listed | Open weights |
| Released | April 14, 2025 | April 5, 2025 |
| Context window | 1.05M | 1.31M |
| Input $/Mtok | $0.10 | $0.175 |
| Output $/Mtok | $0.40 | $0.50 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | $1.276 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-4.1 Nano better than Llama 4 Scout for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4.1 Nano for output tokens 1.3x cheaper ($0.40 vs $0.50 per million) and work measured by Epoch Capabilities Index or MMMU Pro (Vals AI), where it leads.
Which is cheaper, GPT-4.1 Nano or Llama 4 Scout?
GPT-4.1 Nano costs $0.10 per million input tokens and $0.40 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, GPT-4.1 Nano or Llama 4 Scout?
Llama 4 Scout, with 1.31M tokens against 1.05M.
How many benchmarks is the GPT-4.1 Nano vs Llama 4 Scout comparison based on?
66 benchmarks have a result for both models as of October 22, 2026. GPT-4.1 Nano's coding index rests on 0 independent benchmarks and Llama 4 Scout's on 0. Where only one model has a result, the page says so instead of naming a winner.