GPT-5 Nano vs Llama 4 Scout
GPT-5 Nano leads Llama 4 Scout on the Artificial Analysis Intelligence Index, 13.0 to 6.5, and GPT-5 Nano costs 1.3x less per output token ($0.40 vs $0.50 per million). Updated October 22, 2026; 76 benchmarks have a result for both.
GPT-5 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-5 Nano for output tokens 1.3x cheaper ($0.40 vs $0.50 per million) and a lower cost per solved task ($0.109 against $1.276).
- Shared results
- 76
- GPT-5 Nano only
- 12
- Llama 4 Scout only
- 6
- 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-5 Nano
GPT-5 Nano leads, 13.0 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-5 Nano
GPT-5 Nano leads, 63.4% against 47.0%.
Same board, both measured
Math
AIME
GPT-5 Nano
GPT-5 Nano leads, 81.2% against 19.0%.
Same board, both measured
Knowledge
MMLU Pro
GPT-5 Nano
GPT-5 Nano leads, 76.1% against 69.6%.
Same board, both measured
Web design
Design Arena: websites
GPT-5 Nano
GPT-5 Nano leads, 1101 against 750.
Same board, both measured
Long documents
Listed context window
Llama 4 Scout
Llama 4 Scout takes 1.31M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5 Nano
GPT-5 Nano $0.0003, Llama 4 Scout $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5 Nano
GPT-5 Nano $0.109, Llama 4 Scout $1.276.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5 Nano not benchmarked, Llama 4 Scout not benchmarked.
- Widest category
- Math, 37.3 points apart on average over 9 shared benchmarks.
- Widest single gap
- OTIS Mock AIME: GPT-5 Nano 81.1% against Llama 4 Scout 7.8%, 73.3 points apart on a 0 to 100 scale.
- Output price
- GPT-5 Nano $0.40 against Llama 4 Scout $0.50 per million output tokens, 1.3x apart.
- Context window
- GPT-5 Nano takes 400K 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-5 Nano | Bars | Llama 4 Scout | Ties |
|---|---|---|---|---|
| Coding | 6 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 2 | 0 | ||
| Reasoning | 13 | 0 | ||
| Math | 9 | 0 | ||
| Knowledge | 9 | 2 | ||
| Web design | 5 | 0 | ||
| General | 26 | 2 | ||
| All shared boards | 72 | 4 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index60
| Benchmark | GPT-5 Nano | Llama 4 Scout | Leader |
|---|---|---|---|
| SWE-bench Verified | 34.8% | 9.1% | GPT-5 Nano |
| AA Coding Index | Not measured | 8.2 | Only Llama 4 Scout measured |
| LiveCodeBench (Vals AI) | 70.2% | 38.5% | GPT-5 Nano |
| LiveCodeBench (Artificial Analysis run) | 78.9% | 29.9% | GPT-5 Nano |
| SciCode | Not measured | 21.3% | Only Llama 4 Scout measured |
| Terminal-Bench Hard (Artificial Analysis run) | 17.4% | 1.5% | GPT-5 Nano |
| Terminal-Bench | 21.8% | Not measured | Only GPT-5 Nano measured |
| WeirdML | 38.1% | Not measured | Only GPT-5 Nano measured |
| LMArena Text: coding | 1351 | 1286 | GPT-5 Nano |
| LMArena Text: software and IT | 1345 | 1286 | GPT-5 Nano |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5 Nano | Llama 4 Scout | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.0 | 6.5 | GPT-5 Nano |
| Epoch Capabilities Index | 139.2 | 129.6 | GPT-5 Nano |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5 Nano | Llama 4 Scout | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 0.5 | Only Llama 4 Scout measured |
| τ²-Bench Telecom | 36.5% | 15.5% | GPT-5 Nano |
| Berkeley Function Calling Leaderboard (BFCL) | 51.4% | 28.1% | GPT-5 Nano |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index130
| Benchmark | GPT-5 Nano | Llama 4 Scout | Leader |
|---|---|---|---|
| GPQA Diamond | 63.4% | 47.0% | GPT-5 Nano |
| MMMU Pro (Vals AI) | 70.9% | 58.8% | GPT-5 Nano |
| GPQA Diamond (Artificial Analysis run) | 67.6% | 58.7% | GPT-5 Nano |
| Humanity's Last Exam (Artificial Analysis run) | 9.5% | 3.8% | GPT-5 Nano |
| GPQA Diamond (Epoch AI run) | 69.4% | 51.8% | GPT-5 Nano |
| ARC-AGI | 20.7% | 0.5% | GPT-5 Nano |
| ARC-AGI-2 | 2.6% | 0.0% | GPT-5 Nano |
| Chess puzzles | 22.0% | Not measured | Only GPT-5 Nano measured |
| Mystery game puzzles | 9.0% | Not measured | Only GPT-5 Nano measured |
| VPCT | 37.2% | Not measured | Only GPT-5 Nano measured |
| LMArena Text: hard prompts | 1328 | 1266 | GPT-5 Nano |
| LMArena Text: expert | 1321 | 1235 | GPT-5 Nano |
| LMArena Text: hard prompts in English | 1338 | 1293 | GPT-5 Nano |
| LMArena Vision: diagrams | 1141 | 1125 | GPT-5 Nano |
| LMArena Vision: homework | 1157 | 1148 | GPT-5 Nano |
| Kagi LLM Benchmark | 62.2% | 36.9% | GPT-5 Nano |
| Neither model has a result on the other 7 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index90
| Benchmark | GPT-5 Nano | Llama 4 Scout | Leader |
|---|---|---|---|
| AIME | 81.2% | 19.0% | GPT-5 Nano |
| MATH 500 (Vals AI) | 93.8% | 79.2% | GPT-5 Nano |
| MGSM (Vals AI) | 89.3% | 88.0% | GPT-5 Nano |
| AA Math Index | 83.7 | 14.0 | GPT-5 Nano |
| AIME 2025 (Artificial Analysis run) | 83.7% | 14.0% | GPT-5 Nano |
| OTIS Mock AIME | 81.1% | 7.8% | GPT-5 Nano |
| FrontierMath, tiers 1 to 3 | 20.0% | Not measured | Only GPT-5 Nano measured |
| FrontierMath, tier 4 | 2.4% | Not measured | Only GPT-5 Nano measured |
| ProofBench | 12.0% | Not measured | Only GPT-5 Nano measured |
| MATH level 5 | 95.2% | 62.3% | GPT-5 Nano |
| LMArena Text: math | 1317 | 1287 | GPT-5 Nano |
| LMArena Text: mathematical work | 1326 | 1286 | GPT-5 Nano |
| MathArena | 12.8% | Not measured | Only GPT-5 Nano measured |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index92
| Benchmark | GPT-5 Nano | Llama 4 Scout | Leader |
|---|---|---|---|
| MMLU Pro | 76.1% | 69.6% | GPT-5 Nano |
| LegalBench (Vals AI) | 50.1% | 72.0% | Llama 4 Scout |
| CaseLaw (Vals AI) | 52.6% | Not measured | Only GPT-5 Nano measured |
| CorpFin (Vals AI) | Not measured | 46.8% | Only Llama 4 Scout measured |
| TaxEval (Vals AI) | 67.4% | 55.2% | GPT-5 Nano |
| MortgageTax (Vals AI) | 53.6% | 57.8% | Llama 4 Scout |
| MedQA (Vals AI) | 93.3% | 50.9% | GPT-5 Nano |
| MedCode (Vals AI) | 30.4% | 23.3% | GPT-5 Nano |
| MMLU Pro (Artificial Analysis run) | 78.0% | 75.2% | GPT-5 Nano |
| SimpleQA Verified | 11.7% | Not measured | Only GPT-5 Nano measured |
| LMArena Text: science | 1318 | 1278 | GPT-5 Nano |
| LMArena Text: medicine and healthcare | 1335 | 1275 | GPT-5 Nano |
| LMArena Text: legal and government | 1298 | 1285 | GPT-5 Nano |
| LMArena Text: business and finance | 1311 | 1257 | GPT-5 Nano |
| 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 | GPT-5 Nano | Llama 4 Scout | Leader |
|---|---|---|---|
| Design Arena: websites | 1101 | 750 | GPT-5 Nano |
| Design Arena: UI components | 1064 | 765 | GPT-5 Nano |
| Design Arena: game dev | 1044 | 772 | GPT-5 Nano |
| Design Arena: data viz | 1056 | 896 | GPT-5 Nano |
| Design Arena: 3D | 965 | Not measured | Only GPT-5 Nano measured |
| Design Arena: all code categories | 1084 | 789 | GPT-5 Nano |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index262
| Benchmark | GPT-5 Nano | Llama 4 Scout | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 30.4% | 34.8% | Llama 4 Scout |
| MedScribe (Vals AI) | 72.9% | 50.6% | GPT-5 Nano |
| IFBench | 67.6% | 39.5% | GPT-5 Nano |
| Long context reasoning (AA-LCR) | 45.0% | 27.7% | GPT-5 Nano |
| DTBench | 62.7% | 57.9% | GPT-5 Nano |
| LMCA | 7.9% | 12.0% | Llama 4 Scout |
| Fiction.LiveBench | 44.4% | 36.0% | GPT-5 Nano |
| LMArena Text | 1320 | 1279 | GPT-5 Nano |
| LMArena Text: instruction following | 1306 | 1248 | GPT-5 Nano |
| LMArena Text: creative writing | 1249 | 1249 | GPT-5 Nano |
| LMArena Vision | 1159 | 1118 | GPT-5 Nano |
| LMArena Text: English | 1321 | 1304 | GPT-5 Nano |
| LMArena Text: non-English | 1313 | 1252 | GPT-5 Nano |
| LMArena Text: Chinese | 1356 | 1255 | GPT-5 Nano |
| LMArena Text: German | 1327 | 1273 | GPT-5 Nano |
| LMArena Text: Japanese | 1227 | 1206 | GPT-5 Nano |
| LMArena Text: Korean | 1269 | 1207 | GPT-5 Nano |
| LMArena Text: French | Not measured | 1283 | Only Llama 4 Scout measured |
| LMArena Text: Spanish | 1360 | 1278 | GPT-5 Nano |
| LMArena Text: Russian | 1296 | 1263 | GPT-5 Nano |
| LMArena Text: Polish | 1314 | 1265 | GPT-5 Nano |
| LMArena Text: multi-turn | 1311 | 1280 | GPT-5 Nano |
| LMArena Text: longer queries | 1312 | 1265 | GPT-5 Nano |
| LMArena Text: writing and language | 1283 | 1269 | GPT-5 Nano |
| LMArena Text: entertainment and media | 1268 | 1242 | GPT-5 Nano |
| LMArena Vision: English | 1170 | 1143 | GPT-5 Nano |
| LMArena Vision: Chinese | 1176 | 1106 | GPT-5 Nano |
| LMArena Vision: reading text | 1152 | 1117 | GPT-5 Nano |
| LMArena Vision: humor | 1110 | 1102 | GPT-5 Nano |
| 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-5 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-5 Nano costs less
Repository review
50K input + 3K output tokens
GPT-5 Nano costs less
Agent loop
200K input + 10K output tokens
GPT-5 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-5 Nano | Meta Llama 4 Scout |
|---|---|---|
| Maker | OpenAI | Meta Llama |
| Weights | No public weights listed | Open weights |
| Released | August 7, 2025 | April 5, 2025 |
| Context window | 400K | 1.31M |
| Input $/Mtok | $0.05 | $0.175 |
| Output $/Mtok | $0.40 | $0.50 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | $0.109 | $1.276 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5 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-5 Nano for output tokens 1.3x cheaper ($0.40 vs $0.50 per million) and a lower cost per solved task ($0.109 against $1.276).
Which is cheaper, GPT-5 Nano or Llama 4 Scout?
GPT-5 Nano costs $0.05 per million input tokens and $0.40 per million output tokens; Llama 4 Scout costs $0.175 and $0.50. Per solved SWE-bench Verified task, GPT-5 Nano costs $0.109 and Llama 4 Scout $1.276.
Which has the bigger context window, GPT-5 Nano or Llama 4 Scout?
Llama 4 Scout, with 1.31M tokens against 400K.
How many benchmarks is the GPT-5 Nano vs Llama 4 Scout comparison based on?
76 benchmarks have a result for both models as of October 22, 2026. GPT-5 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.