GPT-5.5 vs Llama 4 Scout
GPT-5.5 leads Llama 4 Scout on the Artificial Analysis Intelligence Index, 38.4 to 6.5, and Llama 4 Scout costs 60.0x less per output token ($0.50 vs $30.00 per million). Updated October 22, 2026; 70 benchmarks have a result for both.
GPT-5.5 or Llama 4 Scout: which to pick
Only GPT-5.5 has a coding score, 71.3 on the Vibe Coding Index; Llama 4 Scout has none, so the two cannot be ranked on coding.
Pick Llama 4 Scout for output tokens 60.0x cheaper ($0.50 vs $30.00 per million) and a lower cost per solved task ($1.276 against $1.65).
- Shared results
- 70
- GPT-5.5 only
- 64
- Llama 4 Scout only
- 12
- 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
Only GPT-5.5 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.5
GPT-5.5 leads, 38.4 against 6.5.
Same board, both measured
Agentic work
AA Agentic Index
GPT-5.5
GPT-5.5 leads, 36.4 against 0.5.
Same board, both measured
Reasoning
GPQA Diamond
GPT-5.5
GPT-5.5 leads, 93.2% against 47.0%.
Same board, both measured
Math
AIME
No call
Only Llama 4 Scout has a AIME result, 19.0%.
Directional only
Knowledge
MMLU Pro
GPT-5.5
GPT-5.5 leads, 88.1% against 69.6%.
Same board, both measured
Web design
Design Arena: websites
GPT-5.5
GPT-5.5 leads, 1256 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
Llama 4 Scout
GPT-5.5 $0.020, Llama 4 Scout $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Llama 4 Scout
GPT-5.5 $1.65, Llama 4 Scout $1.276.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5.5 71.3 from 5 benchmarks, Llama 4 Scout not benchmarked.
- Widest category
- Web design, 59.0 points apart on average over 5 shared benchmarks.
- Widest single gap
- ARC-AGI: GPT-5.5 95.0% against Llama 4 Scout 0.5%, 94.5 points apart on a 0 to 100 scale.
- Output price
- GPT-5.5 $30.00 against Llama 4 Scout $0.50 per million output tokens, 60.0x apart.
- Context window
- GPT-5.5 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-5.5 | Bars | Llama 4 Scout | Ties |
|---|---|---|---|---|
| Coding | 6 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 2 | 0 | ||
| Reasoning | 13 | 0 | ||
| Math | 3 | 0 | ||
| Knowledge | 10 | 0 | ||
| Web design | 5 | 0 | ||
| General | 29 | 0 | ||
| All shared boards | 70 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index60
| Benchmark | GPT-5.5 | Llama 4 Scout | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 9.1% | Only Llama 4 Scout measured |
| LiveBench coding | 85.1% | Not measured | Only GPT-5.5 measured |
| LMArena WebDev | 1513 | Not measured | Only GPT-5.5 measured |
| AA Coding Index | 74.9 | 8.2 | GPT-5.5 |
| LiveCodeBench (Vals AI) | 85.3% | 38.5% | GPT-5.5 |
| SWE-bench Verified (Vals AI) | 82.6% | Not measured | Only GPT-5.5 measured |
| Vibe Code Bench (Vals AI) | 69.8% | Not measured | Only GPT-5.5 measured |
| Code migration (Vals AI) | 45.2% | Not measured | Only GPT-5.5 measured |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only GPT-5.5 measured |
| Terminal-Bench 2.1 (Vals AI) | 76.4% | Not measured | Only GPT-5.5 measured |
| SkillsBench (Vals AI) | 62.5% | Not measured | Only GPT-5.5 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 29.9% | Only Llama 4 Scout measured |
| SciCode | 56.1% | 21.3% | GPT-5.5 |
| Terminal-Bench Hard (Artificial Analysis run) | 60.6% | 1.5% | GPT-5.5 |
| Terminal-Bench | 84.7% | Not measured | Only GPT-5.5 measured |
| FrontierCode | 43.0% | Not measured | Only GPT-5.5 measured |
| GSO-Bench | 40.2% | Not measured | Only GPT-5.5 measured |
| WeirdML | 84.9% | Not measured | Only GPT-5.5 measured |
| MirrorCode | 10.0% | Not measured | Only GPT-5.5 measured |
| ALE-Bench | 1943.0 | Not measured | Only GPT-5.5 measured |
| LMArena Text: coding | 1494 | 1286 | GPT-5.5 |
| LMArena Text: software and IT | 1493 | 1286 | GPT-5.5 |
| LMArena WebDev: React | 1506 | Not measured | Only GPT-5.5 measured |
| LMArena WebDev: HTML | 1543 | Not measured | Only GPT-5.5 measured |
| LMArena WebDev: from an image | 1532 | Not measured | Only GPT-5.5 measured |
| Neither model has a result on the other 14 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.5 | Llama 4 Scout | Leader |
|---|---|---|---|
| AA Intelligence Index | 38.4 | 6.5 | GPT-5.5 |
| Epoch Capabilities Index | 159.7 | 129.6 | GPT-5.5 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.5 | Llama 4 Scout | Leader |
|---|---|---|---|
| AA Agentic Index | 36.4 | 0.5 | GPT-5.5 |
| Design Arena: agents building web apps | 1117 | Not measured | Only GPT-5.5 measured |
| Design Arena: agents building full-stack apps | 1089 | Not measured | Only GPT-5.5 measured |
| Design Arena: agents building mobile apps | 1142 | Not measured | Only GPT-5.5 measured |
| Design Arena: agents building Android apps | 1164 | Not measured | Only GPT-5.5 measured |
| Design Arena: agents building Godot games | 1173 | Not measured | Only GPT-5.5 measured |
| Design Arena: agents building games | 1179 | Not measured | Only GPT-5.5 measured |
| Design Arena: HTML slides | 1017 | Not measured | Only GPT-5.5 measured |
| Design Arena: PowerPoint slides | 1084 | Not measured | Only GPT-5.5 measured |
| LiveBench research | 61.6% | Not measured | Only GPT-5.5 measured |
| LiveBench browser | 48.0% | Not measured | Only GPT-5.5 measured |
| LiveBench long agentic | 44.6% | Not measured | Only GPT-5.5 measured |
| SREBench (Vals AI) | 3.8% | Not measured | Only GPT-5.5 measured |
| Finance Agent (Vals AI) | 51.8% | Not measured | Only GPT-5.5 measured |
| Tax Agent Bench (Vals AI) | 21.8% | Not measured | Only GPT-5.5 measured |
| Legal Research Bench (Vals AI) | 40.4% | Not measured | Only GPT-5.5 measured |
| Excel Modeling Benchmark (Vals AI) | 64.5% | Not measured | Only GPT-5.5 measured |
| Time Horizon Index (Vals AI) | 8.3% | Not measured | Only GPT-5.5 measured |
| τ²-Bench Telecom | 93.9% | 15.5% | GPT-5.5 |
| APEX-Agents | 55.1% | Not measured | Only GPT-5.5 measured |
| Remote Labor Index | 6.3% | Not measured | Only GPT-5.5 measured |
| OSWorld 2.0 | 13.0% | Not measured | Only GPT-5.5 measured |
| DeepResearch Bench | 54.0% | Not measured | Only GPT-5.5 measured |
| PostTrainBench | 27.2% | Not measured | Only GPT-5.5 measured |
| Vending-Bench 2 | $7,524 | Not measured | Only GPT-5.5 measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 28.1% | Only Llama 4 Scout measured |
| Neither model has a result on the other 15 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index130
| Benchmark | GPT-5.5 | Llama 4 Scout | Leader |
|---|---|---|---|
| GPQA Diamond | 93.2% | 47.0% | GPT-5.5 |
| LiveBench reasoning | 89.7% | Not measured | Only GPT-5.5 measured |
| MMMU Pro (Vals AI) | 88.3% | 58.8% | GPT-5.5 |
| GPQA Diamond (Artificial Analysis run) | 93.5% | 58.7% | GPT-5.5 |
| Humanity's Last Exam (Artificial Analysis run) | 45.8% | 3.8% | GPT-5.5 |
| GPQA Diamond (Epoch AI run) | 90.7% | 51.8% | GPT-5.5 |
| ARC-AGI | 95.0% | 0.5% | GPT-5.5 |
| ARC-AGI-2 | 85.0% | 0.0% | GPT-5.5 |
| SimpleBench | 69.0% | Not measured | Only GPT-5.5 measured |
| Chess puzzles | 63.0% | Not measured | Only GPT-5.5 measured |
| Mystery game puzzles | 56.0% | Not measured | Only GPT-5.5 measured |
| EBR-bench | 34.3% | Not measured | Only GPT-5.5 measured |
| LMArena Text: hard prompts | 1489 | 1266 | GPT-5.5 |
| LMArena Text: expert | 1507 | 1235 | GPT-5.5 |
| LMArena Text: hard prompts in English | 1483 | 1293 | GPT-5.5 |
| LMArena Vision: diagrams | 1325 | 1125 | GPT-5.5 |
| LMArena Vision: homework | 1332 | 1148 | GPT-5.5 |
| Kagi LLM Benchmark | 88.8% | 36.9% | GPT-5.5 |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GPT-5.5 | 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 | 84.4% | 7.8% | GPT-5.5 |
| FrontierMath, tiers 1 to 3 | 85.3% | Not measured | Only GPT-5.5 measured |
| FrontierMath, tier 4 | 72.5% | Not measured | Only GPT-5.5 measured |
| ProofBench | 50.0% | Not measured | Only GPT-5.5 measured |
| MATH level 5 | Not measured | 62.3% | Only Llama 4 Scout measured |
| LMArena Text: math | 1487 | 1287 | GPT-5.5 |
| LMArena Text: mathematical work | 1496 | 1286 | GPT-5.5 |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index100
| Benchmark | GPT-5.5 | Llama 4 Scout | Leader |
|---|---|---|---|
| MMLU Pro | 88.1% | 69.6% | GPT-5.5 |
| LegalBench (Vals AI) | 86.5% | 72.0% | GPT-5.5 |
| CaseLaw (Vals AI) | 66.2% | Not measured | Only GPT-5.5 measured |
| CorpFin (Vals AI) | 68.4% | 46.8% | GPT-5.5 |
| TaxEval (Vals AI) | 75.0% | 55.2% | GPT-5.5 |
| MortgageTax (Vals AI) | 68.8% | 57.8% | GPT-5.5 |
| MedQA (Vals AI) | Not measured | 50.9% | Only Llama 4 Scout measured |
| MedCode (Vals AI) | 49.1% | 23.3% | GPT-5.5 |
| MMLU Pro (Artificial Analysis run) | Not measured | 75.2% | Only Llama 4 Scout measured |
| SimpleQA Verified | 63.0% | Not measured | Only GPT-5.5 measured |
| LMArena Text: science | 1485 | 1278 | GPT-5.5 |
| LMArena Text: medicine and healthcare | 1465 | 1275 | GPT-5.5 |
| LMArena Text: legal and government | 1491 | 1285 | GPT-5.5 |
| LMArena Text: business and finance | 1478 | 1257 | GPT-5.5 |
| FACTS Benchmark Suite | 66.7% | Not measured | Only GPT-5.5 measured |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GPT-5.5 | Llama 4 Scout | Leader |
|---|---|---|---|
| Design Arena: websites | 1256 | 750 | GPT-5.5 |
| Design Arena: UI components | 1257 | 765 | GPT-5.5 |
| Design Arena: game dev | 1293 | 772 | GPT-5.5 |
| Design Arena: data viz | 1264 | 896 | GPT-5.5 |
| Design Arena: 3D | 1197 | Not measured | Only GPT-5.5 measured |
| Design Arena: all code categories | 1260 | 789 | GPT-5.5 |
| Design Arena: SVG | 1234 | Not measured | Only GPT-5.5 measured |
| Design Arena: ASCII art | 1255 | Not measured | Only GPT-5.5 measured |
| LiveBench design | 70.3% | Not measured | Only GPT-5.5 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index290
| Benchmark | GPT-5.5 | Llama 4 Scout | Leader |
|---|---|---|---|
| LiveBench data analysis | 81.6% | Not measured | Only GPT-5.5 measured |
| Public Benefits Bench (Vals AI) | 60.9% | Not measured | Only GPT-5.5 measured |
| SAGE (Vals AI) | 51.5% | 34.8% | GPT-5.5 |
| MedScribe (Vals AI) | 86.9% | 50.6% | GPT-5.5 |
| Vals Multimodal Index | 68.1% | Not measured | Only GPT-5.5 measured |
| IFBench | 75.9% | 39.5% | GPT-5.5 |
| Long context reasoning (AA-LCR) | 84.3% | 27.7% | GPT-5.5 |
| DTBench | 96.0% | 57.9% | GPT-5.5 |
| LMCA | 54.3% | 12.0% | GPT-5.5 |
| Fiction.LiveBench | Not measured | 36.0% | Only Llama 4 Scout measured |
| Surface Evolver Bench | 88.1% | Not measured | Only GPT-5.5 measured |
| Furniture assembly | 44.2% | Not measured | Only GPT-5.5 measured |
| ExploitBench | 47.4% | Not measured | Only GPT-5.5 measured |
| CL-bench Life | 22.2% | Not measured | Only GPT-5.5 measured |
| LMArena Text | 1471 | 1279 | GPT-5.5 |
| LMArena Text: instruction following | 1478 | 1248 | GPT-5.5 |
| LMArena Text: creative writing | 1455 | 1249 | GPT-5.5 |
| LMArena Vision | 1297 | 1118 | GPT-5.5 |
| LMArena Text: English | 1470 | 1304 | GPT-5.5 |
| LMArena Text: non-English | 1467 | 1252 | GPT-5.5 |
| LMArena Text: Chinese | 1534 | 1255 | GPT-5.5 |
| LMArena Text: German | 1480 | 1273 | GPT-5.5 |
| LMArena Text: Japanese | 1499 | 1206 | GPT-5.5 |
| LMArena Text: Korean | 1460 | 1207 | GPT-5.5 |
| LMArena Text: French | 1485 | 1283 | GPT-5.5 |
| LMArena Text: Spanish | 1468 | 1278 | GPT-5.5 |
| LMArena Text: Russian | 1473 | 1263 | GPT-5.5 |
| LMArena Text: Polish | 1465 | 1265 | GPT-5.5 |
| LMArena Text: multi-turn | 1476 | 1280 | GPT-5.5 |
| LMArena Text: longer queries | 1484 | 1265 | GPT-5.5 |
| LMArena Text: writing and language | 1466 | 1269 | GPT-5.5 |
| LMArena Text: entertainment and media | 1449 | 1242 | GPT-5.5 |
| LMArena Vision: English | 1292 | 1143 | GPT-5.5 |
| LMArena Vision: Chinese | 1360 | 1106 | GPT-5.5 |
| LMArena Vision: reading text | 1306 | 1117 | GPT-5.5 |
| LMArena Vision: humor | 1293 | 1102 | GPT-5.5 |
| LMArena Vision: recognizing things | 1231 | Not measured | Only GPT-5.5 measured |
| LMArena Vision: creative writing | 1301 | 1126 | GPT-5.5 |
| LMArena Document | 1486 | Not measured | Only GPT-5.5 measured |
| Neither model has a result on the other 7 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.5 | 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 | OpenAI GPT-5.5 | Meta Llama 4 Scout |
|---|---|---|
| Maker | OpenAI | Meta Llama |
| Weights | No public weights listed | Open weights |
| Released | April 24, 2026 | April 5, 2025 |
| Context window | 1.05M | 1.31M |
| Input $/Mtok | $5.00 | $0.175 |
| Output $/Mtok | $30.00 | $0.50 |
| Coding index | 71.3 | Not benchmarked |
| Independent measurements behind it | 5 | 0 |
| Cost per solved task | $1.65 | $1.276 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.5 better than Llama 4 Scout for coding?
Only GPT-5.5 has a coding score, 71.3 on the Vibe Coding Index; Llama 4 Scout has none, so the two cannot be ranked on coding. Pick Llama 4 Scout for output tokens 60.0x cheaper ($0.50 vs $30.00 per million) and a lower cost per solved task ($1.276 against $1.65).
Which is cheaper, GPT-5.5 or Llama 4 Scout?
GPT-5.5 costs $5.00 per million input tokens and $30.00 per million output tokens; Llama 4 Scout costs $0.175 and $0.50. Per solved SWE-bench Verified task, GPT-5.5 costs $1.65 and Llama 4 Scout $1.276.
Which has the bigger context window, GPT-5.5 or Llama 4 Scout?
Llama 4 Scout, with 1.31M tokens against 1.05M.
How many benchmarks is the GPT-5.5 vs Llama 4 Scout comparison based on?
70 benchmarks have a result for both models as of October 22, 2026. GPT-5.5's coding index rests on 5 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.