GPT-5.2 Pro vs Llama 4 Scout
Llama 4 Scout costs 336.0x less per output token than GPT-5.2 Pro ($0.50 vs $168.00 per million). Updated October 21, 2026; 3 benchmarks have a result for both.
GPT-5.2 Pro 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
- 3
- GPT-5.2 Pro only
- 4
- Llama 4 Scout only
- 79
- Like-for-like categories
- 2 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
No call
Only Llama 4 Scout has a AA Intelligence Index result, 6.5.
Directional only
Agentic work
AA Agentic Index
No call
Only Llama 4 Scout has a AA Agentic Index result, 0.5.
Directional only
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
No call
Only Llama 4 Scout has a Design Arena: websites result, 750.
Directional only
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
Llama 4 Scout
GPT-5.2 Pro $0.105, 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-5.2 Pro not benchmarked, Llama 4 Scout not benchmarked.
- Widest category
- Reasoning, 72.1 points apart on average over 2 shared benchmarks.
- Widest single gap
- ARC-AGI: GPT-5.2 Pro 90.5% against Llama 4 Scout 0.5%, 90.0 points apart on a 0 to 100 scale.
- Output price
- GPT-5.2 Pro $168.00 against Llama 4 Scout $0.50 per million output tokens, 336.0x apart.
- Context window
- GPT-5.2 Pro 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-5.2 Pro | Bars | Llama 4 Scout | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| All shared boards | 3 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index8 boards, none shared
| Benchmark | GPT-5.2 Pro | Llama 4 Scout | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 9.1% | Only Llama 4 Scout measured |
| AA Coding Index | Not measured | 8.2 | Only Llama 4 Scout measured |
| 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 | Not measured | 21.3% | Only Llama 4 Scout measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 1.5% | Only Llama 4 Scout measured |
| LMArena Text: coding | Not measured | 1286 | Only Llama 4 Scout measured |
| LMArena Text: software and IT | Not measured | 1286 | Only Llama 4 Scout measured |
| Neither model has a result on the other 31 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.2 Pro | Llama 4 Scout | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 6.5 | Only Llama 4 Scout measured |
| Epoch Capabilities Index | 156.3 | 129.6 | GPT-5.2 Pro |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-5.2 Pro | Llama 4 Scout | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 0.5 | Only Llama 4 Scout measured |
| τ²-Bench Telecom | Not measured | 15.5% | Only Llama 4 Scout measured |
| 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 index20
| Benchmark | GPT-5.2 Pro | 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) | Not measured | 58.7% | Only Llama 4 Scout measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 3.8% | Only Llama 4 Scout measured |
| GPQA Diamond (Epoch AI run) | Not measured | 51.8% | Only Llama 4 Scout measured |
| ARC-AGI | 90.5% | 0.5% | GPT-5.2 Pro |
| ARC-AGI-2 | 54.2% | 0.0% | GPT-5.2 Pro |
| SimpleBench | 57.4% | Not measured | Only GPT-5.2 Pro measured |
| Chess puzzles | 60.0% | Not measured | Only GPT-5.2 Pro measured |
| LMArena Text: hard prompts | Not measured | 1266 | Only Llama 4 Scout measured |
| LMArena Text: expert | Not measured | 1235 | Only Llama 4 Scout measured |
| LMArena Text: hard prompts in English | Not measured | 1293 | Only Llama 4 Scout measured |
| 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 7 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | GPT-5.2 Pro | 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 |
| FrontierMath, tiers 1 to 3 | 74.0% | Not measured | Only GPT-5.2 Pro measured |
| FrontierMath, tier 4 | 46.0% | Not measured | Only GPT-5.2 Pro 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 2 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | GPT-5.2 Pro | 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 | Not measured | 1278 | Only Llama 4 Scout measured |
| LMArena Text: medicine and healthcare | Not measured | 1275 | Only Llama 4 Scout measured |
| LMArena Text: legal and government | Not measured | 1285 | Only Llama 4 Scout measured |
| LMArena Text: business and finance | Not measured | 1257 | Only Llama 4 Scout measured |
| Neither model has a result on the other 6 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-5.2 Pro | Llama 4 Scout | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 750 | Only Llama 4 Scout measured |
| Design Arena: UI components | Not measured | 768 | Only Llama 4 Scout measured |
| Design Arena: game dev | Not measured | 773 | Only Llama 4 Scout measured |
| Design Arena: data viz | Not measured | 896 | Only Llama 4 Scout measured |
| Design Arena: all code categories | Not measured | 790 | Only Llama 4 Scout measured |
| Neither model has a result on the other 4 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index30 boards, none shared
| Benchmark | GPT-5.2 Pro | 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 | Not measured | 39.5% | Only Llama 4 Scout measured |
| Long context reasoning (AA-LCR) | Not measured | 27.7% | Only Llama 4 Scout measured |
| 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 | Not measured | 1279 | Only Llama 4 Scout measured |
| LMArena Text: instruction following | Not measured | 1248 | Only Llama 4 Scout measured |
| LMArena Text: creative writing | Not measured | 1249 | Only Llama 4 Scout measured |
| LMArena Vision | Not measured | 1118 | Only Llama 4 Scout measured |
| LMArena Text: English | Not measured | 1304 | Only Llama 4 Scout measured |
| LMArena Text: non-English | Not measured | 1252 | Only Llama 4 Scout measured |
| LMArena Text: Chinese | Not measured | 1255 | Only Llama 4 Scout measured |
| 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 | Not measured | 1263 | Only Llama 4 Scout measured |
| LMArena Text: Polish | Not measured | 1265 | Only Llama 4 Scout measured |
| LMArena Text: multi-turn | Not measured | 1280 | Only Llama 4 Scout measured |
| LMArena Text: longer queries | Not measured | 1265 | Only Llama 4 Scout measured |
| LMArena Text: writing and language | Not measured | 1269 | Only Llama 4 Scout measured |
| LMArena Text: entertainment and media | Not measured | 1242 | Only Llama 4 Scout measured |
| 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 | GPT-5.2 Pro | 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-5.2 Pro | Meta Llama 4 Scout |
|---|---|---|
| Maker | OpenAI | Meta Llama |
| Weights | No public weights listed | Open weights |
| Released | December 10, 2025 | April 5, 2025 |
| Context window | 400K | 1.31M |
| Input $/Mtok | $21.00 | $0.175 |
| Output $/Mtok | $168.00 | $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-5.2 Pro 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, GPT-5.2 Pro or Llama 4 Scout?
GPT-5.2 Pro costs $21.00 per million input tokens and $168.00 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-5.2 Pro or Llama 4 Scout?
Llama 4 Scout, with 1.31M tokens against 400K.
How many benchmarks is the GPT-5.2 Pro vs Llama 4 Scout comparison based on?
3 benchmarks have a result for both models as of October 21, 2026. GPT-5.2 Pro'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.