GPT-5 vs Llama 4 Scout
GPT-5 leads Llama 4 Scout on the Artificial Analysis Intelligence Index, 23.0 to 6.5, and Llama 4 Scout costs 20.0x less per output token ($0.50 vs $10.00 per million). Updated October 21, 2026; 79 benchmarks have a result for both.
GPT-5 or Llama 4 Scout: which to pick
Only GPT-5 has a coding score, 38.8 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 20.0x cheaper ($0.50 vs $10.00 per million) and a larger context window (1.31M against 400K tokens).
- Shared results
- 79
- GPT-5 only
- 34
- Llama 4 Scout only
- 3
- 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 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5
GPT-5 leads, 23.0 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
GPT-5 leads, 85.6% against 47.0%.
Same board, both measured
Math
AIME
GPT-5
GPT-5 leads, 93.4% against 19.0%.
Same board, both measured
Knowledge
MMLU Pro
GPT-5
GPT-5 leads, 86.5% against 69.6%.
Same board, both measured
Web design
Design Arena: websites
GPT-5
GPT-5 leads, 1184 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
Llama 4 Scout
GPT-5 $0.0063, Llama 4 Scout $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5
GPT-5 $0.39, Llama 4 Scout $1.276.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5 38.8 from 3 benchmarks, Llama 4 Scout not benchmarked.
- Widest category
- Agentic work, 71.1 points apart on average over 1 shared benchmark.
- Widest single gap
- OTIS Mock AIME: GPT-5 91.4% against Llama 4 Scout 7.8%, 83.6 points apart on a 0 to 100 scale.
- Output price
- GPT-5 $10.00 against Llama 4 Scout $0.50 per million output tokens, 20.0x apart.
- Context window
- GPT-5 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 | Bars | Llama 4 Scout | Ties |
|---|---|---|---|---|
| Coding | 7 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 13 | 0 | ||
| Math | 9 | 0 | ||
| Knowledge | 12 | 0 | ||
| Web design | 5 | 0 | ||
| General | 30 | 0 | ||
| All shared boards | 79 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index70
| Benchmark | GPT-5 | Llama 4 Scout | Leader |
|---|---|---|---|
| Aider polyglot | 88.0% | Not measured | Only GPT-5 measured |
| SWE-bench Verified | 74.4% | 9.1% | GPT-5 |
| LMArena WebDev | 1418 | Not measured | Only GPT-5 measured |
| AA Coding Index | 37.8 | 8.2 | GPT-5 |
| LiveCodeBench (Vals AI) | 85.9% | 38.5% | GPT-5 |
| SWE-bench Verified (Vals AI) | 69.0% | Not measured | Only GPT-5 measured |
| Vibe Code Bench (Vals AI) | 20.1% | Not measured | Only GPT-5 measured |
| LiveCodeBench (Artificial Analysis run) | 84.6% | 29.9% | GPT-5 |
| SciCode | Not measured | 21.3% | Only Llama 4 Scout measured |
| Terminal-Bench Hard (Artificial Analysis run) | 37.9% | 1.5% | GPT-5 |
| Terminal-Bench | 49.6% | Not measured | Only GPT-5 measured |
| GSO-Bench | 6.9% | Not measured | Only GPT-5 measured |
| WeirdML | 60.7% | Not measured | Only GPT-5 measured |
| ALE-Bench | 807.6 | Not measured | Only GPT-5 measured |
| LMArena Text: coding | 1436 | 1286 | GPT-5 |
| LMArena Text: software and IT | 1420 | 1286 | GPT-5 |
| LMArena WebDev: HTML | 1427 | Not measured | Only GPT-5 measured |
| SciCode | 0.0% | Not measured | Only GPT-5 measured |
| Neither model has a result on the other 21 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5 | Llama 4 Scout | Leader |
|---|---|---|---|
| AA Intelligence Index | 23.0 | 6.5 | GPT-5 |
| Epoch Capabilities Index | 150.0 | 129.6 | GPT-5 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5 | Llama 4 Scout | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 0.5 | Only Llama 4 Scout measured |
| τ²-Bench Telecom | 86.5% | 15.5% | GPT-5 |
| METR time horizons | 69.6% | Not measured | Only GPT-5 measured |
| Remote Labor Index | 1.7% | Not measured | Only GPT-5 measured |
| GDPval | 34.8% | Not measured | Only GPT-5 measured |
| BALROG | 32.8% | Not measured | Only GPT-5 measured |
| DeepResearch Bench | 49.6% | Not measured | Only GPT-5 measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 28.1% | Only Llama 4 Scout measured |
| BrowseComp-Plus | 80.0% | Not measured | Only GPT-5 measured |
| Neither model has a result on the other 32 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index130
| Benchmark | GPT-5 | Llama 4 Scout | Leader |
|---|---|---|---|
| GPQA Diamond | 85.6% | 47.0% | GPT-5 |
| MMMU Pro (Vals AI) | 81.5% | 58.8% | GPT-5 |
| GPQA Diamond (Artificial Analysis run) | 85.4% | 58.7% | GPT-5 |
| Humanity's Last Exam (Artificial Analysis run) | 28.5% | 3.8% | GPT-5 |
| GPQA Diamond (Epoch AI run) | 86.2% | 51.8% | GPT-5 |
| ARC-AGI | 65.7% | 0.5% | GPT-5 |
| ARC-AGI-2 | 9.9% | 0.0% | GPT-5 |
| SimpleBench | 56.7% | Not measured | Only GPT-5 measured |
| Chess puzzles | 36.0% | Not measured | Only GPT-5 measured |
| Mystery game puzzles | 23.0% | Not measured | Only GPT-5 measured |
| EBR-bench | 12.7% | Not measured | Only GPT-5 measured |
| VPCT | 66.0% | Not measured | Only GPT-5 measured |
| LMArena Text: hard prompts | 1415 | 1266 | GPT-5 |
| LMArena Text: expert | 1419 | 1235 | GPT-5 |
| LMArena Text: hard prompts in English | 1419 | 1293 | GPT-5 |
| LMArena Vision: diagrams | 1252 | 1125 | GPT-5 |
| LMArena Vision: homework | 1278 | 1148 | GPT-5 |
| Kagi LLM Benchmark | 72.7% | 36.9% | GPT-5 |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index90
| Benchmark | GPT-5 | Llama 4 Scout | Leader |
|---|---|---|---|
| AIME | 93.4% | 19.0% | GPT-5 |
| MATH 500 (Vals AI) | 96.0% | 79.2% | GPT-5 |
| MGSM (Vals AI) | 92.8% | 88.0% | GPT-5 |
| AA Math Index | 94.3 | 14.0 | GPT-5 |
| AIME 2025 (Artificial Analysis run) | 94.3% | 14.0% | GPT-5 |
| OTIS Mock AIME | 91.4% | 7.8% | GPT-5 |
| FrontierMath, tiers 1 to 3 | 55.4% | Not measured | Only GPT-5 measured |
| FrontierMath, tier 4 | 22.0% | Not measured | Only GPT-5 measured |
| ProofBench | 18.0% | Not measured | Only GPT-5 measured |
| MATH level 5 | 98.1% | 62.3% | GPT-5 |
| LMArena Text: math | 1407 | 1287 | GPT-5 |
| LMArena Text: mathematical work | 1405 | 1286 | GPT-5 |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index120
| Benchmark | GPT-5 | Llama 4 Scout | Leader |
|---|---|---|---|
| MMLU Pro | 86.5% | 69.6% | GPT-5 |
| LegalBench (Vals AI) | 86.0% | 72.0% | GPT-5 |
| CaseLaw (Vals AI) | 66.5% | Not measured | Only GPT-5 measured |
| CorpFin (Vals AI) | 61.1% | 46.8% | GPT-5 |
| TaxEval (Vals AI) | 73.4% | 55.2% | GPT-5 |
| MortgageTax (Vals AI) | 65.5% | 57.8% | GPT-5 |
| MedQA (Vals AI) | 96.3% | 50.9% | GPT-5 |
| MedCode (Vals AI) | 49.6% | 23.3% | GPT-5 |
| MMLU Pro (Artificial Analysis run) | 87.1% | 75.2% | GPT-5 |
| SimpleQA Verified | 50.1% | Not measured | Only GPT-5 measured |
| GeoBench | 81.0% | Not measured | Only GPT-5 measured |
| LMArena Text: science | 1416 | 1278 | GPT-5 |
| LMArena Text: medicine and healthcare | 1413 | 1275 | GPT-5 |
| LMArena Text: legal and government | 1431 | 1285 | GPT-5 |
| LMArena Text: business and finance | 1400 | 1257 | GPT-5 |
| FACTS Benchmark Suite | 56.5% | Not measured | Only GPT-5 measured |
| Neither model has a result on the other 2 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GPT-5 | Llama 4 Scout | Leader |
|---|---|---|---|
| Design Arena: websites | 1184 | 750 | GPT-5 |
| Design Arena: UI components | 1180 | 768 | GPT-5 |
| Design Arena: game dev | 1185 | 773 | GPT-5 |
| Design Arena: data viz | 1221 | 896 | GPT-5 |
| Design Arena: 3D | 1056 | Not measured | Only GPT-5 measured |
| Design Arena: all code categories | 1168 | 790 | GPT-5 |
| Design Arena: SVG | 1189 | Not measured | Only GPT-5 measured |
| Design Arena: ASCII art | 1141 | Not measured | Only GPT-5 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index300
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-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 | 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 | $1.25 | $0.175 |
| Output $/Mtok | $10.00 | $0.50 |
| Coding index | 38.8 | Not benchmarked |
| Independent measurements behind it | 3 | 0 |
| Cost per solved task | $0.39 | $1.276 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5 better than Llama 4 Scout for coding?
Only GPT-5 has a coding score, 38.8 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 20.0x cheaper ($0.50 vs $10.00 per million) and a larger context window (1.31M against 400K tokens).
Which is cheaper, GPT-5 or Llama 4 Scout?
GPT-5 costs $1.25 per million input tokens and $10.00 per million output tokens; Llama 4 Scout costs $0.175 and $0.50. Per solved SWE-bench Verified task, GPT-5 costs $0.39 and Llama 4 Scout $1.276.
Which has the bigger context window, GPT-5 or Llama 4 Scout?
Llama 4 Scout, with 1.31M tokens against 400K.
How many benchmarks is the GPT-5 vs Llama 4 Scout comparison based on?
79 benchmarks have a result for both models as of October 21, 2026. GPT-5's coding index rests on 3 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.