Llama 4 Scout vs o3
o3 leads Llama 4 Scout on the Artificial Analysis Intelligence Index, 20.2 to 6.5, and Llama 4 Scout costs 16.0x less per output token ($0.50 vs $8.00 per million). Updated October 21, 2026; 79 benchmarks have a result for both.
Llama 4 Scout or o3: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Llama 4 Scout for output tokens 16.0x cheaper ($0.50 vs $8.00 per million) and a larger context window (1.31M against 200K tokens).
- Shared results
- 79
- Llama 4 Scout only
- 3
- o3 only
- 23
- 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
o3
o3 leads, 20.2 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
o3
o3 leads, 84.1% against 47.0%.
Same board, both measured
Math
AIME
o3
o3 leads, 85.3% against 19.0%.
Same board, both measured
Knowledge
MMLU Pro
o3
o3 leads, 85.6% against 69.6%.
Same board, both measured
Web design
Design Arena: websites
o3
o3 leads, 1036 against 750.
Same board, both measured
Long documents
Listed context window
Llama 4 Scout
Llama 4 Scout takes 1.31M tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 4 Scout
Llama 4 Scout $0.0004, o3 $0.0060 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
o3
Llama 4 Scout $1.276, o3 $0.571.
Published attempt cost and pass rate
At a glance
- Coding index
- Llama 4 Scout not benchmarked, o3 not benchmarked.
- Widest category
- Agentic work, 50.1 points apart on average over 2 shared benchmarks.
- Widest single gap
- OTIS Mock AIME: Llama 4 Scout 7.8% against o3 84.4%, 76.7 points apart on a 0 to 100 scale.
- Output price
- Llama 4 Scout $0.50 against o3 $8.00 per million output tokens, 16.0x apart.
- Context window
- Llama 4 Scout takes 1.31M tokens, o3 200K.
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 | Llama 4 Scout | Bars | o3 | Ties |
|---|---|---|---|---|
| Coding | 0 | 6 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 2 | ||
| Reasoning | 0 | 13 | ||
| Math | 0 | 9 | ||
| Knowledge | 0 | 12 | ||
| Web design | 0 | 5 | ||
| General | 0 | 30 | ||
| All shared boards | 0 | 79 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index06
| Benchmark | Llama 4 Scout | o3 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 81.3% | Only o3 measured |
| SWE-bench Verified | 9.1% | 58.4% | o3 |
| SWE-bench Multimodal | Not measured | 36.0% | Only o3 measured |
| AA Coding Index | 8.2 | Not measured | Only Llama 4 Scout measured |
| LiveCodeBench (Vals AI) | 38.5% | 83.9% | o3 |
| LiveCodeBench (Artificial Analysis run) | 29.9% | 80.8% | o3 |
| SciCode | 21.3% | Not measured | Only Llama 4 Scout measured |
| Terminal-Bench Hard (Artificial Analysis run) | 1.5% | 37.1% | o3 |
| GSO-Bench | Not measured | 8.8% | Only o3 measured |
| WeirdML | Not measured | 52.4% | Only o3 measured |
| ALE-Bench | Not measured | 933.5 | Only o3 measured |
| LMArena Text: coding | 1286 | 1407 | o3 |
| LMArena Text: software and IT | 1286 | 1415 | o3 |
| SciCode | Not measured | 9.2% | Only o3 measured |
| Neither model has a result on the other 25 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Llama 4 Scout | o3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.5 | 20.2 | o3 |
| Epoch Capabilities Index | 129.6 | 146.7 | o3 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Llama 4 Scout | o3 | Leader |
|---|---|---|---|
| AA Agentic Index | 0.5 | Not measured | Only Llama 4 Scout measured |
| τ²-Bench Telecom | 15.5% | 80.7% | o3 |
| METR time horizons | Not measured | 65.4% | Only o3 measured |
| GDPval | Not measured | 30.8% | Only o3 measured |
| DeepResearch Bench | Not measured | 45.2% | Only o3 measured |
| OSWorld | Not measured | 23.0% | Only o3 measured |
| Berkeley Function Calling Leaderboard (BFCL) | 28.1% | 63.0% | o3 |
| BrowseComp-Plus | Not measured | 65.9% | Only o3 measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index013
| Benchmark | Llama 4 Scout | o3 | Leader |
|---|---|---|---|
| GPQA Diamond | 47.0% | 84.1% | o3 |
| MMMU Pro (Vals AI) | 58.8% | 80.4% | o3 |
| GPQA Diamond (Artificial Analysis run) | 58.7% | 82.7% | o3 |
| Humanity's Last Exam (Artificial Analysis run) | 3.8% | 20.1% | o3 |
| GPQA Diamond (Epoch AI run) | 51.8% | 81.8% | o3 |
| ARC-AGI | 0.5% | 60.8% | o3 |
| ARC-AGI-2 | 0.0% | 6.5% | o3 |
| SimpleBench | Not measured | 53.1% | Only o3 measured |
| Chess puzzles | Not measured | 37.0% | Only o3 measured |
| Mystery game puzzles | Not measured | 29.0% | Only o3 measured |
| VPCT | Not measured | 52.0% | Only o3 measured |
| LMArena Text: hard prompts | 1266 | 1402 | o3 |
| LMArena Text: expert | 1235 | 1402 | o3 |
| LMArena Text: hard prompts in English | 1293 | 1411 | o3 |
| LMArena Vision: diagrams | 1125 | 1220 | o3 |
| LMArena Vision: homework | 1148 | 1234 | o3 |
| Kagi LLM Benchmark | 36.9% | 67.6% | o3 |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index09
| Benchmark | Llama 4 Scout | o3 | Leader |
|---|---|---|---|
| AIME | 19.0% | 85.3% | o3 |
| MATH 500 (Vals AI) | 79.2% | 94.6% | o3 |
| MGSM (Vals AI) | 88.0% | 91.8% | o3 |
| AA Math Index | 14.0 | 88.3 | o3 |
| AIME 2025 (Artificial Analysis run) | 14.0% | 88.3% | o3 |
| OTIS Mock AIME | 7.8% | 84.4% | o3 |
| FrontierMath, tiers 1 to 3 | Not measured | 33.3% | Only o3 measured |
| MATH level 5 | 62.3% | 97.8% | o3 |
| LMArena Text: math | 1287 | 1426 | o3 |
| LMArena Text: mathematical work | 1286 | 1423 | o3 |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index012
| Benchmark | Llama 4 Scout | o3 | Leader |
|---|---|---|---|
| MMLU Pro | 69.6% | 85.6% | o3 |
| LegalBench (Vals AI) | 72.0% | 83.8% | o3 |
| CorpFin (Vals AI) | 46.8% | 59.7% | o3 |
| TaxEval (Vals AI) | 55.2% | 74.6% | o3 |
| MortgageTax (Vals AI) | 57.8% | 65.7% | o3 |
| MedQA (Vals AI) | 50.9% | 96.1% | o3 |
| MedCode (Vals AI) | 23.3% | 47.3% | o3 |
| MMLU Pro (Artificial Analysis run) | 75.2% | 85.3% | o3 |
| SimpleQA Verified | Not measured | 49.4% | Only o3 measured |
| GeoBench | Not measured | 74.0% | Only o3 measured |
| LMArena Text: science | 1278 | 1429 | o3 |
| LMArena Text: medicine and healthcare | 1275 | 1448 | o3 |
| LMArena Text: legal and government | 1285 | 1428 | o3 |
| LMArena Text: business and finance | 1257 | 1393 | o3 |
| FACTS Benchmark Suite | Not measured | 44.4% | Only o3 measured |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Llama 4 Scout | o3 | Leader |
|---|---|---|---|
| Design Arena: websites | 750 | 1036 | o3 |
| Design Arena: UI components | 768 | 1017 | o3 |
| Design Arena: game dev | 773 | 1034 | o3 |
| Design Arena: data viz | 896 | 1200 | o3 |
| Design Arena: all code categories | 790 | 1020 | o3 |
| Neither model has a result on the other 4 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index030
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 4 Scout | o3 | 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 | Meta Llama 4 Scout | OpenAI o3 |
|---|---|---|
| Maker | Meta Llama | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | April 5, 2025 | April 16, 2025 |
| Context window | 1.31M | 200K |
| Input $/Mtok | $0.175 | $2.00 |
| Output $/Mtok | $0.50 | $8.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | $1.276 | $0.571 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Llama 4 Scout better than o3 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Llama 4 Scout for output tokens 16.0x cheaper ($0.50 vs $8.00 per million) and a larger context window (1.31M against 200K tokens).
Which is cheaper, Llama 4 Scout or o3?
Llama 4 Scout costs $0.175 per million input tokens and $0.50 per million output tokens; o3 costs $2.00 and $8.00. Per solved SWE-bench Verified task, Llama 4 Scout costs $1.276 and o3 $0.571.
Which has the bigger context window, Llama 4 Scout or o3?
Llama 4 Scout, with 1.31M tokens against 200K.
How many benchmarks is the Llama 4 Scout vs o3 comparison based on?
79 benchmarks have a result for both models as of October 21, 2026. Llama 4 Scout's coding index rests on 0 independent benchmarks and o3's on 0. Where only one model has a result, the page says so instead of naming a winner.