Llama 4 Scout vs Beam 1.0 501B A23B
Llama 4 Scout costs 2.2x less per output token than Beam 1.0 501B A23B ($0.50 vs $1.10 per million). Updated October 22, 2026; 25 benchmarks have a result for both.
Llama 4 Scout or Beam 1.0 501B A23B: 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 2.2x cheaper ($0.50 vs $1.10 per million) and a larger context window (1.31M against 1.05M tokens).
- Shared results
- 25
- Llama 4 Scout only
- 57
- Beam 1.0 501B A23B only
- 3
- Like-for-like categories
- 6 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
Beam 1.0 501B A23B
Beam 1.0 501B A23B leads, 1250 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
Llama 4 Scout $0.0004, Beam 1.0 501B A23B $0.0009 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
- Llama 4 Scout not benchmarked, Beam 1.0 501B A23B not benchmarked.
- Widest category
- Web design, 59.5 points apart on average over 2 shared benchmarks.
- Widest single gap
- Design Arena: websites: Llama 4 Scout 750 against Beam 1.0 501B A23B 1250, 62.5 points apart on a 0 to 100 scale.
- Output price
- Llama 4 Scout $0.50 against Beam 1.0 501B A23B $1.10 per million output tokens, 2.2x apart.
- Context window
- Llama 4 Scout takes 1.31M tokens, Beam 1.0 501B A23B 1.05M.
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 | Beam 1.0 501B A23B | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| Web design | 0 | 2 | ||
| General | 0 | 12 | ||
| All shared boards | 0 | 25 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | Llama 4 Scout | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| SWE-bench Verified | 9.1% | Not measured | Only Llama 4 Scout measured |
| LMArena WebDev | Not measured | 1437 | Only Beam 1.0 501B A23B measured |
| AA Coding Index | 8.2 | Not measured | Only Llama 4 Scout measured |
| LiveCodeBench (Vals AI) | 38.5% | Not measured | Only Llama 4 Scout measured |
| LiveCodeBench (Artificial Analysis run) | 29.9% | Not measured | Only Llama 4 Scout measured |
| SciCode | 21.3% | Not measured | Only Llama 4 Scout measured |
| Terminal-Bench Hard (Artificial Analysis run) | 1.5% | Not measured | Only Llama 4 Scout measured |
| LMArena Text: coding | 1286 | 1473 | Beam 1.0 501B A23B |
| LMArena Text: software and IT | 1286 | 1461 | Beam 1.0 501B A23B |
| LMArena WebDev: React | Not measured | 1443 | Only Beam 1.0 501B A23B measured |
| LMArena WebDev: HTML | Not measured | 1436 | Only Beam 1.0 501B A23B measured |
| Neither model has a result on the other 28 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Llama 4 Scout | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.5 | Not measured | Only Llama 4 Scout measured |
| Epoch Capabilities Index | 129.6 | Not measured | Only Llama 4 Scout measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Llama 4 Scout | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| AA Agentic Index | 0.5 | Not measured | Only Llama 4 Scout measured |
| τ²-Bench Telecom | 15.5% | Not measured | Only Llama 4 Scout measured |
| Berkeley Function Calling Leaderboard (BFCL) | 28.1% | Not measured | 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 index03
| Benchmark | Llama 4 Scout | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| GPQA Diamond | 47.0% | Not measured | Only Llama 4 Scout measured |
| MMMU Pro (Vals AI) | 58.8% | Not measured | Only Llama 4 Scout measured |
| GPQA Diamond (Artificial Analysis run) | 58.7% | Not measured | Only Llama 4 Scout measured |
| Humanity's Last Exam (Artificial Analysis run) | 3.8% | Not measured | Only Llama 4 Scout measured |
| GPQA Diamond (Epoch AI run) | 51.8% | Not measured | Only Llama 4 Scout measured |
| ARC-AGI | 0.5% | Not measured | Only Llama 4 Scout measured |
| ARC-AGI-2 | 0.0% | Not measured | Only Llama 4 Scout measured |
| LMArena Text: hard prompts | 1266 | 1442 | Beam 1.0 501B A23B |
| LMArena Text: expert | 1235 | 1447 | Beam 1.0 501B A23B |
| LMArena Text: hard prompts in English | 1293 | 1451 | Beam 1.0 501B A23B |
| LMArena Vision: diagrams | 1125 | Not measured | Only Llama 4 Scout measured |
| LMArena Vision: homework | 1148 | Not measured | Only Llama 4 Scout measured |
| Kagi LLM Benchmark | 36.9% | Not measured | Only Llama 4 Scout measured |
| Neither model has a result on the other 9 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Llama 4 Scout | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| AIME | 19.0% | Not measured | Only Llama 4 Scout measured |
| MATH 500 (Vals AI) | 79.2% | Not measured | Only Llama 4 Scout measured |
| MGSM (Vals AI) | 88.0% | Not measured | Only Llama 4 Scout measured |
| AA Math Index | 14.0 | Not measured | Only Llama 4 Scout measured |
| AIME 2025 (Artificial Analysis run) | 14.0% | Not measured | Only Llama 4 Scout measured |
| OTIS Mock AIME | 7.8% | Not measured | Only Llama 4 Scout measured |
| MATH level 5 | 62.3% | Not measured | Only Llama 4 Scout measured |
| LMArena Text: math | 1287 | 1459 | Beam 1.0 501B A23B |
| LMArena Text: mathematical work | 1286 | 1475 | Beam 1.0 501B A23B |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Llama 4 Scout | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| MMLU Pro | 69.6% | Not measured | Only Llama 4 Scout measured |
| LegalBench (Vals AI) | 72.0% | Not measured | Only Llama 4 Scout measured |
| CorpFin (Vals AI) | 46.8% | Not measured | Only Llama 4 Scout measured |
| TaxEval (Vals AI) | 55.2% | Not measured | Only Llama 4 Scout measured |
| MortgageTax (Vals AI) | 57.8% | Not measured | Only Llama 4 Scout measured |
| MedQA (Vals AI) | 50.9% | Not measured | Only Llama 4 Scout measured |
| MedCode (Vals AI) | 23.3% | Not measured | Only Llama 4 Scout measured |
| MMLU Pro (Artificial Analysis run) | 75.2% | Not measured | Only Llama 4 Scout measured |
| LMArena Text: science | 1278 | 1430 | Beam 1.0 501B A23B |
| LMArena Text: medicine and healthcare | 1275 | 1376 | Beam 1.0 501B A23B |
| LMArena Text: legal and government | 1285 | 1433 | Beam 1.0 501B A23B |
| LMArena Text: business and finance | 1257 | 1430 | Beam 1.0 501B A23B |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Llama 4 Scout | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| Design Arena: websites | 750 | 1250 | Beam 1.0 501B A23B |
| Design Arena: UI components | 765 | Not measured | Only Llama 4 Scout measured |
| Design Arena: game dev | 772 | 1224 | Beam 1.0 501B A23B |
| Design Arena: data viz | 896 | Not measured | Only Llama 4 Scout measured |
| Design Arena: all code categories | 789 | Not measured | Only Llama 4 Scout measured |
| Neither model has a result on the other 4 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index012
| Benchmark | Llama 4 Scout | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 34.8% | Not measured | Only Llama 4 Scout measured |
| MedScribe (Vals AI) | 50.6% | Not measured | Only Llama 4 Scout measured |
| IFBench | 39.5% | Not measured | Only Llama 4 Scout measured |
| Long context reasoning (AA-LCR) | 27.7% | Not measured | Only Llama 4 Scout measured |
| DTBench | 57.9% | Not measured | Only Llama 4 Scout measured |
| LMCA | 12.0% | Not measured | Only Llama 4 Scout measured |
| Fiction.LiveBench | 36.0% | Not measured | Only Llama 4 Scout measured |
| LMArena Text | 1279 | 1425 | Beam 1.0 501B A23B |
| LMArena Text: instruction following | 1248 | 1430 | Beam 1.0 501B A23B |
| LMArena Text: creative writing | 1249 | 1390 | Beam 1.0 501B A23B |
| LMArena Vision | 1118 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: English | 1304 | 1436 | Beam 1.0 501B A23B |
| LMArena Text: non-English | 1252 | 1413 | Beam 1.0 501B A23B |
| LMArena Text: Chinese | 1255 | 1424 | Beam 1.0 501B A23B |
| LMArena Text: German | 1273 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: Japanese | 1206 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: Korean | 1207 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: French | 1283 | 1461 | Beam 1.0 501B A23B |
| LMArena Text: Spanish | 1278 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: Russian | 1263 | 1412 | Beam 1.0 501B A23B |
| LMArena Text: Polish | 1265 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: multi-turn | 1280 | 1414 | Beam 1.0 501B A23B |
| LMArena Text: longer queries | 1265 | 1429 | Beam 1.0 501B A23B |
| LMArena Text: writing and language | 1269 | 1400 | Beam 1.0 501B A23B |
| LMArena Text: entertainment and media | 1242 | 1399 | Beam 1.0 501B A23B |
| LMArena Vision: English | 1143 | Not measured | Only Llama 4 Scout measured |
| LMArena Vision: Chinese | 1106 | Not measured | Only Llama 4 Scout measured |
| LMArena Vision: reading text | 1117 | Not measured | Only Llama 4 Scout measured |
| LMArena Vision: humor | 1102 | Not measured | Only Llama 4 Scout measured |
| LMArena Vision: creative writing | 1126 | Not measured | 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 | Llama 4 Scout | Beam 1.0 501B A23B | 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 | Reflection Beam 1.0 501B A23B |
|---|---|---|
| Maker | Meta Llama | Reflection |
| Weights | Open weights | Open weights |
| Released | April 5, 2025 | October 16, 2026 |
| Context window | 1.31M | 1.05M |
| Input $/Mtok | $0.175 | $0.35 |
| Output $/Mtok | $0.50 | $1.10 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | $1.276 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Llama 4 Scout better than Beam 1.0 501B A23B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Llama 4 Scout for output tokens 2.2x cheaper ($0.50 vs $1.10 per million) and a larger context window (1.31M against 1.05M tokens).
Which is cheaper, Llama 4 Scout or Beam 1.0 501B A23B?
Llama 4 Scout costs $0.175 per million input tokens and $0.50 per million output tokens; Beam 1.0 501B A23B costs $0.35 and $1.10. Cost per solved task is known for Llama 4 Scout only.
Which has the bigger context window, Llama 4 Scout or Beam 1.0 501B A23B?
Llama 4 Scout, with 1.31M tokens against 1.05M.
How many benchmarks is the Llama 4 Scout vs Beam 1.0 501B A23B comparison based on?
25 benchmarks have a result for both models as of October 22, 2026. Llama 4 Scout's coding index rests on 0 independent benchmarks and Beam 1.0 501B A23B's on 1. Where only one model has a result, the page says so instead of naming a winner.