Llama 4 Scout vs Qwen3 32B
Updated October 21, 2026. 41 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick Qwen3 32B for coding: it scores 40.0 on the Vibe Coding Index against 9.1 for Llama 4 Scout.
Pick Llama 4 Scout when the context window is larger, 1.31M against 131K tokens, or when what matters is IFBench or Long context reasoning (AA-LCR), where it leads.
- Shared results
- 41
- Llama 4 Scout only
- 39
- Qwen3 32B only
- 1
- Like-for-like categories
- 7 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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, four public coding benchmarks
Qwen3 32B
Qwen3 32B scores 40.0 against 9.1.
One benchmark behind each score
Intelligence
AA Intelligence Index
Qwen3 32B
Qwen3 32B leads, 7.3 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
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 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 32B
Llama 4 Scout $0.0003, Qwen3 32B $0.0002 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 9.1 from 1 benchmark, Qwen3 32B 40.0 from 1 benchmark.
- Widest category
- Math, 43.6 points apart on average over 5 shared benchmarks.
- Widest single gap
- OTIS Mock AIME: Llama 4 Scout 7.8% against Qwen3 32B 66.9%, 59.2 points apart on a 0 to 100 scale.
- Output price
- Llama 4 Scout $0.30 against Qwen3 32B $0.28 per million output tokens, 1.1x apart.
- Context window
- Llama 4 Scout takes 1.31M tokens, Qwen3 32B 131K.
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 | Qwen3 32B | Ties |
|---|---|---|---|---|
| Coding | 0 | 5 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 6 | ||
| Math | 0 | 5 | ||
| Knowledge | 0 | 5 | ||
| General | 2 | 15 | ||
| All shared boards | 2 | 39 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index05
| Benchmark | Llama 4 Scout | Qwen3 32B | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 40.0% | Only Qwen3 32B measured |
| SWE-bench Verified | 9.1% | Not measured | Only Llama 4 Scout 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% | 54.6% | Qwen3 32B |
| SciCode | 21.3% | 36.0% | Qwen3 32B |
| Terminal-Bench Hard (Artificial Analysis run) | 1.5% | 3.0% | Qwen3 32B |
| LMArena Text: coding | 1286 | 1357 | Qwen3 32B |
| LMArena Text: software and IT | 1286 | 1366 | Qwen3 32B |
| Neither model has a result on the other 29 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Llama 4 Scout | Qwen3 32B | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.5 | 7.3 | Qwen3 32B |
| Epoch Capabilities Index | 129.6 | 138.4 | Qwen3 32B |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 4 Scout | Qwen3 32B | Leader |
|---|---|---|---|
| AA Agentic Index | 0.5 | Not measured | Only Llama 4 Scout measured |
| τ²-Bench Telecom | 15.5% | 29.8% | Qwen3 32B |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | Llama 4 Scout | Qwen3 32B | 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% | 66.8% | Qwen3 32B |
| Humanity's Last Exam (Artificial Analysis run) | 3.8% | 7.4% | Qwen3 32B |
| GPQA Diamond (Epoch AI run) | 51.8% | 65.7% | Qwen3 32B |
| 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 | 1334 | Qwen3 32B |
| LMArena Text: expert | 1235 | 1362 | Qwen3 32B |
| LMArena Text: hard prompts in English | 1293 | 1350 | Qwen3 32B |
| LMArena Vision: diagrams | 1125 | Not measured | Only Llama 4 Scout measured |
| LMArena Vision: homework | 1148 | 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 index05
| Benchmark | Llama 4 Scout | Qwen3 32B | 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 | 73.0 | Qwen3 32B |
| AIME 2025 (Artificial Analysis run) | 14.0% | 73.0% | Qwen3 32B |
| OTIS Mock AIME | 7.8% | 66.9% | Qwen3 32B |
| MATH level 5 | 62.3% | Not measured | Only Llama 4 Scout measured |
| LMArena Text: math | 1287 | 1399 | Qwen3 32B |
| LMArena Text: mathematical work | 1286 | 1420 | Qwen3 32B |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Llama 4 Scout | Qwen3 32B | 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% | 79.8% | Qwen3 32B |
| LMArena Text: science | 1278 | 1372 | Qwen3 32B |
| LMArena Text: medicine and healthcare | 1275 | 1368 | Qwen3 32B |
| LMArena Text: legal and government | 1285 | 1324 | Qwen3 32B |
| LMArena Text: business and finance | 1257 | 1318 | Qwen3 32B |
| Neither model has a result on the other 5 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Llama 4 Scout | Qwen3 32B | Leader |
|---|---|---|---|
| Design Arena: websites | 750 | Not measured | Only Llama 4 Scout measured |
| Design Arena: UI components | 767 | Not measured | Only Llama 4 Scout measured |
| Design Arena: game dev | 773 | Not measured | Only Llama 4 Scout measured |
| Design Arena: data viz | 896 | Not measured | Only Llama 4 Scout measured |
| Design Arena: all code categories | 790 | 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 index215
| Benchmark | Llama 4 Scout | Qwen3 32B | 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% | 36.3% | Llama 4 Scout |
| Long context reasoning (AA-LCR) | 27.7% | 0.0% | Llama 4 Scout |
| DTBench | 57.9% | 67.5% | Qwen3 32B |
| LMCA | 12.0% | 17.3% | Qwen3 32B |
| Fiction.LiveBench | 36.0% | 74.2% | Qwen3 32B |
| LMArena Text | 1279 | 1340 | Qwen3 32B |
| LMArena Text: instruction following | 1248 | 1305 | Qwen3 32B |
| LMArena Text: creative writing | 1249 | 1297 | Qwen3 32B |
| LMArena Vision | 1118 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: English | 1304 | 1359 | Qwen3 32B |
| LMArena Text: non-English | 1252 | 1317 | Qwen3 32B |
| LMArena Text: Chinese | 1255 | 1357 | Qwen3 32B |
| LMArena Text: German | 1273 | 1341 | Qwen3 32B |
| 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 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: Spanish | 1278 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: Russian | 1263 | 1311 | Qwen3 32B |
| LMArena Text: Polish | 1265 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: multi-turn | 1280 | 1331 | Qwen3 32B |
| LMArena Text: longer queries | 1265 | 1327 | Qwen3 32B |
| LMArena Text: writing and language | 1269 | 1298 | Qwen3 32B |
| LMArena Text: entertainment and media | 1242 | 1279 | Qwen3 32B |
| 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 | Qwen3 32B | 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
Qwen3 32B costs less
Repository review
50K input + 3K output tokens
Qwen3 32B costs less
Agent loop
200K input + 10K output tokens
Qwen3 32B 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 | Qwen3 32B |
|---|---|---|
| Maker | Meta Llama | Qwen |
| Weights | Open weights | Open weights |
| Released | April 5, 2025 | April 28, 2025 |
| Context window | 1.31M | 131K |
| Input $/Mtok | $0.10 | $0.08 |
| Output $/Mtok | $0.30 | $0.28 |
| Coding index | 9.1 | 40.0 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | $1.276 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Llama 4 Scout better than Qwen3 32B for coding?
Pick Qwen3 32B for coding: it scores 40.0 on the Vibe Coding Index against 9.1 for Llama 4 Scout. Pick Llama 4 Scout when the context window is larger, 1.31M against 131K tokens, or when what matters is IFBench or Long context reasoning (AA-LCR), where it leads.
Which is cheaper, Llama 4 Scout or Qwen3 32B?
Llama 4 Scout costs $0.10 per million input tokens and $0.30 per million output tokens; Qwen3 32B costs $0.08 and $0.28. Cost per solved task is known for Llama 4 Scout only.
Which has the bigger context window, Llama 4 Scout or Qwen3 32B?
Llama 4 Scout, with 1.31M tokens against 131K.
How many benchmarks is the Llama 4 Scout vs Qwen3 32B comparison based on?
41 benchmarks have a result for both models as of October 21, 2026. Llama 4 Scout's coding index rests on 1 independent benchmark and Qwen3 32B's on 1. Where only one model has a result, the page says so instead of naming a winner.