Llama 4 Scout vs Qwen3.6 27B
Qwen3.6 27B leads Llama 4 Scout on the Artificial Analysis Intelligence Index, 21.4 to 6.5, and Llama 4 Scout costs 5.4x less per output token ($0.50 vs $2.70 per million). Updated October 22, 2026; 19 benchmarks have a result for both.
Llama 4 Scout or Qwen3.6 27B: which to pick
Only Qwen3.6 27B has a coding score, 36.8 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 5.4x cheaper ($0.50 vs $2.70 per million) and a lower cost per solved task ($1.276 against $1.30).
- Shared results
- 19
- Llama 4 Scout only
- 63
- Qwen3.6 27B only
- 5
- Like-for-like categories
- 7 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 Qwen3.6 27B has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Qwen3.6 27B
Qwen3.6 27B leads, 21.4 against 6.5.
Same board, both measured
Agentic work
AA Agentic Index
Qwen3.6 27B
Qwen3.6 27B leads, 18.5 against 0.5.
Same board, both measured
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 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 4 Scout
Llama 4 Scout $0.0004, Qwen3.6 27B $0.0018 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Llama 4 Scout
Llama 4 Scout $1.276, Qwen3.6 27B $1.30.
Published attempt cost and pass rate
At a glance
- Coding index
- Llama 4 Scout not benchmarked, Qwen3.6 27B 36.8 from 2 benchmarks.
- Widest category
- Math, 83.3 points apart on average over 1 shared benchmark.
- Widest single gap
- OTIS Mock AIME: Llama 4 Scout 7.8% against Qwen3.6 27B 91.1%, 83.3 points apart on a 0 to 100 scale.
- Output price
- Llama 4 Scout $0.50 against Qwen3.6 27B $2.70 per million output tokens, 5.4x apart.
- Context window
- Llama 4 Scout takes 1.31M tokens, Qwen3.6 27B 262K.
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 | Qwen3.6 27B | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 2 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 1 | ||
| Knowledge | 0 | 3 | ||
| General | 0 | 5 | ||
| All shared boards | 0 | 19 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | Llama 4 Scout | Qwen3.6 27B | Leader |
|---|---|---|---|
| SWE-bench Verified | 9.1% | Not measured | Only Llama 4 Scout measured |
| AA Coding Index | 8.2 | 53.7 | Qwen3.6 27B |
| LiveCodeBench (Vals AI) | 38.5% | Not measured | Only Llama 4 Scout measured |
| SWE-bench Verified (Vals AI) | Not measured | 70.0% | Only Qwen3.6 27B measured |
| Vibe Code Bench (Vals AI) | Not measured | 11.9% | Only Qwen3.6 27B measured |
| LiveCodeBench (Artificial Analysis run) | 29.9% | Not measured | Only Llama 4 Scout measured |
| SciCode | 21.3% | 42.8% | Qwen3.6 27B |
| Terminal-Bench Hard (Artificial Analysis run) | 1.5% | 34.8% | Qwen3.6 27B |
| LMArena Text: coding | 1286 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: software and IT | 1286 | Not measured | Only Llama 4 Scout measured |
| 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 | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.5 | 21.4 | Qwen3.6 27B |
| Epoch Capabilities Index | 129.6 | 146.2 | Qwen3.6 27B |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Llama 4 Scout | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Agentic Index | 0.5 | 18.5 | Qwen3.6 27B |
| τ²-Bench Telecom | 15.5% | 94.2% | Qwen3.6 27B |
| 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 | Qwen3.6 27B | 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% | 84.2% | Qwen3.6 27B |
| Humanity's Last Exam (Artificial Analysis run) | 3.8% | 23.1% | Qwen3.6 27B |
| GPQA Diamond (Epoch AI run) | 51.8% | 85.9% | Qwen3.6 27B |
| ARC-AGI | 0.5% | Not measured | Only Llama 4 Scout measured |
| ARC-AGI-2 | 0.0% | Not measured | Only Llama 4 Scout measured |
| Mystery game puzzles | Not measured | 7.0% | Only Qwen3.6 27B measured |
| LMArena Text: hard prompts | 1266 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: expert | 1235 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: hard prompts in English | 1293 | Not measured | Only Llama 4 Scout measured |
| 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 8 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 4 Scout | Qwen3.6 27B | 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% | 91.1% | Qwen3.6 27B |
| FrontierMath, tiers 1 to 3 | Not measured | 35.1% | Only Qwen3.6 27B measured |
| MATH level 5 | 62.3% | Not measured | Only Llama 4 Scout measured |
| LMArena Text: math | 1287 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: mathematical work | 1286 | Not measured | Only Llama 4 Scout measured |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Llama 4 Scout | Qwen3.6 27B | Leader |
|---|---|---|---|
| MMLU Pro | 69.6% | Not measured | Only Llama 4 Scout measured |
| LegalBench (Vals AI) | 72.0% | Not measured | Only Llama 4 Scout measured |
| CaseLaw (Vals AI) | Not measured | 53.2% | Only Qwen3.6 27B measured |
| CorpFin (Vals AI) | 46.8% | 62.3% | Qwen3.6 27B |
| TaxEval (Vals AI) | 55.2% | 71.3% | Qwen3.6 27B |
| MortgageTax (Vals AI) | 57.8% | 68.3% | Qwen3.6 27B |
| 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 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: medicine and healthcare | 1275 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: legal and government | 1285 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: business and finance | 1257 | Not measured | Only Llama 4 Scout measured |
| 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 | Qwen3.6 27B | Leader |
|---|---|---|---|
| Design Arena: websites | 750 | Not measured | Only Llama 4 Scout measured |
| Design Arena: UI components | 765 | Not measured | Only Llama 4 Scout measured |
| Design Arena: game dev | 772 | Not measured | Only Llama 4 Scout measured |
| 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 index05
| Benchmark | Llama 4 Scout | Qwen3.6 27B | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 34.8% | 45.6% | Qwen3.6 27B |
| MedScribe (Vals AI) | 50.6% | Not measured | Only Llama 4 Scout measured |
| IFBench | 39.5% | 67.6% | Qwen3.6 27B |
| Long context reasoning (AA-LCR) | 27.7% | 77.3% | Qwen3.6 27B |
| DTBench | 57.9% | 78.1% | Qwen3.6 27B |
| LMCA | 12.0% | 34.5% | Qwen3.6 27B |
| Fiction.LiveBench | 36.0% | Not measured | Only Llama 4 Scout measured |
| LMArena Text | 1279 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: instruction following | 1248 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: creative writing | 1249 | Not measured | Only Llama 4 Scout measured |
| LMArena Vision | 1118 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: English | 1304 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: non-English | 1252 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: Chinese | 1255 | Not measured | Only Llama 4 Scout measured |
| 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 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: Spanish | 1278 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: Russian | 1263 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: Polish | 1265 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: multi-turn | 1280 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: longer queries | 1265 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: writing and language | 1269 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: entertainment and media | 1242 | Not measured | Only Llama 4 Scout measured |
| 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 | Qwen3.6 27B | 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 | Qwen3.6 27B |
|---|---|---|
| Maker | Meta Llama | Qwen |
| Weights | Open weights | Open weights |
| Released | April 5, 2025 | April 27, 2026 |
| Context window | 1.31M | 262K |
| Input $/Mtok | $0.175 | $0.45 |
| Output $/Mtok | $0.50 | $2.70 |
| Coding index | Not benchmarked | 36.8 |
| Independent measurements behind it | 0 | 2 |
| Cost per solved task | $1.276 | $1.30 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Llama 4 Scout better than Qwen3.6 27B for coding?
Only Qwen3.6 27B has a coding score, 36.8 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 5.4x cheaper ($0.50 vs $2.70 per million) and a lower cost per solved task ($1.276 against $1.30).
Which is cheaper, Llama 4 Scout or Qwen3.6 27B?
Llama 4 Scout costs $0.175 per million input tokens and $0.50 per million output tokens; Qwen3.6 27B costs $0.45 and $2.70. Per solved SWE-bench Verified task, Llama 4 Scout costs $1.276 and Qwen3.6 27B $1.30.
Which has the bigger context window, Llama 4 Scout or Qwen3.6 27B?
Llama 4 Scout, with 1.31M tokens against 262K.
How many benchmarks is the Llama 4 Scout vs Qwen3.6 27B comparison based on?
19 benchmarks have a result for both models as of October 22, 2026. Llama 4 Scout's coding index rests on 0 independent benchmarks and Qwen3.6 27B's on 2. Where only one model has a result, the page says so instead of naming a winner.