GLM 5 vs Llama 4 Scout
GLM 5 leads Llama 4 Scout on the Artificial Analysis Intelligence Index, 27.9 to 6.5, and Llama 4 Scout costs 6.4x less per output token ($0.50 vs $3.20 per million). Updated October 22, 2026; 55 benchmarks have a result for both.
GLM 5 or Llama 4 Scout: which to pick
Only GLM 5 has a coding score, 41.7 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 6.4x cheaper ($0.50 vs $3.20 per million) and a larger context window (1.31M against 205K tokens).
- Shared results
- 55
- GLM 5 only
- 21
- Llama 4 Scout only
- 27
- 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 GLM 5 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GLM 5
GLM 5 leads, 27.9 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
GLM 5
GLM 5 leads, 83.3% against 47.0%.
Same board, both measured
Math
AIME
GLM 5
GLM 5 leads, 91.7% against 19.0%.
Same board, both measured
Knowledge
MMLU Pro
GLM 5
GLM 5 leads, 86.0% against 69.6%.
Same board, both measured
Web design
Design Arena: websites
GLM 5
GLM 5 leads, 1247 against 750.
Same board, both measured
Long documents
Listed context window
Llama 4 Scout
Llama 4 Scout takes 1.31M tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 4 Scout
GLM 5 $0.0026, Llama 4 Scout $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5
GLM 5 $0.63, Llama 4 Scout $1.276.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5 41.7 from 3 benchmarks, Llama 4 Scout not benchmarked.
- Widest category
- Agentic work, 82.7 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: GLM 5 98.2% against Llama 4 Scout 15.5%, 82.7 points apart on a 0 to 100 scale.
- Output price
- GLM 5 $3.20 against Llama 4 Scout $0.50 per million output tokens, 6.4x apart.
- Context window
- GLM 5 takes 205K 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 | GLM 5 | Bars | Llama 4 Scout | Ties |
|---|---|---|---|---|
| Coding | 5 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 10 | 0 | ||
| Math | 4 | 0 | ||
| Knowledge | 9 | 0 | ||
| Web design | 5 | 0 | ||
| General | 19 | 0 | ||
| All shared boards | 55 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index50
| Benchmark | GLM 5 | Llama 4 Scout | Leader |
|---|---|---|---|
| SWE-bench Verified | 72.8% | 9.1% | GLM 5 |
| SWE-bench Multilingual | 69.7% | Not measured | Only GLM 5 measured |
| LMArena WebDev | 1435 | Not measured | Only GLM 5 measured |
| AA Coding Index | Not measured | 8.2 | Only Llama 4 Scout measured |
| LiveCodeBench (Vals AI) | 81.9% | 38.5% | GLM 5 |
| SWE-bench Verified (Vals AI) | 71.4% | Not measured | Only GLM 5 measured |
| Vibe Code Bench (Vals AI) | 23.4% | Not measured | Only GLM 5 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 29.9% | Only Llama 4 Scout measured |
| SciCode | Not measured | 21.3% | Only Llama 4 Scout measured |
| Terminal-Bench Hard (Artificial Analysis run) | 43.2% | 1.5% | GLM 5 |
| Terminal-Bench | 52.4% | Not measured | Only GLM 5 measured |
| WeirdML | 48.2% | Not measured | Only GLM 5 measured |
| ALE-Bench | 765.6 | Not measured | Only GLM 5 measured |
| LMArena Text: coding | 1461 | 1286 | GLM 5 |
| LMArena Text: software and IT | 1460 | 1286 | GLM 5 |
| LMArena WebDev: React | 1424 | Not measured | Only GLM 5 measured |
| LMArena WebDev: HTML | 1450 | Not measured | Only GLM 5 measured |
| Neither model has a result on the other 22 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5 | Llama 4 Scout | Leader |
|---|---|---|---|
| AA Intelligence Index | 27.9 | 6.5 | GLM 5 |
| Epoch Capabilities Index | 145.8 | 129.6 | GLM 5 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 5 | Llama 4 Scout | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 0.5 | Only Llama 4 Scout measured |
| Design Arena: agents building full-stack apps | 1123 | Not measured | Only GLM 5 measured |
| Design Arena: agents building mobile apps | 1136 | Not measured | Only GLM 5 measured |
| Design Arena: agents building Android apps | 1147 | Not measured | Only GLM 5 measured |
| Design Arena: agents building Godot games | 1144 | Not measured | Only GLM 5 measured |
| Design Arena: HTML slides | 1098 | Not measured | Only GLM 5 measured |
| τ²-Bench Telecom | 98.2% | 15.5% | GLM 5 |
| Vending-Bench 2 | $4,432 | Not measured | Only GLM 5 measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 28.1% | Only Llama 4 Scout measured |
| Neither model has a result on the other 32 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index100
| Benchmark | GLM 5 | Llama 4 Scout | Leader |
|---|---|---|---|
| GPQA Diamond | 83.3% | 47.0% | GLM 5 |
| MMMU Pro (Vals AI) | Not measured | 58.8% | Only Llama 4 Scout measured |
| GPQA Diamond (Artificial Analysis run) | 82.0% | 58.7% | GLM 5 |
| Humanity's Last Exam (Artificial Analysis run) | 29.3% | 3.8% | GLM 5 |
| GPQA Diamond (Epoch AI run) | 87.8% | 51.8% | GLM 5 |
| ARC-AGI | 44.7% | 0.5% | GLM 5 |
| ARC-AGI-2 | 4.9% | 0.0% | GLM 5 |
| SimpleBench | 53.2% | Not measured | Only GLM 5 measured |
| LMArena Text: hard prompts | 1452 | 1266 | GLM 5 |
| LMArena Text: expert | 1454 | 1235 | GLM 5 |
| LMArena Text: hard prompts in English | 1460 | 1293 | GLM 5 |
| LMArena Vision: diagrams | Not measured | 1125 | Only Llama 4 Scout measured |
| LMArena Vision: homework | Not measured | 1148 | Only Llama 4 Scout measured |
| Kagi LLM Benchmark | 75.0% | 36.9% | GLM 5 |
| Neither model has a result on the other 8 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 5 | Llama 4 Scout | Leader |
|---|---|---|---|
| AIME | 91.7% | 19.0% | GLM 5 |
| MATH 500 (Vals AI) | Not measured | 79.2% | Only Llama 4 Scout measured |
| MGSM (Vals AI) | Not measured | 88.0% | Only Llama 4 Scout measured |
| AA Math Index | Not measured | 14.0 | Only Llama 4 Scout measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 14.0% | Only Llama 4 Scout measured |
| OTIS Mock AIME | 80.0% | 7.8% | GLM 5 |
| MATH level 5 | Not measured | 62.3% | Only Llama 4 Scout measured |
| LMArena Text: math | 1440 | 1287 | GLM 5 |
| LMArena Text: mathematical work | 1451 | 1286 | GLM 5 |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index90
| Benchmark | GLM 5 | Llama 4 Scout | Leader |
|---|---|---|---|
| MMLU Pro | 86.0% | 69.6% | GLM 5 |
| LegalBench (Vals AI) | 84.1% | 72.0% | GLM 5 |
| CaseLaw (Vals AI) | 52.5% | Not measured | Only GLM 5 measured |
| CorpFin (Vals AI) | 62.9% | 46.8% | GLM 5 |
| TaxEval (Vals AI) | 70.0% | 55.2% | GLM 5 |
| MortgageTax (Vals AI) | Not measured | 57.8% | Only Llama 4 Scout measured |
| MedQA (Vals AI) | 94.3% | 50.9% | GLM 5 |
| MedCode (Vals AI) | Not measured | 23.3% | Only Llama 4 Scout measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 75.2% | Only Llama 4 Scout measured |
| LMArena Text: science | 1460 | 1278 | GLM 5 |
| LMArena Text: medicine and healthcare | 1455 | 1275 | GLM 5 |
| LMArena Text: legal and government | 1453 | 1285 | GLM 5 |
| LMArena Text: business and finance | 1430 | 1257 | GLM 5 |
| Neither model has a result on the other 5 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 5 | Llama 4 Scout | Leader |
|---|---|---|---|
| Design Arena: websites | 1247 | 750 | GLM 5 |
| Design Arena: UI components | 1227 | 765 | GLM 5 |
| Design Arena: game dev | 1234 | 772 | GLM 5 |
| Design Arena: data viz | 1228 | 896 | GLM 5 |
| Design Arena: 3D | 1228 | Not measured | Only GLM 5 measured |
| Design Arena: all code categories | 1246 | 789 | GLM 5 |
| Design Arena: SVG | 1168 | Not measured | Only GLM 5 measured |
| Design Arena: ASCII art | 1147 | Not measured | Only GLM 5 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index190
| Benchmark | GLM 5 | Llama 4 Scout | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 34.8% | Only Llama 4 Scout measured |
| MedScribe (Vals AI) | Not measured | 50.6% | Only Llama 4 Scout measured |
| IFBench | 72.3% | 39.5% | GLM 5 |
| Long context reasoning (AA-LCR) | 75.7% | 27.7% | GLM 5 |
| DTBench | Not measured | 57.9% | Only Llama 4 Scout measured |
| LMCA | Not measured | 12.0% | Only Llama 4 Scout measured |
| Fiction.LiveBench | Not measured | 36.0% | Only Llama 4 Scout measured |
| CL-bench | 18.7% | Not measured | Only GLM 5 measured |
| LMArena Text | 1446 | 1279 | GLM 5 |
| LMArena Text: instruction following | 1428 | 1248 | GLM 5 |
| LMArena Text: creative writing | 1438 | 1249 | GLM 5 |
| LMArena Vision | Not measured | 1118 | Only Llama 4 Scout measured |
| LMArena Text: English | 1458 | 1304 | GLM 5 |
| LMArena Text: non-English | 1430 | 1252 | GLM 5 |
| LMArena Text: Chinese | 1511 | 1255 | GLM 5 |
| LMArena Text: German | 1447 | 1273 | GLM 5 |
| LMArena Text: Japanese | 1416 | 1206 | GLM 5 |
| LMArena Text: Korean | 1423 | 1207 | GLM 5 |
| LMArena Text: French | 1456 | 1283 | GLM 5 |
| LMArena Text: Spanish | 1454 | 1278 | GLM 5 |
| LMArena Text: Russian | 1436 | 1263 | GLM 5 |
| LMArena Text: Polish | 1434 | 1265 | GLM 5 |
| LMArena Text: multi-turn | 1456 | 1280 | GLM 5 |
| LMArena Text: longer queries | 1446 | 1265 | GLM 5 |
| LMArena Text: writing and language | 1437 | 1269 | GLM 5 |
| LMArena Text: entertainment and media | 1426 | 1242 | GLM 5 |
| LMArena Vision: English | Not measured | 1143 | Only Llama 4 Scout measured |
| LMArena Vision: Chinese | Not measured | 1106 | Only Llama 4 Scout measured |
| LMArena Vision: reading text | Not measured | 1117 | Only Llama 4 Scout measured |
| LMArena Vision: humor | Not measured | 1102 | Only Llama 4 Scout measured |
| LMArena Vision: creative writing | Not measured | 1126 | Only Llama 4 Scout measured |
| Neither model has a result on the other 15 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 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 | Z.ai GLM 5 | Meta Llama 4 Scout |
|---|---|---|
| Maker | Z.ai | Meta Llama |
| Weights | Open weights | Open weights |
| Released | February 11, 2026 | April 5, 2025 |
| Context window | 205K | 1.31M |
| Input $/Mtok | $1.00 | $0.175 |
| Output $/Mtok | $3.20 | $0.50 |
| Coding index | 41.7 | Not benchmarked |
| Independent measurements behind it | 3 | 0 |
| Cost per solved task | $0.63 | $1.276 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5 better than Llama 4 Scout for coding?
Only GLM 5 has a coding score, 41.7 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 6.4x cheaper ($0.50 vs $3.20 per million) and a larger context window (1.31M against 205K tokens).
Which is cheaper, GLM 5 or Llama 4 Scout?
GLM 5 costs $1.00 per million input tokens and $3.20 per million output tokens; Llama 4 Scout costs $0.175 and $0.50. Per solved SWE-bench Verified task, GLM 5 costs $0.63 and Llama 4 Scout $1.276.
Which has the bigger context window, GLM 5 or Llama 4 Scout?
Llama 4 Scout, with 1.31M tokens against 205K.
How many benchmarks is the GLM 5 vs Llama 4 Scout comparison based on?
55 benchmarks have a result for both models as of October 22, 2026. GLM 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.