GLM 5.3 vs Llama 4 Scout
Updated October 21, 2026. 52 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GLM 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 9.1 for Llama 4 Scout.
Pick Llama 4 Scout when output tokens cost 4.7x less, or when the context window is larger, 1.31M against 1.05M tokens.
- Shared results
- 52
- GLM 5.3 only
- 48
- Llama 4 Scout only
- 28
- Like-for-like categories
- 8 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
GLM 5.3
GLM 5.3 scores 73.1 against 9.1.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
GLM 5.3
GLM 5.3 leads, 44.8 against 6.5.
Same board, both measured
Agentic work
AA Agentic Index
GLM 5.3
GLM 5.3 leads, 53.1 against 0.5.
Same board, both measured
Reasoning
GPQA Diamond
GLM 5.3
GLM 5.3 leads, 88.1% against 47.0%.
Same board, both measured
Math
AIME
No call
Only Llama 4 Scout has a AIME result, 19.0%.
Directional only
Knowledge
MMLU Pro
GLM 5.3
GLM 5.3 leads, 86.8% against 69.6%.
Same board, both measured
Web design
Design Arena: websites
GLM 5.3
GLM 5.3 leads, 1295 against 750.
Same board, both measured
General
Vals Index
No call
Only GLM 5.3 has a Vals Index result, 59.7%.
Directional only
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
GLM 5.3 $0.0008, Llama 4 Scout $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.3
GLM 5.3 $0.36, Llama 4 Scout $1.276.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5.3 73.1 from 2 benchmarks, Llama 4 Scout 9.1 from 1 benchmark.
- Widest category
- Web design, 62.7 points apart on average over 5 shared benchmarks.
- Widest single gap
- OTIS Mock AIME: GLM 5.3 91.1% against Llama 4 Scout 7.8%, 83.3 points apart on a 0 to 100 scale.
- Output price
- GLM 5.3 $1.40 against Llama 4 Scout $0.30 per million output tokens, 4.7x apart.
- Context window
- GLM 5.3 takes 1.05M 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 5.3 | Bars | Llama 4 Scout | Ties |
|---|---|---|---|---|
| Coding | 5 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 7 | 0 | ||
| Math | 3 | 0 | ||
| Knowledge | 8 | 0 | ||
| Web design | 5 | 0 | ||
| General | 21 | 0 | ||
| All shared boards | 52 | 0 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index50
| Benchmark | GLM 5.3 | Llama 4 Scout | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 9.1% | Only Llama 4 Scout measured |
| LiveBench coding | 82.8% | Not measured | Only GLM 5.3 measured |
| LMArena WebDev | 1621 | Not measured | Only GLM 5.3 measured |
| AA Coding Index | 74.8 | 8.2 | GLM 5.3 |
| LiveCodeBench (Vals AI) | 80.5% | 38.5% | GLM 5.3 |
| SWE-bench Verified (Vals AI) | 95.4% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench 4 (Vals AI) | 38.9% | Not measured | Only GLM 5.3 measured |
| Vibe Code Bench (Vals AI) | 78.1% | Not measured | Only GLM 5.3 measured |
| Code migration (Vals AI) | 44.2% | Not measured | Only GLM 5.3 measured |
| ProgramBench (Vals AI) | 1.5% | Not measured | Only GLM 5.3 measured |
| IOI (Vals AI) | 68.4% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench 2.1 (Vals AI) | 71.5% | Not measured | Only GLM 5.3 measured |
| Vibe Code Bench 1-100 (Vals AI) | 20.0% | Not measured | Only GLM 5.3 measured |
| SkillsBench (Vals AI) | 47.5% | Not measured | Only GLM 5.3 measured |
| CyberBench (Vals AI) | 72.1% | Not measured | Only GLM 5.3 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 29.9% | Only Llama 4 Scout measured |
| SciCode | 59.0% | 21.3% | GLM 5.3 |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 1.5% | Only Llama 4 Scout measured |
| FrontierCode | 40.1% | Not measured | Only GLM 5.3 measured |
| FrontierSWE | 30.2% | Not measured | Only GLM 5.3 measured |
| WeirdML | 75.4% | Not measured | Only GLM 5.3 measured |
| ALE-Bench | 1317.4 | Not measured | Only GLM 5.3 measured |
| LMArena Text: coding | 1497 | 1286 | GLM 5.3 |
| LMArena Text: software and IT | 1493 | 1286 | GLM 5.3 |
| LMArena WebDev: React | 1641 | Not measured | Only GLM 5.3 measured |
| LMArena WebDev: HTML | 1595 | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 12 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5.3 | Llama 4 Scout | Leader |
|---|---|---|---|
| AA Intelligence Index | 44.8 | 6.5 | GLM 5.3 |
| Epoch Capabilities Index | 156.0 | 129.6 | GLM 5.3 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 5.3 | Llama 4 Scout | Leader |
|---|---|---|---|
| AA Agentic Index | 53.1 | 0.5 | GLM 5.3 |
| Design Arena: agents building mobile apps | 1194 | Not measured | Only GLM 5.3 measured |
| Design Arena: HTML slides | 1189 | Not measured | Only GLM 5.3 measured |
| Design Arena: PowerPoint slides | 1255 | Not measured | Only GLM 5.3 measured |
| LiveBench research | 73.2% | Not measured | Only GLM 5.3 measured |
| LiveBench browser | 0.0% | Not measured | Only GLM 5.3 measured |
| LiveBench long agentic | 35.9% | Not measured | Only GLM 5.3 measured |
| SREBench (Vals AI) | 9.2% | Not measured | Only GLM 5.3 measured |
| Finance Agent (Vals AI) | 55.8% | Not measured | Only GLM 5.3 measured |
| Tax Agent Bench (Vals AI) | 39.7% | Not measured | Only GLM 5.3 measured |
| Legal Research Bench (Vals AI) | 49.0% | Not measured | Only GLM 5.3 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 14.6% | Not measured | Only GLM 5.3 measured |
| Excel Modeling Benchmark (Vals AI) | 56.3% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench-Science (Vals AI) | 5.7% | Not measured | Only GLM 5.3 measured |
| τ²-Bench Telecom | Not measured | 15.5% | Only Llama 4 Scout measured |
| APEX-Agents | 56.6% | Not measured | Only GLM 5.3 measured |
| Vending-Bench 2 | $8,164 | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 22 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | GLM 5.3 | Llama 4 Scout | Leader |
|---|---|---|---|
| GPQA Diamond | 88.1% | 47.0% | GLM 5.3 |
| LiveBench reasoning | 85.8% | Not measured | Only GLM 5.3 measured |
| MysteryMechanism (Vals AI) | 23.0% | Not measured | Only GLM 5.3 measured |
| MMMU Pro (Vals AI) | Not measured | 58.8% | Only Llama 4 Scout measured |
| GPQA Diamond (Artificial Analysis run) | 91.7% | 58.7% | GLM 5.3 |
| Humanity's Last Exam (Artificial Analysis run) | 42.3% | 3.8% | GLM 5.3 |
| GPQA Diamond (Epoch AI run) | 90.9% | 51.8% | GLM 5.3 |
| ARC-AGI | Not measured | 0.5% | Only Llama 4 Scout measured |
| ARC-AGI-2 | Not measured | 0.0% | Only Llama 4 Scout measured |
| Chess puzzles | 37.0% | Not measured | Only GLM 5.3 measured |
| Mystery game puzzles | 33.0% | Not measured | Only GLM 5.3 measured |
| LMArena Text: hard prompts | 1490 | 1266 | GLM 5.3 |
| LMArena Text: expert | 1514 | 1235 | GLM 5.3 |
| LMArena Text: hard prompts in English | 1496 | 1293 | GLM 5.3 |
| LMArena Vision: diagrams | Not measured | 1125 | Only Llama 4 Scout measured |
| LMArena Vision: homework | Not measured | 1148 | Only Llama 4 Scout measured |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GLM 5.3 | Llama 4 Scout | Leader |
|---|---|---|---|
| AIME | Not measured | 19.0% | Only Llama 4 Scout measured |
| 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 | 91.1% | 7.8% | GLM 5.3 |
| FrontierMath, tiers 1 to 3 | 68.8% | Not measured | Only GLM 5.3 measured |
| FrontierMath, tier 4 | 29.3% | Not measured | Only GLM 5.3 measured |
| ProofBench | 49.0% | Not measured | Only GLM 5.3 measured |
| MATH level 5 | Not measured | 62.3% | Only Llama 4 Scout measured |
| LMArena Text: math | 1495 | 1287 | GLM 5.3 |
| LMArena Text: mathematical work | 1497 | 1286 | GLM 5.3 |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | GLM 5.3 | Llama 4 Scout | Leader |
|---|---|---|---|
| MMLU Pro | 86.8% | 69.6% | GLM 5.3 |
| LegalBench (Vals AI) | 84.8% | 72.0% | GLM 5.3 |
| CorpFin (Vals AI) | Not measured | 46.8% | Only Llama 4 Scout measured |
| TaxEval (Vals AI) | 72.4% | 55.2% | GLM 5.3 |
| MortgageTax (Vals AI) | Not measured | 57.8% | Only Llama 4 Scout measured |
| MedQA (Vals AI) | Not measured | 50.9% | Only Llama 4 Scout measured |
| MedCode (Vals AI) | 42.9% | 23.3% | GLM 5.3 |
| MMLU Pro (Artificial Analysis run) | Not measured | 75.2% | Only Llama 4 Scout measured |
| SimpleQA Verified | 41.0% | Not measured | Only GLM 5.3 measured |
| LMArena Text: science | 1498 | 1278 | GLM 5.3 |
| LMArena Text: medicine and healthcare | 1481 | 1275 | GLM 5.3 |
| LMArena Text: legal and government | 1473 | 1285 | GLM 5.3 |
| LMArena Text: business and finance | 1459 | 1257 | GLM 5.3 |
| Neither model has a result on the other 4 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 5.3 | Llama 4 Scout | Leader |
|---|---|---|---|
| Design Arena: websites | 1295 | 750 | GLM 5.3 |
| Design Arena: UI components | 1314 | 767 | GLM 5.3 |
| Design Arena: game dev | 1311 | 773 | GLM 5.3 |
| Design Arena: data viz | 1260 | 896 | GLM 5.3 |
| Design Arena: 3D | 1332 | Not measured | Only GLM 5.3 measured |
| Design Arena: all code categories | 1304 | 790 | GLM 5.3 |
| Design Arena: SVG | 1292 | Not measured | Only GLM 5.3 measured |
| Design Arena: ASCII art | 1213 | Not measured | Only GLM 5.3 measured |
| LiveBench design | 77.3% | Not measured | Only GLM 5.3 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index210
| Benchmark | GLM 5.3 | Llama 4 Scout | Leader |
|---|---|---|---|
| Vals Index | 59.7% | Not measured | Only GLM 5.3 measured |
| LiveBench data analysis | 70.2% | Not measured | Only GLM 5.3 measured |
| Public Benefits Bench (Vals AI) | 68.5% | Not measured | Only GLM 5.3 measured |
| SAGE (Vals AI) | Not measured | 34.8% | Only Llama 4 Scout measured |
| MedScribe (Vals AI) | 88.8% | 50.6% | GLM 5.3 |
| IFBench | Not measured | 39.5% | Only Llama 4 Scout measured |
| Long context reasoning (AA-LCR) | 79.7% | 27.7% | GLM 5.3 |
| DTBench | 87.7% | 57.9% | GLM 5.3 |
| LMCA | 55.5% | 12.0% | GLM 5.3 |
| Fiction.LiveBench | Not measured | 36.0% | Only Llama 4 Scout measured |
| LMArena Text | 1474 | 1279 | GLM 5.3 |
| LMArena Text: instruction following | 1479 | 1248 | GLM 5.3 |
| LMArena Text: creative writing | 1458 | 1249 | GLM 5.3 |
| LMArena Vision | Not measured | 1118 | Only Llama 4 Scout measured |
| LMArena Text: English | 1482 | 1304 | GLM 5.3 |
| LMArena Text: non-English | 1459 | 1252 | GLM 5.3 |
| LMArena Text: Chinese | 1532 | 1255 | GLM 5.3 |
| LMArena Text: German | 1488 | 1273 | GLM 5.3 |
| LMArena Text: Japanese | 1460 | 1206 | GLM 5.3 |
| LMArena Text: Korean | 1460 | 1207 | GLM 5.3 |
| LMArena Text: French | 1503 | 1283 | GLM 5.3 |
| LMArena Text: Spanish | 1462 | 1278 | GLM 5.3 |
| LMArena Text: Russian | 1469 | 1263 | GLM 5.3 |
| LMArena Text: Polish | 1466 | 1265 | GLM 5.3 |
| LMArena Text: multi-turn | 1472 | 1280 | GLM 5.3 |
| LMArena Text: longer queries | 1483 | 1265 | GLM 5.3 |
| LMArena Text: writing and language | 1469 | 1269 | GLM 5.3 |
| LMArena Text: entertainment and media | 1447 | 1242 | GLM 5.3 |
| 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 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.3 | 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 5.3 | Meta Llama 4 Scout |
|---|---|---|
| Maker | Z.ai | Meta Llama |
| Weights | Open weights | Open weights |
| Released | August 18, 2026 | April 5, 2025 |
| Context window | 1.05M | 1.31M |
| Input $/Mtok | $0.079 | $0.10 |
| Output $/Mtok | $1.40 | $0.30 |
| Coding index | 73.1 | 9.1 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | $0.36 | $1.276 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 better than Llama 4 Scout for coding?
Pick GLM 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 9.1 for Llama 4 Scout. Pick Llama 4 Scout when output tokens cost 4.7x less, or when the context window is larger, 1.31M against 1.05M tokens.
Which is cheaper, GLM 5.3 or Llama 4 Scout?
GLM 5.3 costs $0.079 per million input tokens and $1.40 per million output tokens; Llama 4 Scout costs $0.10 and $0.30. Per solved SWE-bench Verified task, GLM 5.3 costs $0.36 and Llama 4 Scout $1.276.
Which has the bigger context window, GLM 5.3 or Llama 4 Scout?
Llama 4 Scout, with 1.31M tokens against 1.05M.
How many benchmarks is the GLM 5.3 vs Llama 4 Scout comparison based on?
52 benchmarks have a result for both models as of October 21, 2026. GLM 5.3's coding index rests on 2 independent benchmarks and Llama 4 Scout's on 1. Where only one model has a result, the page says so instead of naming a winner.