GLM 5.3 Flash vs Grok 4.5
Updated October 21, 2026. 96 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick Grok 4.5 for coding: it scores 75.5 on the Vibe Coding Index against 71.0 for GLM 5.3 Flash.
Pick GLM 5.3 Flash when output tokens cost 12.0x less, or when a solved task costs $0.02 against $0.62.
- Shared results
- 96
- GLM 5.3 Flash only
- 6
- Grok 4.5 only
- 24
- 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
Grok 4.5
Grok 4.5 scores 75.5 against 71.0.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
GLM 5.3 Flash
GLM 5.3 Flash leads, 41.8 against 38.8.
Same board, both measured
Agentic work
AA Agentic Index
GLM 5.3 Flash
GLM 5.3 Flash leads, 50.9 against 41.2.
Same board, both measured
Reasoning
GPQA Diamond
Grok 4.5
Grok 4.5 leads, 92.9% against 86.4%.
Same board, both measured
Knowledge
MMLU Pro
Grok 4.5
Grok 4.5 leads, 89.2% against 86.1%.
Same board, both measured
Web design
Design Arena: websites
Grok 4.5
Grok 4.5 leads, 1281 against 1272.
Same board, both measured
General
Vals Index
No call
Only GLM 5.3 Flash has a Vals Index result, 50.9%.
Directional only
Long documents
Listed context window
GLM 5.3 Flash
GLM 5.3 Flash takes 1.05M tokens against 500K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.3 Flash
GLM 5.3 Flash $0.0004, Grok 4.5 $0.0050 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.3 Flash
GLM 5.3 Flash $0.02, Grok 4.5 $0.62.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5.3 Flash 71.0 from 2 benchmarks, Grok 4.5 75.5 from 2 benchmarks.
- Widest category
- Coding, 11.1 points apart on average over 21 shared benchmarks.
- Widest single gap
- LiveBench browser: GLM 5.3 Flash 84.0% against Grok 4.5 44.0%, 40.0 points apart on a 0 to 100 scale.
- Output price
- GLM 5.3 Flash $0.50 against Grok 4.5 $6.00 per million output tokens, 12.0x apart.
- Context window
- GLM 5.3 Flash takes 1.05M tokens, Grok 4.5 500K.
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 Flash | Bars | Grok 4.5 | Ties |
|---|---|---|---|---|
| Coding | 9 | 11 | 1 tie | |
| Intelligence | 1 | 1 | ||
| Agentic work | 7 | 2 | 1 tie | |
| Reasoning | 8 | 6 | ||
| Math | 2 | 4 | ||
| Knowledge | 5 | 2 | ||
| Web design | 6 | 3 | ||
| General | 24 | 3 | ||
| All shared boards | 62 | 32 | 2 ties |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index911
| Benchmark | GLM 5.3 Flash | Grok 4.5 | Leader |
|---|---|---|---|
| LiveBench coding | 80.6% | 94.3% | Grok 4.5 |
| LMArena WebDev | 1607 | 1553 | GLM 5.3 Flash |
| AA Coding Index | 71.5 | 72.4 | Grok 4.5 |
| LiveCodeBench (Vals AI) | 80.5% | 87.3% | Grok 4.5 |
| SWE-bench Verified (Vals AI) | 92.0% | 86.6% | GLM 5.3 Flash |
| Terminal-Bench 4 (Vals AI) | 37.4% | 8.6% | GLM 5.3 Flash |
| Vibe Code Bench (Vals AI) | 30.8% | 69.0% | Grok 4.5 |
| Code migration (Vals AI) | 20.5% | 36.6% | Grok 4.5 |
| ProgramBench (Vals AI) | 0.0% | 0.0% | Tie |
| IOI (Vals AI) | 52.5% | 40.6% | GLM 5.3 Flash |
| Terminal-Bench 2.1 (Vals AI) | 62.9% | 67.8% | Grok 4.5 |
| Vibe Code Bench 1-100 (Vals AI) | 16.0% | Not measured | Only GLM 5.3 Flash measured |
| SkillsBench (Vals AI) | 40.2% | 66.0% | Grok 4.5 |
| CyberBench (Vals AI) | 67.9% | 71.0% | Grok 4.5 |
| SciCode | 51.6% | 55.0% | Grok 4.5 |
| FrontierCode | 31.8% | 42.4% | Grok 4.5 |
| FrontierSWE | 18.1% | Not measured | Only GLM 5.3 Flash measured |
| WeirdML | Not measured | 46.4% | Only Grok 4.5 measured |
| ALE-Bench | 303.6 | 1309.3 | Grok 4.5 |
| LMArena Text: coding | 1507 | 1474 | GLM 5.3 Flash |
| LMArena Text: software and IT | 1500 | 1471 | GLM 5.3 Flash |
| LMArena WebDev: React | 1624 | 1556 | GLM 5.3 Flash |
| LMArena WebDev: HTML | 1577 | 1567 | GLM 5.3 Flash |
| LMArena WebDev: from an image | 1578 | 1562 | GLM 5.3 Flash |
| Neither model has a result on the other 14 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GLM 5.3 Flash | Grok 4.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 41.8 | 38.8 | GLM 5.3 Flash |
| Epoch Capabilities Index | 153.1 | 154.1 | Grok 4.5 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index72
| Benchmark | GLM 5.3 Flash | Grok 4.5 | Leader |
|---|---|---|---|
| AA Agentic Index | 50.9 | 41.2 | GLM 5.3 Flash |
| Design Arena: agents building web apps | Not measured | 1217 | Only Grok 4.5 measured |
| Design Arena: agents building full-stack apps | Not measured | 1249 | Only Grok 4.5 measured |
| Design Arena: agents building mobile apps | Not measured | 1221 | Only Grok 4.5 measured |
| Design Arena: agents building Android apps | Not measured | 1242 | Only Grok 4.5 measured |
| Design Arena: agents building Godot games | Not measured | 1229 | Only Grok 4.5 measured |
| Design Arena: agents building games | Not measured | 1235 | Only Grok 4.5 measured |
| Design Arena: HTML slides | Not measured | 1204 | Only Grok 4.5 measured |
| Design Arena: PowerPoint slides | Not measured | 1232 | Only Grok 4.5 measured |
| LiveBench research | 70.1% | 85.9% | Grok 4.5 |
| LiveBench browser | 84.0% | 44.0% | GLM 5.3 Flash |
| LiveBench long agentic | 28.4% | 27.9% | GLM 5.3 Flash |
| SREBench (Vals AI) | 0.8% | 0.8% | Tie |
| Finance Agent (Vals AI) | 57.9% | 48.4% | GLM 5.3 Flash |
| Tax Agent Bench (Vals AI) | 31.6% | 24.9% | GLM 5.3 Flash |
| Legal Research Bench (Vals AI) | 45.2% | 38.0% | GLM 5.3 Flash |
| Harvey's Legal Agent Benchmark (Vals AI) | 10.4% | Not measured | Only GLM 5.3 Flash measured |
| Excel Modeling Benchmark (Vals AI) | 55.9% | 52.9% | GLM 5.3 Flash |
| Terminal-Bench-Science (Vals AI) | 2.9% | Not measured | Only GLM 5.3 Flash measured |
| Time Horizon Index (Vals AI) | Not measured | 6.3% | Only Grok 4.5 measured |
| APEX-Agents | 52.8% | 56.2% | Grok 4.5 |
| PostTrainBench | Not measured | 23.4% | Only Grok 4.5 measured |
| Vending-Bench 2 | Not measured | $3,887 | Only Grok 4.5 measured |
| LMArena Search | Not measured | 1216 | Only Grok 4.5 measured |
| Neither model has a result on the other 15 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index86
| Benchmark | GLM 5.3 Flash | Grok 4.5 | Leader |
|---|---|---|---|
| GPQA Diamond | 86.4% | 92.9% | Grok 4.5 |
| LiveBench reasoning | 77.6% | 87.2% | Grok 4.5 |
| MMMU Pro (Vals AI) | 86.0% | 61.8% | GLM 5.3 Flash |
| GPQA Diamond (Artificial Analysis run) | 91.2% | 93.1% | Grok 4.5 |
| Humanity's Last Exam (Artificial Analysis run) | 39.9% | 42.7% | Grok 4.5 |
| GPQA Diamond (Epoch AI run) | 90.2% | 93.4% | Grok 4.5 |
| ARC-AGI | 91.0% | 87.2% | GLM 5.3 Flash |
| ARC-AGI-2 | 65.8% | 52.6% | GLM 5.3 Flash |
| SimpleBench | Not measured | 70.0% | Only Grok 4.5 measured |
| Chess puzzles | 21.0% | 35.0% | Grok 4.5 |
| Mystery game puzzles | 8.0% | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: hard prompts | 1490 | 1462 | GLM 5.3 Flash |
| LMArena Text: expert | 1511 | 1465 | GLM 5.3 Flash |
| LMArena Text: hard prompts in English | 1488 | 1464 | GLM 5.3 Flash |
| LMArena Vision: diagrams | 1317 | 1304 | GLM 5.3 Flash |
| LMArena Vision: homework | 1318 | 1295 | GLM 5.3 Flash |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index24
| Benchmark | GLM 5.3 Flash | Grok 4.5 | Leader |
|---|---|---|---|
| OTIS Mock AIME | 93.9% | 97.8% | Grok 4.5 |
| FrontierMath, tiers 1 to 3 | 55.8% | 57.2% | Grok 4.5 |
| FrontierMath, tier 4 | 17.1% | 24.4% | Grok 4.5 |
| ProofBench | 21.0% | 31.0% | Grok 4.5 |
| LMArena Text: math | 1503 | 1460 | GLM 5.3 Flash |
| LMArena Text: mathematical work | 1497 | 1482 | GLM 5.3 Flash |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index52
| Benchmark | GLM 5.3 Flash | Grok 4.5 | Leader |
|---|---|---|---|
| MMLU Pro | 86.1% | 89.2% | Grok 4.5 |
| LegalBench (Vals AI) | 83.9% | 86.0% | Grok 4.5 |
| CorpFin (Vals AI) | Not measured | 67.4% | Only Grok 4.5 measured |
| TaxEval (Vals AI) | 75.6% | 71.7% | GLM 5.3 Flash |
| MortgageTax (Vals AI) | Not measured | 61.8% | Only Grok 4.5 measured |
| MedCode (Vals AI) | Not measured | 43.3% | Only Grok 4.5 measured |
| SimpleQA Verified | Not measured | 48.3% | Only Grok 4.5 measured |
| LMArena Text: science | 1484 | 1464 | GLM 5.3 Flash |
| LMArena Text: medicine and healthcare | 1475 | 1440 | GLM 5.3 Flash |
| LMArena Text: legal and government | 1473 | 1463 | GLM 5.3 Flash |
| LMArena Text: business and finance | 1460 | 1441 | GLM 5.3 Flash |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index63
| Benchmark | GLM 5.3 Flash | Grok 4.5 | Leader |
|---|---|---|---|
| Design Arena: websites | 1272 | 1281 | Grok 4.5 |
| Design Arena: UI components | 1316 | 1279 | GLM 5.3 Flash |
| Design Arena: game dev | 1279 | 1263 | GLM 5.3 Flash |
| Design Arena: data viz | 1262 | 1267 | Grok 4.5 |
| Design Arena: 3D | 1316 | 1257 | GLM 5.3 Flash |
| Design Arena: all code categories | 1282 | 1276 | GLM 5.3 Flash |
| Design Arena: SVG | 1284 | 1215 | GLM 5.3 Flash |
| Design Arena: ASCII art | 1269 | 1257 | GLM 5.3 Flash |
| LiveBench design | 77.2% | 78.1% | Grok 4.5 |
GeneralLike-for-likePublic boards, shown as published, kept out of the index243
| Benchmark | GLM 5.3 Flash | Grok 4.5 | Leader |
|---|---|---|---|
| Vals Index | 50.9% | Not measured | Only GLM 5.3 Flash measured |
| LiveBench data analysis | 76.4% | 73.0% | GLM 5.3 Flash |
| SAGE (Vals AI) | Not measured | 35.0% | Only Grok 4.5 measured |
| MedScribe (Vals AI) | 88.9% | 86.9% | GLM 5.3 Flash |
| Vals Multimodal Index | Not measured | 63.4% | Only Grok 4.5 measured |
| Long context reasoning (AA-LCR) | 80.0% | 79.3% | GLM 5.3 Flash |
| DTBench | Not measured | 96.5% | Only Grok 4.5 measured |
| LMCA | Not measured | 45.2% | Only Grok 4.5 measured |
| Surface Evolver Bench | 52.5% | 74.4% | Grok 4.5 |
| Furniture assembly | Not measured | 22.5% | Only Grok 4.5 measured |
| LMArena Text | 1471 | 1448 | GLM 5.3 Flash |
| LMArena Text: instruction following | 1478 | 1445 | GLM 5.3 Flash |
| LMArena Text: creative writing | 1441 | 1442 | Grok 4.5 |
| LMArena Vision | 1294 | 1287 | GLM 5.3 Flash |
| LMArena Text: English | 1474 | 1453 | GLM 5.3 Flash |
| LMArena Text: non-English | 1462 | 1440 | GLM 5.3 Flash |
| LMArena Text: Chinese | 1531 | 1495 | GLM 5.3 Flash |
| LMArena Text: German | 1462 | 1448 | GLM 5.3 Flash |
| LMArena Text: Japanese | 1444 | 1430 | GLM 5.3 Flash |
| LMArena Text: Korean | 1436 | 1406 | GLM 5.3 Flash |
| LMArena Text: French | 1491 | 1457 | GLM 5.3 Flash |
| LMArena Text: Spanish | 1475 | 1451 | GLM 5.3 Flash |
| LMArena Text: Russian | 1465 | 1449 | GLM 5.3 Flash |
| LMArena Text: Polish | 1450 | 1432 | GLM 5.3 Flash |
| LMArena Text: multi-turn | 1469 | 1456 | GLM 5.3 Flash |
| LMArena Text: longer queries | 1481 | 1462 | GLM 5.3 Flash |
| LMArena Text: writing and language | 1455 | 1445 | GLM 5.3 Flash |
| LMArena Text: entertainment and media | 1440 | 1434 | GLM 5.3 Flash |
| LMArena Vision: English | 1295 | 1285 | GLM 5.3 Flash |
| LMArena Vision: Chinese | 1363 | 1345 | GLM 5.3 Flash |
| LMArena Vision: reading text | 1299 | 1293 | GLM 5.3 Flash |
| LMArena Vision: humor | 1279 | 1276 | GLM 5.3 Flash |
| LMArena Vision: creative writing | 1270 | 1293 | Grok 4.5 |
| LMArena Document | Not measured | 1452 | Only Grok 4.5 measured |
| Neither model has a result on the other 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.3 Flash | Grok 4.5 | 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
GLM 5.3 Flash costs less
Repository review
50K input + 3K output tokens
GLM 5.3 Flash costs less
Agent loop
200K input + 10K output tokens
GLM 5.3 Flash 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 Flash | SpaceXAI Grok 4.5 |
|---|---|---|
| Maker | Z.ai | xAI |
| Weights | Open weights | No public weights listed |
| Released | August 26, 2026 | July 8, 2026 |
| Context window | 1.05M | 500K |
| Input $/Mtok | $0.15 | $2.00 |
| Output $/Mtok | $0.50 | $6.00 |
| Coding index | 71.0 | 75.5 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | $0.02 | $0.62 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 Flash better than Grok 4.5 for coding?
Pick Grok 4.5 for coding: it scores 75.5 on the Vibe Coding Index against 71.0 for GLM 5.3 Flash. Pick GLM 5.3 Flash when output tokens cost 12.0x less, or when a solved task costs $0.02 against $0.62.
Which is cheaper, GLM 5.3 Flash or Grok 4.5?
GLM 5.3 Flash costs $0.15 per million input tokens and $0.50 per million output tokens; Grok 4.5 costs $2.00 and $6.00. Per solved SWE-bench Verified task, GLM 5.3 Flash costs $0.02 and Grok 4.5 $0.62.
Which has the bigger context window, GLM 5.3 Flash or Grok 4.5?
GLM 5.3 Flash, with 1.05M tokens against 500K.
How many benchmarks is the GLM 5.3 Flash vs Grok 4.5 comparison based on?
96 benchmarks have a result for both models as of October 21, 2026. GLM 5.3 Flash's coding index rests on 2 independent benchmarks and Grok 4.5's on 2. Where only one model has a result, the page says so instead of naming a winner.