GLM 5 vs GLM 5.3 Flash
GLM 5.3 Flash leads GLM 5 on coding, 57.7 to 41.7 on the Vibe Coding Index, and GLM 5.3 Flash costs 6.4x less per output token ($0.50 vs $3.20 per million). Updated October 21, 2026; 56 benchmarks have a result for both.
GLM 5 or GLM 5.3 Flash: which to pick
Pick GLM 5.3 Flash for coding: it scores 57.7 on the Vibe Coding Index against 41.7 for GLM 5.
Pick GLM 5 for work measured by LiveCodeBench (Vals AI) or ALE-Bench, where it leads.
- Shared results
- 56
- GLM 5 only
- 20
- GLM 5.3 Flash only
- 46
- 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
GLM 5.3 Flash
GLM 5.3 Flash scores 57.7 against 41.7.
3 and 6 benchmarks behind the scores
Intelligence
AA Intelligence Index
GLM 5.3 Flash
GLM 5.3 Flash leads, 41.8 against 27.9.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GLM 5.3 Flash has a AA Agentic Index result, 50.9.
Directional only
Reasoning
GPQA Diamond
GLM 5.3 Flash
GLM 5.3 Flash leads, 86.4% against 83.3%.
Same board, both measured
Math
AIME
No call
Only GLM 5 has a AIME result, 91.7%.
Directional only
Knowledge
MMLU Pro
GLM 5.3 Flash
GLM 5.3 Flash leads, 86.1% against 86.0%.
Same board, both measured
Web design
Design Arena: websites
GLM 5.3 Flash
GLM 5.3 Flash leads, 1272 against 1247.
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 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.3 Flash
GLM 5 $0.0026, GLM 5.3 Flash $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.3 Flash
GLM 5 $0.63, GLM 5.3 Flash $0.02.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5 41.7 from 3 benchmarks, GLM 5.3 Flash 57.7 from 6 benchmarks.
- Widest category
- Reasoning, 17.0 points apart on average over 9 shared benchmarks.
- Widest single gap
- ARC-AGI-2: GLM 5 4.9% against GLM 5.3 Flash 65.8%, 61.0 points apart on a 0 to 100 scale.
- Output price
- GLM 5 $3.20 against GLM 5.3 Flash $0.50 per million output tokens, 6.4x apart.
- Context window
- GLM 5 takes 205K tokens, GLM 5.3 Flash 1.05M.
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 | GLM 5.3 Flash | Ties |
|---|---|---|---|---|
| Coding | 2 | 7 | ||
| Intelligence | 0 | 2 | ||
| Reasoning | 0 | 9 | ||
| Math | 0 | 3 | ||
| Knowledge | 1 | 6 | ||
| Web design | 0 | 8 | ||
| General | 0 | 18 | ||
| All shared boards | 3 | 53 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index27
| Benchmark | GLM 5 | GLM 5.3 Flash | Leader |
|---|---|---|---|
| SWE-bench Verified | 72.8% | Not measured | Only GLM 5 measured |
| SWE-bench Multilingual | 69.7% | Not measured | Only GLM 5 measured |
| LiveBench coding | Not measured | 80.6% | Only GLM 5.3 Flash measured |
| LMArena WebDev | 1435 | 1607 | GLM 5.3 Flash |
| AA Coding Index | Not measured | 71.5 | Only GLM 5.3 Flash measured |
| LiveCodeBench (Vals AI) | 81.9% | 80.5% | GLM 5 |
| SWE-bench Verified (Vals AI) | 71.4% | 92.0% | GLM 5.3 Flash |
| Terminal-Bench 4 (Vals AI) | Not measured | 37.4% | Only GLM 5.3 Flash measured |
| Vibe Code Bench (Vals AI) | 23.4% | 30.8% | GLM 5.3 Flash |
| Code migration (Vals AI) | Not measured | 20.5% | Only GLM 5.3 Flash measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only GLM 5.3 Flash measured |
| IOI (Vals AI) | Not measured | 52.5% | Only GLM 5.3 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 62.9% | Only GLM 5.3 Flash measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 16.0% | Only GLM 5.3 Flash measured |
| SkillsBench (Vals AI) | Not measured | 40.2% | Only GLM 5.3 Flash measured |
| CyberBench (Vals AI) | Not measured | 67.9% | Only GLM 5.3 Flash measured |
| SciCode | Not measured | 51.6% | Only GLM 5.3 Flash measured |
| Terminal-Bench Hard (Artificial Analysis run) | 43.2% | Not measured | Only GLM 5 measured |
| Terminal-Bench | 52.4% | Not measured | Only GLM 5 measured |
| FrontierCode | Not measured | 31.8% | Only GLM 5.3 Flash measured |
| FrontierSWE | Not measured | 18.1% | Only GLM 5.3 Flash measured |
| WeirdML | 48.2% | Not measured | Only GLM 5 measured |
| ALE-Bench | 765.6 | 303.6 | GLM 5 |
| LMArena Text: coding | 1461 | 1507 | GLM 5.3 Flash |
| LMArena Text: software and IT | 1460 | 1500 | GLM 5.3 Flash |
| LMArena WebDev: React | 1424 | 1624 | GLM 5.3 Flash |
| LMArena WebDev: HTML | 1450 | 1577 | GLM 5.3 Flash |
| LMArena WebDev: from an image | Not measured | 1578 | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 11 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 5 | GLM 5.3 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 27.9 | 41.8 | GLM 5.3 Flash |
| Epoch Capabilities Index | 145.8 | 153.1 | GLM 5.3 Flash |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index19 boards, none shared
| Benchmark | GLM 5 | GLM 5.3 Flash | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 50.9 | Only GLM 5.3 Flash 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 | 1146 | Not measured | Only GLM 5 measured |
| Design Arena: agents building Godot games | 1144 | Not measured | Only GLM 5 measured |
| Design Arena: HTML slides | 1148 | Not measured | Only GLM 5 measured |
| LiveBench research | Not measured | 70.1% | Only GLM 5.3 Flash measured |
| LiveBench browser | Not measured | 84.0% | Only GLM 5.3 Flash measured |
| LiveBench long agentic | Not measured | 28.4% | Only GLM 5.3 Flash measured |
| SREBench (Vals AI) | Not measured | 0.8% | Only GLM 5.3 Flash measured |
| Finance Agent (Vals AI) | Not measured | 57.9% | Only GLM 5.3 Flash measured |
| Tax Agent Bench (Vals AI) | Not measured | 31.6% | Only GLM 5.3 Flash measured |
| Legal Research Bench (Vals AI) | Not measured | 45.2% | Only GLM 5.3 Flash measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 10.4% | Only GLM 5.3 Flash measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 55.9% | Only GLM 5.3 Flash measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 2.9% | Only GLM 5.3 Flash measured |
| τ²-Bench Telecom | 98.2% | Not measured | Only GLM 5 measured |
| APEX-Agents | Not measured | 52.8% | Only GLM 5.3 Flash measured |
| Vending-Bench 2 | $4,432 | Not measured | Only GLM 5 measured |
| Neither model has a result on the other 22 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index09
| Benchmark | GLM 5 | GLM 5.3 Flash | Leader |
|---|---|---|---|
| GPQA Diamond | 83.3% | 86.4% | GLM 5.3 Flash |
| LiveBench reasoning | Not measured | 77.6% | Only GLM 5.3 Flash measured |
| MMMU Pro (Vals AI) | Not measured | 86.0% | Only GLM 5.3 Flash measured |
| GPQA Diamond (Artificial Analysis run) | 82.0% | 91.2% | GLM 5.3 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 29.3% | 39.9% | GLM 5.3 Flash |
| GPQA Diamond (Epoch AI run) | 87.8% | 90.2% | GLM 5.3 Flash |
| ARC-AGI | 44.7% | 91.0% | GLM 5.3 Flash |
| ARC-AGI-2 | 4.9% | 65.8% | GLM 5.3 Flash |
| SimpleBench | 53.2% | Not measured | Only GLM 5 measured |
| Chess puzzles | Not measured | 21.0% | Only GLM 5.3 Flash measured |
| Mystery game puzzles | Not measured | 8.0% | Only GLM 5.3 Flash measured |
| LMArena Text: hard prompts | 1452 | 1490 | GLM 5.3 Flash |
| LMArena Text: expert | 1454 | 1511 | GLM 5.3 Flash |
| LMArena Text: hard prompts in English | 1460 | 1488 | GLM 5.3 Flash |
| LMArena Vision: diagrams | Not measured | 1317 | Only GLM 5.3 Flash measured |
| LMArena Vision: homework | Not measured | 1318 | Only GLM 5.3 Flash measured |
| Kagi LLM Benchmark | 75.0% | Not measured | Only GLM 5 measured |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GLM 5 | GLM 5.3 Flash | Leader |
|---|---|---|---|
| AIME | 91.7% | Not measured | Only GLM 5 measured |
| OTIS Mock AIME | 80.0% | 93.9% | GLM 5.3 Flash |
| FrontierMath, tiers 1 to 3 | Not measured | 55.8% | Only GLM 5.3 Flash measured |
| FrontierMath, tier 4 | Not measured | 17.1% | Only GLM 5.3 Flash measured |
| ProofBench | Not measured | 21.0% | Only GLM 5.3 Flash measured |
| LMArena Text: math | 1440 | 1503 | GLM 5.3 Flash |
| LMArena Text: mathematical work | 1451 | 1497 | GLM 5.3 Flash |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index16
| Benchmark | GLM 5 | GLM 5.3 Flash | Leader |
|---|---|---|---|
| MMLU Pro | 86.0% | 86.1% | GLM 5.3 Flash |
| LegalBench (Vals AI) | 84.1% | 83.9% | GLM 5 |
| CaseLaw (Vals AI) | 52.5% | Not measured | Only GLM 5 measured |
| CorpFin (Vals AI) | 62.9% | Not measured | Only GLM 5 measured |
| TaxEval (Vals AI) | 70.0% | 75.6% | GLM 5.3 Flash |
| MedQA (Vals AI) | 94.3% | Not measured | Only GLM 5 measured |
| LMArena Text: science | 1460 | 1484 | GLM 5.3 Flash |
| LMArena Text: medicine and healthcare | 1455 | 1475 | GLM 5.3 Flash |
| LMArena Text: legal and government | 1453 | 1473 | GLM 5.3 Flash |
| LMArena Text: business and finance | 1430 | 1460 | GLM 5.3 Flash |
| Neither model has a result on the other 8 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index08
| Benchmark | GLM 5 | GLM 5.3 Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1247 | 1272 | GLM 5.3 Flash |
| Design Arena: UI components | 1230 | 1316 | GLM 5.3 Flash |
| Design Arena: game dev | 1234 | 1278 | GLM 5.3 Flash |
| Design Arena: data viz | 1228 | 1261 | GLM 5.3 Flash |
| Design Arena: 3D | 1229 | 1316 | GLM 5.3 Flash |
| Design Arena: all code categories | 1246 | 1282 | GLM 5.3 Flash |
| Design Arena: SVG | 1168 | 1284 | GLM 5.3 Flash |
| Design Arena: ASCII art | 1147 | 1268 | GLM 5.3 Flash |
| LiveBench design | Not measured | 77.2% | Only GLM 5.3 Flash measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index018
| Benchmark | GLM 5 | GLM 5.3 Flash | Leader |
|---|---|---|---|
| Vals Index | Not measured | 50.9% | Only GLM 5.3 Flash measured |
| LiveBench data analysis | Not measured | 76.4% | Only GLM 5.3 Flash measured |
| MedScribe (Vals AI) | Not measured | 88.9% | Only GLM 5.3 Flash measured |
| IFBench | 72.3% | Not measured | Only GLM 5 measured |
| Long context reasoning (AA-LCR) | 75.7% | 80.0% | GLM 5.3 Flash |
| CL-bench | 18.7% | Not measured | Only GLM 5 measured |
| Surface Evolver Bench | Not measured | 52.5% | Only GLM 5.3 Flash measured |
| LMArena Text | 1446 | 1471 | GLM 5.3 Flash |
| LMArena Text: instruction following | 1428 | 1478 | GLM 5.3 Flash |
| LMArena Text: creative writing | 1438 | 1441 | GLM 5.3 Flash |
| LMArena Vision | Not measured | 1294 | Only GLM 5.3 Flash measured |
| LMArena Text: English | 1458 | 1474 | GLM 5.3 Flash |
| LMArena Text: non-English | 1430 | 1462 | GLM 5.3 Flash |
| LMArena Text: Chinese | 1511 | 1531 | GLM 5.3 Flash |
| LMArena Text: German | 1447 | 1462 | GLM 5.3 Flash |
| LMArena Text: Japanese | 1416 | 1444 | GLM 5.3 Flash |
| LMArena Text: Korean | 1423 | 1436 | GLM 5.3 Flash |
| LMArena Text: French | 1456 | 1491 | GLM 5.3 Flash |
| LMArena Text: Spanish | 1454 | 1475 | GLM 5.3 Flash |
| LMArena Text: Russian | 1436 | 1465 | GLM 5.3 Flash |
| LMArena Text: Polish | 1434 | 1450 | GLM 5.3 Flash |
| LMArena Text: multi-turn | 1456 | 1469 | GLM 5.3 Flash |
| LMArena Text: longer queries | 1446 | 1481 | GLM 5.3 Flash |
| LMArena Text: writing and language | 1437 | 1455 | GLM 5.3 Flash |
| LMArena Text: entertainment and media | 1426 | 1440 | GLM 5.3 Flash |
| LMArena Vision: English | Not measured | 1295 | Only GLM 5.3 Flash measured |
| LMArena Vision: Chinese | Not measured | 1363 | Only GLM 5.3 Flash measured |
| LMArena Vision: reading text | Not measured | 1299 | Only GLM 5.3 Flash measured |
| LMArena Vision: humor | Not measured | 1279 | Only GLM 5.3 Flash measured |
| LMArena Vision: creative writing | Not measured | 1270 | Only GLM 5.3 Flash 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 | GLM 5 | GLM 5.3 Flash | 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 | Z.ai GLM 5.3 Flash |
|---|---|---|
| Maker | Z.ai | Z.ai |
| Weights | Open weights | Open weights |
| Released | February 11, 2026 | August 26, 2026 |
| Context window | 205K | 1.05M |
| Input $/Mtok | $1.00 | $0.15 |
| Output $/Mtok | $3.20 | $0.50 |
| Coding index | 41.7 | 57.7 |
| Independent measurements behind it | 3 | 6 |
| Cost per solved task | $0.63 | $0.02 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5 better than GLM 5.3 Flash for coding?
Pick GLM 5.3 Flash for coding: it scores 57.7 on the Vibe Coding Index against 41.7 for GLM 5. Pick GLM 5 for work measured by LiveCodeBench (Vals AI) or ALE-Bench, where it leads.
Which is cheaper, GLM 5 or GLM 5.3 Flash?
GLM 5 costs $1.00 per million input tokens and $3.20 per million output tokens; GLM 5.3 Flash costs $0.15 and $0.50. Per solved SWE-bench Verified task, GLM 5 costs $0.63 and GLM 5.3 Flash $0.02.
Which has the bigger context window, GLM 5 or GLM 5.3 Flash?
GLM 5.3 Flash, with 1.05M tokens against 205K.
How many benchmarks is the GLM 5 vs GLM 5.3 Flash comparison based on?
56 benchmarks have a result for both models as of October 21, 2026. GLM 5's coding index rests on 3 independent benchmarks and GLM 5.3 Flash's on 6. Where only one model has a result, the page says so instead of naming a winner.