GLM 5.3 Flash vs Qwen3.5-27B
Updated October 21, 2026. 44 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 Flash for coding: it scores 71.0 on the Vibe Coding Index against 22.6 for Qwen3.5-27B.
Pick Qwen3.5-27B when the benchmark that matters is ALE-Bench, where it leads.
- Shared results
- 44
- GLM 5.3 Flash only
- 58
- Qwen3.5-27B only
- 9
- Like-for-like categories
- 6 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 Flash
GLM 5.3 Flash scores 71.0 against 22.6.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
GLM 5.3 Flash
GLM 5.3 Flash leads, 41.8 against 22.9.
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
No call
Only GLM 5.3 Flash has a GPQA Diamond result, 86.4%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5.3 Flash has a MMLU Pro result, 86.1%.
Directional only
Web design
Design Arena: websites
No call
Only GLM 5.3 Flash has a Design Arena: websites result, 1272.
Directional only
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 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.3 Flash
GLM 5.3 Flash $0.0004, Qwen3.5-27B $0.0014 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5.3 Flash has the number, $0.02.
Directional only
At a glance
- Coding index
- GLM 5.3 Flash 71.0 from 2 benchmarks, Qwen3.5-27B 22.6 from 1 benchmark.
- Widest category
- Coding, 22.7 points apart on average over 6 shared benchmarks.
- Widest single gap
- LMArena WebDev: React: GLM 5.3 Flash 1624 against Qwen3.5-27B 1346, 42.3 points apart on a 0 to 100 scale.
- Output price
- GLM 5.3 Flash $0.50 against Qwen3.5-27B $2.16 per million output tokens, 4.3x apart.
- Context window
- GLM 5.3 Flash takes 1.05M tokens, Qwen3.5-27B 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 | GLM 5.3 Flash | Bars | Qwen3.5-27B | Ties |
|---|---|---|---|---|
| Coding | 5 | 1 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 7 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 24 | 0 | ||
| All shared boards | 43 | 1 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index51
| Benchmark | GLM 5.3 Flash | Qwen3.5-27B | Leader |
|---|---|---|---|
| LiveBench coding | 80.6% | Not measured | Only GLM 5.3 Flash measured |
| LMArena WebDev | 1607 | 1358 | GLM 5.3 Flash |
| AA Coding Index | 71.5 | Not measured | Only GLM 5.3 Flash measured |
| LiveCodeBench (Vals AI) | 80.5% | Not measured | Only GLM 5.3 Flash measured |
| SWE-bench Verified (Vals AI) | 92.0% | Not measured | Only GLM 5.3 Flash measured |
| Terminal-Bench 4 (Vals AI) | 37.4% | Not measured | Only GLM 5.3 Flash measured |
| Vibe Code Bench (Vals AI) | 30.8% | Not measured | Only GLM 5.3 Flash measured |
| Code migration (Vals AI) | 20.5% | Not measured | Only GLM 5.3 Flash measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only GLM 5.3 Flash measured |
| IOI (Vals AI) | 52.5% | Not measured | Only GLM 5.3 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | 62.9% | Not measured | Only GLM 5.3 Flash measured |
| Vibe Code Bench 1-100 (Vals AI) | 16.0% | Not measured | Only GLM 5.3 Flash measured |
| SkillsBench (Vals AI) | 40.2% | Not measured | Only GLM 5.3 Flash measured |
| CyberBench (Vals AI) | 67.9% | Not measured | Only GLM 5.3 Flash measured |
| SciCode | 51.6% | Not measured | Only GLM 5.3 Flash measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 32.6% | Only Qwen3.5-27B measured |
| FrontierCode | 31.8% | Not measured | Only GLM 5.3 Flash measured |
| FrontierSWE | 18.1% | Not measured | Only GLM 5.3 Flash measured |
| WeirdML | Not measured | 39.5% | Only Qwen3.5-27B measured |
| ALE-Bench | 303.6 | 349.4 | Qwen3.5-27B |
| LMArena Text: coding | 1507 | 1427 | GLM 5.3 Flash |
| LMArena Text: software and IT | 1500 | 1431 | GLM 5.3 Flash |
| LMArena WebDev: React | 1624 | 1346 | GLM 5.3 Flash |
| LMArena WebDev: HTML | 1577 | 1386 | GLM 5.3 Flash |
| LMArena WebDev: from an image | 1578 | Not measured | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 13 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 5.3 Flash | Qwen3.5-27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 41.8 | 22.9 | GLM 5.3 Flash |
| Epoch Capabilities Index | 153.1 | Not measured | Only GLM 5.3 Flash measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index14 boards, none shared
| Benchmark | GLM 5.3 Flash | Qwen3.5-27B | Leader |
|---|---|---|---|
| AA Agentic Index | 50.9 | Not measured | Only GLM 5.3 Flash measured |
| LiveBench research | 70.1% | Not measured | Only GLM 5.3 Flash measured |
| LiveBench browser | 84.0% | Not measured | Only GLM 5.3 Flash measured |
| LiveBench long agentic | 28.4% | Not measured | Only GLM 5.3 Flash measured |
| SREBench (Vals AI) | 0.8% | Not measured | Only GLM 5.3 Flash measured |
| Finance Agent (Vals AI) | 57.9% | Not measured | Only GLM 5.3 Flash measured |
| Tax Agent Bench (Vals AI) | 31.6% | Not measured | Only GLM 5.3 Flash measured |
| Legal Research Bench (Vals AI) | 45.2% | Not measured | Only GLM 5.3 Flash measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 10.4% | Not measured | Only GLM 5.3 Flash measured |
| Excel Modeling Benchmark (Vals AI) | 55.9% | Not measured | Only GLM 5.3 Flash measured |
| Terminal-Bench-Science (Vals AI) | 2.9% | Not measured | Only GLM 5.3 Flash measured |
| τ²-Bench Telecom | Not measured | 93.9% | Only Qwen3.5-27B measured |
| APEX-Agents | 52.8% | Not measured | Only GLM 5.3 Flash measured |
| Vending-Bench 2 | Not measured | $202 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 25 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | GLM 5.3 Flash | Qwen3.5-27B | Leader |
|---|---|---|---|
| GPQA Diamond | 86.4% | Not measured | Only GLM 5.3 Flash measured |
| LiveBench reasoning | 77.6% | Not measured | Only GLM 5.3 Flash measured |
| MMMU Pro (Vals AI) | 86.0% | Not measured | Only GLM 5.3 Flash measured |
| GPQA Diamond (Artificial Analysis run) | 91.2% | 85.8% | GLM 5.3 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 39.9% | 23.9% | GLM 5.3 Flash |
| GPQA Diamond (Epoch AI run) | 90.2% | Not measured | Only GLM 5.3 Flash measured |
| ARC-AGI | 91.0% | Not measured | Only GLM 5.3 Flash measured |
| ARC-AGI-2 | 65.8% | Not measured | Only GLM 5.3 Flash measured |
| Chess puzzles | 21.0% | Not measured | Only GLM 5.3 Flash measured |
| Mystery game puzzles | 8.0% | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: hard prompts | 1490 | 1415 | GLM 5.3 Flash |
| LMArena Text: expert | 1511 | 1427 | GLM 5.3 Flash |
| LMArena Text: hard prompts in English | 1488 | 1429 | GLM 5.3 Flash |
| LMArena Vision: diagrams | 1317 | 1249 | GLM 5.3 Flash |
| LMArena Vision: homework | 1318 | 1256 | GLM 5.3 Flash |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5.3 Flash | Qwen3.5-27B | Leader |
|---|---|---|---|
| OTIS Mock AIME | 93.9% | Not measured | Only GLM 5.3 Flash measured |
| FrontierMath, tiers 1 to 3 | 55.8% | Not measured | Only GLM 5.3 Flash measured |
| FrontierMath, tier 4 | 17.1% | Not measured | Only GLM 5.3 Flash measured |
| ProofBench | 21.0% | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: math | 1503 | 1429 | GLM 5.3 Flash |
| LMArena Text: mathematical work | 1497 | 1429 | 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 index40
| Benchmark | GLM 5.3 Flash | Qwen3.5-27B | Leader |
|---|---|---|---|
| MMLU Pro | 86.1% | Not measured | Only GLM 5.3 Flash measured |
| LegalBench (Vals AI) | 83.9% | Not measured | Only GLM 5.3 Flash measured |
| TaxEval (Vals AI) | 75.6% | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: science | 1484 | 1437 | GLM 5.3 Flash |
| LMArena Text: medicine and healthcare | 1475 | 1425 | GLM 5.3 Flash |
| LMArena Text: legal and government | 1473 | 1405 | GLM 5.3 Flash |
| LMArena Text: business and finance | 1460 | 1399 | GLM 5.3 Flash |
| Neither model has a result on the other 10 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GLM 5.3 Flash | Qwen3.5-27B | Leader |
|---|---|---|---|
| Design Arena: websites | 1272 | Not measured | Only GLM 5.3 Flash measured |
| Design Arena: UI components | 1316 | Not measured | Only GLM 5.3 Flash measured |
| Design Arena: game dev | 1279 | Not measured | Only GLM 5.3 Flash measured |
| Design Arena: data viz | 1262 | Not measured | Only GLM 5.3 Flash measured |
| Design Arena: 3D | 1316 | Not measured | Only GLM 5.3 Flash measured |
| Design Arena: all code categories | 1282 | Not measured | Only GLM 5.3 Flash measured |
| Design Arena: SVG | 1284 | Not measured | Only GLM 5.3 Flash measured |
| Design Arena: ASCII art | 1269 | Not measured | Only GLM 5.3 Flash measured |
| LiveBench design | 77.2% | Not measured | Only GLM 5.3 Flash measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index240
| Benchmark | GLM 5.3 Flash | Qwen3.5-27B | Leader |
|---|---|---|---|
| Vals Index | 50.9% | Not measured | Only GLM 5.3 Flash measured |
| LiveBench data analysis | 76.4% | Not measured | Only GLM 5.3 Flash measured |
| MedScribe (Vals AI) | 88.9% | Not measured | Only GLM 5.3 Flash measured |
| IFBench | Not measured | 75.6% | Only Qwen3.5-27B measured |
| Long context reasoning (AA-LCR) | 80.0% | 77.7% | GLM 5.3 Flash |
| DTBench | Not measured | 82.4% | Only Qwen3.5-27B measured |
| LMCA | Not measured | 34.0% | Only Qwen3.5-27B measured |
| Surface Evolver Bench | 52.5% | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text | 1471 | 1409 | GLM 5.3 Flash |
| LMArena Text: instruction following | 1478 | 1393 | GLM 5.3 Flash |
| LMArena Text: creative writing | 1441 | 1362 | GLM 5.3 Flash |
| LMArena Vision | 1294 | 1241 | GLM 5.3 Flash |
| LMArena Text: English | 1474 | 1425 | GLM 5.3 Flash |
| LMArena Text: non-English | 1462 | 1390 | GLM 5.3 Flash |
| LMArena Text: Chinese | 1531 | 1478 | GLM 5.3 Flash |
| LMArena Text: German | 1462 | 1392 | GLM 5.3 Flash |
| LMArena Text: Japanese | 1444 | 1346 | GLM 5.3 Flash |
| LMArena Text: Korean | 1436 | 1359 | GLM 5.3 Flash |
| LMArena Text: French | 1491 | 1410 | GLM 5.3 Flash |
| LMArena Text: Spanish | 1475 | 1407 | GLM 5.3 Flash |
| LMArena Text: Russian | 1465 | 1390 | GLM 5.3 Flash |
| LMArena Text: Polish | 1450 | 1375 | GLM 5.3 Flash |
| LMArena Text: multi-turn | 1469 | 1410 | GLM 5.3 Flash |
| LMArena Text: longer queries | 1481 | 1413 | GLM 5.3 Flash |
| LMArena Text: writing and language | 1455 | 1380 | GLM 5.3 Flash |
| LMArena Text: entertainment and media | 1440 | 1363 | GLM 5.3 Flash |
| LMArena Vision: English | 1295 | 1246 | GLM 5.3 Flash |
| LMArena Vision: Chinese | 1363 | 1301 | GLM 5.3 Flash |
| LMArena Vision: reading text | 1299 | 1246 | GLM 5.3 Flash |
| LMArena Vision: humor | 1279 | 1227 | GLM 5.3 Flash |
| LMArena Vision: captioning | Not measured | 1170 | Only Qwen3.5-27B measured |
| LMArena Vision: recognizing things | Not measured | 1210 | Only Qwen3.5-27B measured |
| LMArena Vision: creative writing | 1270 | 1224 | GLM 5.3 Flash |
| 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 Flash | Qwen3.5-27B | 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
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 | Qwen3.5-27B |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | Open weights |
| Released | August 26, 2026 | February 25, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $0.15 | $0.27 |
| Output $/Mtok | $0.50 | $2.16 |
| Coding index | 71.0 | 22.6 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | $0.02 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 Flash better than Qwen3.5-27B for coding?
Pick GLM 5.3 Flash for coding: it scores 71.0 on the Vibe Coding Index against 22.6 for Qwen3.5-27B. Pick Qwen3.5-27B when the benchmark that matters is ALE-Bench, where it leads.
Which is cheaper, GLM 5.3 Flash or Qwen3.5-27B?
GLM 5.3 Flash costs $0.15 per million input tokens and $0.50 per million output tokens; Qwen3.5-27B costs $0.27 and $2.16. Cost per solved task is known for GLM 5.3 Flash only.
Which has the bigger context window, GLM 5.3 Flash or Qwen3.5-27B?
GLM 5.3 Flash, with 1.05M tokens against 262K.
How many benchmarks is the GLM 5.3 Flash vs Qwen3.5-27B comparison based on?
44 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 Qwen3.5-27B's on 1. Where only one model has a result, the page says so instead of naming a winner.