GLM 4.5 vs Qwen3.5-Flash
Updated October 21, 2026. 36 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 4.5 for coding: it scores 64.2 on the Vibe Coding Index against 6.2 for Qwen3.5-Flash.
Pick Qwen3.5-Flash when output tokens cost 8.5x less, or when the context window is larger, 1M against 131K tokens.
- Shared results
- 36
- GLM 4.5 only
- 24
- Qwen3.5-Flash only
- 19
- Like-for-like categories
- 5 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 4.5
GLM 4.5 scores 64.2 against 6.2.
One benchmark behind each score
Intelligence
AA Intelligence Index
No call
Only GLM 4.5 has a AA Intelligence Index result, 12.8.
Directional only
Reasoning
GPQA Diamond
Qwen3.5-Flash
Qwen3.5-Flash leads, 82.8% against 72.2%.
Same board, both measured
Math
AIME
Qwen3.5-Flash
Qwen3.5-Flash leads, 92.5% against 86.7%.
Same board, both measured
Knowledge
MMLU Pro
Qwen3.5-Flash
Qwen3.5-Flash leads, 84.1% against 81.2%.
Same board, both measured
Web design
Design Arena: websites
No call
Only GLM 4.5 has a Design Arena: websites result, 1170.
Directional only
Long documents
Listed context window
Qwen3.5-Flash
Qwen3.5-Flash takes 1M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-Flash
GLM 4.5 $0.0017, Qwen3.5-Flash $0.0002 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.5-Flash has the number, $0.40.
Directional only
At a glance
- Coding index
- GLM 4.5 64.2 from 1 benchmark, Qwen3.5-Flash 6.2 from 1 benchmark.
- Widest category
- Coding, 6.6 points apart on average over 4 shared benchmarks.
- Widest single gap
- LiveCodeBench (Vals AI): GLM 4.5 67.5% against Qwen3.5-Flash 83.3%, 15.8 points apart on a 0 to 100 scale.
- Output price
- GLM 4.5 $2.20 against Qwen3.5-Flash $0.26 per million output tokens, 8.5x apart.
- Context window
- GLM 4.5 takes 131K tokens, Qwen3.5-Flash 1M.
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 4.5 | Bars | Qwen3.5-Flash | Ties |
|---|---|---|---|---|
| Coding | 3 | 1 | ||
| Reasoning | 3 | 1 | ||
| Math | 2 | 1 | ||
| Knowledge | 5 | 3 | ||
| General | 17 | 0 | ||
| All shared boards | 30 | 6 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index31
| Benchmark | GLM 4.5 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| SWE-bench Verified | 64.2% | Not measured | Only GLM 4.5 measured |
| LMArena WebDev | Not measured | 1244 | Only Qwen3.5-Flash measured |
| LiveCodeBench (Vals AI) | 67.5% | 83.3% | Qwen3.5-Flash |
| SWE-bench Verified (Vals AI) | Not measured | 64.4% | Only Qwen3.5-Flash measured |
| LiveCodeBench (Artificial Analysis run) | 73.8% | Not measured | Only GLM 4.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 22.0% | Not measured | Only GLM 4.5 measured |
| WeirdML | 40.6% | Not measured | Only GLM 4.5 measured |
| ALE-Bench | 344.8 | 265.9 | GLM 4.5 |
| LMArena Text: coding | 1434 | 1412 | GLM 4.5 |
| LMArena Text: software and IT | 1440 | 1416 | GLM 4.5 |
| LMArena WebDev: React | Not measured | 1229 | Only Qwen3.5-Flash measured |
| LMArena WebDev: HTML | Not measured | 1291 | Only Qwen3.5-Flash measured |
| Neither model has a result on the other 26 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GLM 4.5 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.8 | Not measured | Only GLM 4.5 measured |
| Epoch Capabilities Index | Not measured | 143.5 | Only Qwen3.5-Flash measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GLM 4.5 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 43.0% | Not measured | Only GLM 4.5 measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index31
| Benchmark | GLM 4.5 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| GPQA Diamond | 72.2% | 82.8% | Qwen3.5-Flash |
| MMMU Pro (Vals AI) | Not measured | 81.9% | Only Qwen3.5-Flash measured |
| GPQA Diamond (Artificial Analysis run) | 78.2% | Not measured | Only GLM 4.5 measured |
| Humanity's Last Exam (Artificial Analysis run) | 13.0% | Not measured | Only GLM 4.5 measured |
| GPQA Diamond (Epoch AI run) | Not measured | 82.3% | Only Qwen3.5-Flash measured |
| Chess puzzles | Not measured | 14.0% | Only Qwen3.5-Flash measured |
| Mystery game puzzles | Not measured | 20.0% | Only Qwen3.5-Flash measured |
| LMArena Text: hard prompts | 1429 | 1403 | GLM 4.5 |
| LMArena Text: expert | 1433 | 1407 | GLM 4.5 |
| LMArena Text: hard prompts in English | 1429 | 1409 | GLM 4.5 |
| Neither model has a result on the other 11 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index21
| Benchmark | GLM 4.5 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AIME | 86.7% | 92.5% | Qwen3.5-Flash |
| MATH 500 (Vals AI) | 94.0% | Not measured | Only GLM 4.5 measured |
| MGSM (Vals AI) | 90.8% | Not measured | Only GLM 4.5 measured |
| AA Math Index | 73.7 | Not measured | Only GLM 4.5 measured |
| AIME 2025 (Artificial Analysis run) | 73.7% | Not measured | Only GLM 4.5 measured |
| OTIS Mock AIME | Not measured | 84.4% | Only Qwen3.5-Flash measured |
| FrontierMath, tiers 1 to 3 | Not measured | 18.2% | Only Qwen3.5-Flash measured |
| LMArena Text: math | 1427 | 1407 | GLM 4.5 |
| LMArena Text: mathematical work | 1430 | 1413 | GLM 4.5 |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index53
| Benchmark | GLM 4.5 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| MMLU Pro | 81.2% | 84.1% | Qwen3.5-Flash |
| LegalBench (Vals AI) | 75.6% | 84.3% | Qwen3.5-Flash |
| CaseLaw (Vals AI) | Not measured | 56.0% | Only Qwen3.5-Flash measured |
| CorpFin (Vals AI) | 61.0% | 63.6% | Qwen3.5-Flash |
| TaxEval (Vals AI) | 72.4% | 72.2% | GLM 4.5 |
| MortgageTax (Vals AI) | Not measured | 67.4% | Only Qwen3.5-Flash measured |
| MedQA (Vals AI) | 90.0% | Not measured | Only GLM 4.5 measured |
| MedCode (Vals AI) | Not measured | 33.0% | Only Qwen3.5-Flash measured |
| MMLU Pro (Artificial Analysis run) | 83.5% | Not measured | Only GLM 4.5 measured |
| SimpleQA Verified | Not measured | 20.3% | Only Qwen3.5-Flash measured |
| LMArena Text: science | 1444 | 1415 | GLM 4.5 |
| LMArena Text: medicine and healthcare | 1455 | 1407 | GLM 4.5 |
| LMArena Text: legal and government | 1438 | 1398 | GLM 4.5 |
| LMArena Text: business and finance | 1420 | 1394 | GLM 4.5 |
| Neither model has a result on the other 3 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GLM 4.5 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1170 | Not measured | Only GLM 4.5 measured |
| Design Arena: UI components | 1144 | Not measured | Only GLM 4.5 measured |
| Design Arena: game dev | 1145 | Not measured | Only GLM 4.5 measured |
| Design Arena: data viz | 1159 | Not measured | Only GLM 4.5 measured |
| Design Arena: 3D | 1170 | Not measured | Only GLM 4.5 measured |
| Design Arena: all code categories | 1165 | Not measured | Only GLM 4.5 measured |
| Design Arena: SVG | 1096 | Not measured | Only GLM 4.5 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index170
| Benchmark | GLM 4.5 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 42.5% | Only Qwen3.5-Flash measured |
| MedScribe (Vals AI) | Not measured | 70.6% | Only Qwen3.5-Flash measured |
| IFBench | 44.1% | Not measured | Only GLM 4.5 measured |
| Long context reasoning (AA-LCR) | 52.7% | Not measured | Only GLM 4.5 measured |
| DTBench | Not measured | 82.9% | Only Qwen3.5-Flash measured |
| LMCA | Not measured | 29.1% | Only Qwen3.5-Flash measured |
| Creative writing (Lech Mazur) | 73.4% | Not measured | Only GLM 4.5 measured |
| LMArena Text | 1430 | 1397 | GLM 4.5 |
| LMArena Text: instruction following | 1404 | 1374 | GLM 4.5 |
| LMArena Text: creative writing | 1395 | 1343 | GLM 4.5 |
| LMArena Text: English | 1437 | 1404 | GLM 4.5 |
| LMArena Text: non-English | 1417 | 1385 | GLM 4.5 |
| LMArena Text: Chinese | 1465 | 1445 | GLM 4.5 |
| LMArena Text: German | 1407 | 1390 | GLM 4.5 |
| LMArena Text: Japanese | 1415 | 1369 | GLM 4.5 |
| LMArena Text: Korean | 1380 | 1344 | GLM 4.5 |
| LMArena Text: French | 1418 | 1413 | GLM 4.5 |
| LMArena Text: Spanish | 1454 | 1400 | GLM 4.5 |
| LMArena Text: Russian | 1413 | 1380 | GLM 4.5 |
| LMArena Text: Polish | 1422 | 1376 | GLM 4.5 |
| LMArena Text: multi-turn | 1415 | 1393 | GLM 4.5 |
| LMArena Text: longer queries | 1412 | 1392 | GLM 4.5 |
| LMArena Text: writing and language | 1403 | 1369 | GLM 4.5 |
| LMArena Text: entertainment and media | 1404 | 1347 | GLM 4.5 |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.5 | Qwen3.5-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
Qwen3.5-Flash costs less
Repository review
50K input + 3K output tokens
Qwen3.5-Flash costs less
Agent loop
200K input + 10K output tokens
Qwen3.5-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 4.5 | Qwen3.5-Flash |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | No public weights listed |
| Released | July 25, 2025 | February 25, 2026 |
| Context window | 131K | 1M |
| Input $/Mtok | $0.60 | $0.065 |
| Output $/Mtok | $2.20 | $0.26 |
| Coding index | 64.2 | 6.2 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | Not computable | $0.40 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.5 better than Qwen3.5-Flash for coding?
Pick GLM 4.5 for coding: it scores 64.2 on the Vibe Coding Index against 6.2 for Qwen3.5-Flash. Pick Qwen3.5-Flash when output tokens cost 8.5x less, or when the context window is larger, 1M against 131K tokens.
Which is cheaper, GLM 4.5 or Qwen3.5-Flash?
GLM 4.5 costs $0.60 per million input tokens and $2.20 per million output tokens; Qwen3.5-Flash costs $0.065 and $0.26. Cost per solved task is known for Qwen3.5-Flash only.
Which has the bigger context window, GLM 4.5 or Qwen3.5-Flash?
Qwen3.5-Flash, with 1M tokens against 131K.
How many benchmarks is the GLM 4.5 vs Qwen3.5-Flash comparison based on?
36 benchmarks have a result for both models as of October 21, 2026. GLM 4.5's coding index rests on 1 independent benchmark and Qwen3.5-Flash's on 1. Where only one model has a result, the page says so instead of naming a winner.