GLM 4.5 Air vs Qwen3 VL 235B A22B Instruct
GLM 4.5 Air leads Qwen3 VL 235B A22B Instruct on the Artificial Analysis Intelligence Index, 11.1 to 9.9, and GLM 4.5 Air costs 1.7x less per output token ($1.10 vs $1.90 per million). Updated October 22, 2026; 39 benchmarks have a result for both.
GLM 4.5 Air or Qwen3 VL 235B A22B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 4.5 Air for output tokens 1.7x cheaper ($1.10 vs $1.90 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
- Shared results
- 39
- GLM 4.5 Air only
- 10
- Qwen3 VL 235B A22B Instruct only
- 10
- 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
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
GLM 4.5 Air
GLM 4.5 Air leads, 11.1 against 9.9.
Same board, both measured
Web design
Design Arena: websites
No call
Only GLM 4.5 Air has a Design Arena: websites result, 1146.
Directional only
Long documents
Listed context window
Qwen3 VL 235B A22B Instruct
Qwen3 VL 235B A22B Instruct takes 262K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.5 Air
GLM 4.5 Air $0.0008, Qwen3 VL 235B A22B Instruct $0.0012 per turn.
List prices
At a glance
- Coding index
- GLM 4.5 Air not benchmarked, Qwen3 VL 235B A22B Instruct not benchmarked.
- Widest category
- General, 8.4 points apart on average over 19 shared benchmarks.
- Widest single gap
- IFBench: GLM 4.5 Air 37.6% against Qwen3 VL 235B A22B Instruct 56.5%, 18.9 points apart on a 0 to 100 scale.
- Output price
- GLM 4.5 Air $1.10 against Qwen3 VL 235B A22B Instruct $1.90 per million output tokens, 1.7x apart.
- Context window
- GLM 4.5 Air takes 131K tokens, Qwen3 VL 235B A22B Instruct 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 4.5 Air | Bars | Qwen3 VL 235B A22B Instruct | Ties |
|---|---|---|---|---|
| Coding | 2 | 2 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 4 | ||
| Knowledge | 0 | 5 | ||
| General | 0 | 19 | ||
| All shared boards | 3 | 36 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index22
| Benchmark | GLM 4.5 Air | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 68.4% | 64.6% | GLM 4.5 Air |
| Terminal-Bench Hard (Artificial Analysis run) | 20.5% | 11.4% | GLM 4.5 Air |
| GSO-Bench | 2.9% | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: coding | 1397 | 1438 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: software and IT | 1401 | 1442 | Qwen3 VL 235B A22B Instruct |
| Neither model has a result on the other 38 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.5 Air | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.1 | 9.9 | GLM 4.5 Air |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.5 Air | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 46.5% | 54.1% | Qwen3 VL 235B A22B Instruct |
| Neither model has a result on the other 41 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | GLM 4.5 Air | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 73.3% | 77.2% | Qwen3 VL 235B A22B Instruct |
| Humanity's Last Exam (Artificial Analysis run) | 7.0% | 11.9% | Qwen3 VL 235B A22B Instruct |
| LMArena Text: hard prompts | 1379 | 1428 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: expert | 1370 | 1442 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: hard prompts in English | 1388 | 1439 | Qwen3 VL 235B A22B Instruct |
| LMArena Vision: diagrams | Not measured | 1255 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Vision: homework | Not measured | 1277 | Only Qwen3 VL 235B A22B Instruct measured |
| Kagi LLM Benchmark | 43.0% | Not measured | Only GLM 4.5 Air measured |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GLM 4.5 Air | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| AA Math Index | 80.7 | 88.3 | Qwen3 VL 235B A22B Instruct |
| AIME 2025 (Artificial Analysis run) | 80.7% | 88.3% | Qwen3 VL 235B A22B Instruct |
| LMArena Text: math | 1396 | 1425 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: mathematical work | 1396 | 1419 | Qwen3 VL 235B A22B Instruct |
| MathArena | 12.0% | Not measured | Only GLM 4.5 Air measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | GLM 4.5 Air | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 81.5% | 83.6% | Qwen3 VL 235B A22B Instruct |
| LMArena Text: science | 1407 | 1444 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: medicine and healthcare | 1402 | 1431 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: legal and government | 1382 | 1438 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: business and finance | 1371 | 1433 | Qwen3 VL 235B A22B Instruct |
| Neither model has a result on the other 15 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GLM 4.5 Air | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 1146 | Not measured | Only GLM 4.5 Air measured |
| Design Arena: UI components | 1121 | Not measured | Only GLM 4.5 Air measured |
| Design Arena: game dev | 1093 | Not measured | Only GLM 4.5 Air measured |
| Design Arena: data viz | 1186 | Not measured | Only GLM 4.5 Air measured |
| Design Arena: 3D | 1122 | Not measured | Only GLM 4.5 Air measured |
| Design Arena: all code categories | 1137 | Not measured | Only GLM 4.5 Air measured |
| Design Arena: SVG | 1070 | Not measured | Only GLM 4.5 Air measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index019
| Benchmark | GLM 4.5 Air | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| IFBench | 37.6% | 56.5% | Qwen3 VL 235B A22B Instruct |
| Long context reasoning (AA-LCR) | 46.7% | 63.7% | Qwen3 VL 235B A22B Instruct |
| LMArena Text | 1384 | 1420 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: instruction following | 1354 | 1406 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: creative writing | 1343 | 1366 | Qwen3 VL 235B A22B Instruct |
| LMArena Vision | Not measured | 1247 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: English | 1397 | 1429 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: non-English | 1366 | 1405 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: Chinese | 1426 | 1463 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: German | 1377 | 1424 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: Japanese | 1348 | 1385 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: Korean | 1308 | 1394 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: French | 1400 | 1452 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: Spanish | 1385 | 1428 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: Russian | 1373 | 1408 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: Polish | 1368 | 1419 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: multi-turn | 1371 | 1428 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: longer queries | 1366 | 1419 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: writing and language | 1354 | 1392 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: entertainment and media | 1339 | 1368 | Qwen3 VL 235B A22B Instruct |
| LMArena Vision: English | Not measured | 1256 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Vision: Chinese | Not measured | 1283 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Vision: reading text | Not measured | 1253 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Vision: humor | Not measured | 1209 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Vision: captioning | Not measured | 1281 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Vision: recognizing things | Not measured | 1249 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Vision: creative writing | Not measured | 1220 | Only Qwen3 VL 235B A22B Instruct measured |
| Neither model has a result on the other 21 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.5 Air | Qwen3 VL 235B A22B Instruct | 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 4.5 Air costs less
Repository review
50K input + 3K output tokens
GLM 4.5 Air costs less
Agent loop
200K input + 10K output tokens
GLM 4.5 Air 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 Air | Qwen3 VL 235B A22B Instruct |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | Open weights |
| Released | July 25, 2025 | September 23, 2025 |
| Context window | 131K | 262K |
| Input $/Mtok | $0.20 | $0.21 |
| Output $/Mtok | $1.10 | $1.90 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.5 Air better than Qwen3 VL 235B A22B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.5 Air for output tokens 1.7x cheaper ($1.10 vs $1.90 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
Which is cheaper, GLM 4.5 Air or Qwen3 VL 235B A22B Instruct?
GLM 4.5 Air costs $0.20 per million input tokens and $1.10 per million output tokens; Qwen3 VL 235B A22B Instruct costs $0.21 and $1.90. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.5 Air or Qwen3 VL 235B A22B Instruct?
Qwen3 VL 235B A22B Instruct, with 262K tokens against 131K.
How many benchmarks is the GLM 4.5 Air vs Qwen3 VL 235B A22B Instruct comparison based on?
39 benchmarks have a result for both models as of October 22, 2026. GLM 4.5 Air's coding index rests on 0 independent benchmarks and Qwen3 VL 235B A22B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.