GLM 4.7 vs Qwen3 VL 235B A22B Instruct
GLM 4.7 leads Qwen3 VL 235B A22B Instruct on the Artificial Analysis Intelligence Index, 22.2 to 9.9, at about the same output price ($2.20 vs $1.90 per million). Updated October 22, 2026; 39 benchmarks have a result for both.
GLM 4.7 or Qwen3 VL 235B A22B Instruct: which to pick
Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; Qwen3 VL 235B A22B Instruct has none, so the two cannot be ranked on coding.
Pick Qwen3 VL 235B A22B Instruct for output tokens 1.2x cheaper ($1.90 vs $2.20 per million) and a larger context window (262K against 205K tokens).
- Shared results
- 39
- GLM 4.7 only
- 42
- 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
Only GLM 4.7 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GLM 4.7
GLM 4.7 leads, 22.2 against 9.9.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GLM 4.7 has a GPQA Diamond result, 80.0%.
Directional only
Math
AIME
No call
Only GLM 4.7 has a AIME result, 93.3%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 4.7 has a MMLU Pro result, 82.7%.
Directional only
Web design
Design Arena: websites
No call
Only GLM 4.7 has a Design Arena: websites result, 1225.
Directional only
Long documents
Listed context window
Qwen3 VL 235B A22B Instruct
Qwen3 VL 235B A22B Instruct takes 262K tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 VL 235B A22B Instruct
GLM 4.7 $0.0017, Qwen3 VL 235B A22B Instruct $0.0012 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 4.7 has the number, $0.29.
Directional only
At a glance
- Coding index
- GLM 4.7 55.1 from 2 benchmarks, Qwen3 VL 235B A22B Instruct not benchmarked.
- Widest category
- Agentic work, 41.8 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: GLM 4.7 95.9% against Qwen3 VL 235B A22B Instruct 54.1%, 41.8 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 $2.20 against Qwen3 VL 235B A22B Instruct $1.90 per million output tokens, 1.2x apart.
- Context window
- GLM 4.7 takes 205K 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 4.7 | Bars | Qwen3 VL 235B A22B Instruct | Ties |
|---|---|---|---|---|
| Coding | 4 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 4 | 1 | ||
| Math | 2 | 2 | ||
| Knowledge | 4 | 1 | ||
| General | 18 | 1 | ||
| All shared boards | 34 | 5 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
| Benchmark | GLM 4.7 | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| LMArena WebDev | 1435 | Not measured | Only GLM 4.7 measured |
| AA Coding Index | 45.3 | Not measured | Only GLM 4.7 measured |
| LiveCodeBench (Vals AI) | 82.2% | Not measured | Only GLM 4.7 measured |
| SWE-bench Verified (Vals AI) | 69.4% | Not measured | Only GLM 4.7 measured |
| LiveCodeBench (Artificial Analysis run) | 89.4% | 64.6% | GLM 4.7 |
| Terminal-Bench Hard (Artificial Analysis run) | 31.8% | 11.4% | GLM 4.7 |
| Terminal-Bench | 33.4% | Not measured | Only GLM 4.7 measured |
| ALE-Bench | 399.5 | Not measured | Only GLM 4.7 measured |
| LMArena Text: coding | 1454 | 1438 | GLM 4.7 |
| LMArena Text: software and IT | 1453 | 1442 | GLM 4.7 |
| LMArena WebDev: React | 1420 | Not measured | Only GLM 4.7 measured |
| LMArena WebDev: HTML | 1447 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 27 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.7 | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 22.2 | 9.9 | GLM 4.7 |
| Epoch Capabilities Index | 143.6 | Not measured | Only GLM 4.7 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.7 | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| Design Arena: agents building full-stack apps | 1050 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building mobile apps | 1105 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building Android apps | 1112 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building Godot games | 1058 | Not measured | Only GLM 4.7 measured |
| τ²-Bench Telecom | 95.9% | 54.1% | GLM 4.7 |
| Vending-Bench 2 | $2,377 | Not measured | Only GLM 4.7 measured |
| BrowseComp-Plus | 64.7% | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 34 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | GLM 4.7 | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond | 80.0% | Not measured | Only GLM 4.7 measured |
| GPQA Diamond (Artificial Analysis run) | 85.9% | 77.2% | GLM 4.7 |
| Humanity's Last Exam (Artificial Analysis run) | 27.4% | 11.9% | GLM 4.7 |
| GPQA Diamond (Epoch AI run) | 83.3% | Not measured | Only GLM 4.7 measured |
| SimpleBench | 47.7% | Not measured | Only GLM 4.7 measured |
| Chess puzzles | 21.0% | Not measured | Only GLM 4.7 measured |
| LMArena Text: hard prompts | 1443 | 1428 | GLM 4.7 |
| LMArena Text: expert | 1424 | 1442 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: hard prompts in English | 1452 | 1439 | GLM 4.7 |
| 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 |
| Neither model has a result on the other 11 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index22
| Benchmark | GLM 4.7 | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| AIME | 93.3% | Not measured | Only GLM 4.7 measured |
| MGSM (Vals AI) | 88.2% | Not measured | Only GLM 4.7 measured |
| AA Math Index | 95.0 | 88.3 | GLM 4.7 |
| AIME 2025 (Artificial Analysis run) | 95.0% | 88.3% | GLM 4.7 |
| OTIS Mock AIME | 83.3% | Not measured | Only GLM 4.7 measured |
| ProofBench | 6.0% | Not measured | Only GLM 4.7 measured |
| LMArena Text: math | 1424 | 1425 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: mathematical work | 1411 | 1419 | Qwen3 VL 235B A22B Instruct |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | GLM 4.7 | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| MMLU Pro | 82.7% | Not measured | Only GLM 4.7 measured |
| LegalBench (Vals AI) | 83.4% | Not measured | Only GLM 4.7 measured |
| CaseLaw (Vals AI) | 54.9% | Not measured | Only GLM 4.7 measured |
| CorpFin (Vals AI) | 46.4% | Not measured | Only GLM 4.7 measured |
| TaxEval (Vals AI) | 68.8% | Not measured | Only GLM 4.7 measured |
| MedQA (Vals AI) | 93.7% | Not measured | Only GLM 4.7 measured |
| MedCode (Vals AI) | 32.8% | Not measured | Only GLM 4.7 measured |
| MMLU Pro (Artificial Analysis run) | 85.6% | 83.6% | GLM 4.7 |
| SimpleQA Verified | 32.2% | Not measured | Only GLM 4.7 measured |
| LMArena Text: science | 1457 | 1444 | GLM 4.7 |
| LMArena Text: medicine and healthcare | 1469 | 1431 | GLM 4.7 |
| LMArena Text: legal and government | 1444 | 1438 | GLM 4.7 |
| LMArena Text: business and finance | 1429 | 1433 | Qwen3 VL 235B A22B Instruct |
| Neither model has a result on the other 5 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GLM 4.7 | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 1225 | Not measured | Only GLM 4.7 measured |
| Design Arena: UI components | 1196 | Not measured | Only GLM 4.7 measured |
| Design Arena: game dev | 1188 | Not measured | Only GLM 4.7 measured |
| Design Arena: data viz | 1195 | Not measured | Only GLM 4.7 measured |
| Design Arena: 3D | 1190 | Not measured | Only GLM 4.7 measured |
| Design Arena: all code categories | 1216 | Not measured | Only GLM 4.7 measured |
| Design Arena: SVG | 1142 | Not measured | Only GLM 4.7 measured |
| Design Arena: ASCII art | 1165 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index181
| Benchmark | GLM 4.7 | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 68.6% | Not measured | Only GLM 4.7 measured |
| IFBench | 67.9% | 56.5% | GLM 4.7 |
| Long context reasoning (AA-LCR) | 71.0% | 63.7% | GLM 4.7 |
| CL-bench | 15.9% | Not measured | Only GLM 4.7 measured |
| CL-bench Life | 10.9% | Not measured | Only GLM 4.7 measured |
| LMArena Text | 1435 | 1420 | GLM 4.7 |
| LMArena Text: instruction following | 1412 | 1406 | GLM 4.7 |
| LMArena Text: creative writing | 1401 | 1366 | GLM 4.7 |
| LMArena Vision | Not measured | 1247 | Only Qwen3 VL 235B A22B Instruct measured |
| LMArena Text: English | 1453 | 1429 | GLM 4.7 |
| LMArena Text: non-English | 1417 | 1405 | GLM 4.7 |
| LMArena Text: Chinese | 1495 | 1463 | GLM 4.7 |
| LMArena Text: German | 1426 | 1424 | GLM 4.7 |
| LMArena Text: Japanese | 1439 | 1385 | GLM 4.7 |
| LMArena Text: Korean | 1399 | 1394 | GLM 4.7 |
| LMArena Text: French | 1432 | 1452 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: Spanish | 1434 | 1428 | GLM 4.7 |
| LMArena Text: Russian | 1423 | 1408 | GLM 4.7 |
| LMArena Text: Polish | 1435 | 1419 | GLM 4.7 |
| LMArena Text: multi-turn | 1446 | 1428 | GLM 4.7 |
| LMArena Text: longer queries | 1432 | 1419 | GLM 4.7 |
| LMArena Text: writing and language | 1411 | 1392 | GLM 4.7 |
| LMArena Text: entertainment and media | 1400 | 1368 | GLM 4.7 |
| 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 16 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.7 | 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
Qwen3 VL 235B A22B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen3 VL 235B A22B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen3 VL 235B A22B Instruct 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 4.7 | Qwen3 VL 235B A22B Instruct |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | Open weights |
| Released | December 22, 2025 | September 23, 2025 |
| Context window | 205K | 262K |
| Input $/Mtok | $0.60 | $0.21 |
| Output $/Mtok | $2.20 | $1.90 |
| Coding index | 55.1 | Not benchmarked |
| Independent measurements behind it | 2 | 0 |
| Cost per solved task | $0.29 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.7 better than Qwen3 VL 235B A22B Instruct for coding?
Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; Qwen3 VL 235B A22B Instruct has none, so the two cannot be ranked on coding. Pick Qwen3 VL 235B A22B Instruct for output tokens 1.2x cheaper ($1.90 vs $2.20 per million) and a larger context window (262K against 205K tokens).
Which is cheaper, GLM 4.7 or Qwen3 VL 235B A22B Instruct?
GLM 4.7 costs $0.60 per million input tokens and $2.20 per million output tokens; Qwen3 VL 235B A22B Instruct costs $0.21 and $1.90. Cost per solved task is known for GLM 4.7 only.
Which has the bigger context window, GLM 4.7 or Qwen3 VL 235B A22B Instruct?
Qwen3 VL 235B A22B Instruct, with 262K tokens against 205K.
How many benchmarks is the GLM 4.7 vs Qwen3 VL 235B A22B Instruct comparison based on?
39 benchmarks have a result for both models as of October 22, 2026. GLM 4.7's coding index rests on 2 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.