GLM 4.7 vs Qwen3.5 Plus 2026-04-20
GLM 4.7 leads Qwen3.5 Plus 2026-04-20 on coding, 55.1 to 39.5 on the Vibe Coding Index, and Qwen3.5 Plus 2026-04-20 costs 1.2x less per output token ($1.80 vs $2.20 per million). Updated October 23, 2026; 18 benchmarks have a result for both.
GLM 4.7 or Qwen3.5 Plus 2026-04-20: which to pick
Pick GLM 4.7 for coding: it scores 55.1 on the Vibe Coding Index against 39.5 for Qwen3.5 Plus 2026-04-20.
Pick Qwen3.5 Plus 2026-04-20 for output tokens 1.2x cheaper ($1.80 vs $2.20 per million) and a larger context window (1M against 205K tokens).
- Shared results
- 18
- GLM 4.7 only
- 64
- Qwen3.5 Plus 2026-04-20 only
- 7
- 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 4.7
GLM 4.7 scores 55.1 against 39.5.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
No call
Only GLM 4.7 has a AA Intelligence Index result, 22.2.
Directional only
Reasoning
GPQA Diamond
Qwen3.5 Plus 2026-04-20
Qwen3.5 Plus 2026-04-20 leads, 87.4% against 80.0%.
Same board, both measured
Math
AIME
GLM 4.7
GLM 4.7 leads, 93.3% against 86.0%.
Same board, both measured
Knowledge
MMLU Pro
Qwen3.5 Plus 2026-04-20
Qwen3.5 Plus 2026-04-20 leads, 87.2% against 82.7%.
Same board, both measured
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
Qwen3.5 Plus 2026-04-20
Qwen3.5 Plus 2026-04-20 takes 1M tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5 Plus 2026-04-20
GLM 4.7 $0.0017, Qwen3.5 Plus 2026-04-20 $0.0012 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 4.7
GLM 4.7 $0.29, Qwen3.5 Plus 2026-04-20 $1.35.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 4.7 55.1 from 2 benchmarks, Qwen3.5 Plus 2026-04-20 39.5 from 2 benchmarks.
- Widest category
- Agentic work, 14.9 points apart on average over 1 shared benchmark.
- Widest single gap
- CorpFin (Vals AI): GLM 4.7 46.4% against Qwen3.5 Plus 2026-04-20 65.3%, 18.9 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 $2.20 against Qwen3.5 Plus 2026-04-20 $1.80 per million output tokens, 1.2x apart.
- Context window
- GLM 4.7 takes 205K tokens, Qwen3.5 Plus 2026-04-20 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 4.7 | Bars | Qwen3.5 Plus 2026-04-20 | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 1 | 2 | ||
| Math | 1 | 1 | ||
| Knowledge | 1 | 5 | ||
| General | 0 | 2 | ||
| All shared boards | 4 | 14 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | GLM 4.7 | Qwen3.5 Plus 2026-04-20 | 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% | 85.3% | Qwen3.5 Plus 2026-04-20 |
| SWE-bench Verified (Vals AI) | 69.4% | 71.2% | Qwen3.5 Plus 2026-04-20 |
| Vibe Code Bench (Vals AI) | Not measured | 15.7% | Only Qwen3.5 Plus 2026-04-20 measured |
| LiveCodeBench (Artificial Analysis run) | 89.4% | Not measured | Only GLM 4.7 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.8% | Not measured | Only GLM 4.7 measured |
| Terminal-Bench | 33.4% | Not measured | Only GLM 4.7 measured |
| ALE-Bench | 399.5 | 608.9 | Qwen3.5 Plus 2026-04-20 |
| LMArena Text: coding | 1454 | Not measured | Only GLM 4.7 measured |
| LMArena Text: software and IT | 1453 | Not measured | Only GLM 4.7 measured |
| 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 30 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.7 | Qwen3.5 Plus 2026-04-20 | Leader |
|---|---|---|---|
| AA Intelligence Index | 22.2 | Not measured | Only GLM 4.7 measured |
| Epoch Capabilities Index | 143.6 | 146.2 | Qwen3.5 Plus 2026-04-20 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.7 | Qwen3.5 Plus 2026-04-20 | 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 | 1114 | 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% | Not measured | Only GLM 4.7 measured |
| Vending-Bench 2 | $2,377 | $1 | GLM 4.7 |
| BrowseComp-Plus | 64.7% | Not measured | Only GLM 4.7 measured |
| MCP Atlas | 58.1% | 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 index12
| Benchmark | GLM 4.7 | Qwen3.5 Plus 2026-04-20 | Leader |
|---|---|---|---|
| GPQA Diamond | 80.0% | 87.4% | Qwen3.5 Plus 2026-04-20 |
| MMMU Pro (Vals AI) | Not measured | 22.8% | Only Qwen3.5 Plus 2026-04-20 measured |
| GPQA Diamond (Artificial Analysis run) | 85.9% | Not measured | Only GLM 4.7 measured |
| Humanity's Last Exam (Artificial Analysis run) | 27.4% | Not measured | Only GLM 4.7 measured |
| GPQA Diamond (Epoch AI run) | 83.3% | 84.8% | Qwen3.5 Plus 2026-04-20 |
| SimpleBench | 47.7% | Not measured | Only GLM 4.7 measured |
| Chess puzzles | 21.0% | 8.0% | GLM 4.7 |
| Mystery game puzzles | Not measured | 17.0% | Only Qwen3.5 Plus 2026-04-20 measured |
| LMArena Text: hard prompts | 1443 | Not measured | Only GLM 4.7 measured |
| LMArena Text: expert | 1423 | Not measured | Only GLM 4.7 measured |
| LMArena Text: hard prompts in English | 1452 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GLM 4.7 | Qwen3.5 Plus 2026-04-20 | Leader |
|---|---|---|---|
| AIME | 93.3% | 86.0% | GLM 4.7 |
| MGSM (Vals AI) | 88.2% | Not measured | Only GLM 4.7 measured |
| AA Math Index | 95.0 | Not measured | Only GLM 4.7 measured |
| AIME 2025 (Artificial Analysis run) | 95.0% | Not measured | Only GLM 4.7 measured |
| OTIS Mock AIME | 83.3% | 86.7% | Qwen3.5 Plus 2026-04-20 |
| ProofBench | 6.0% | Not measured | Only GLM 4.7 measured |
| LMArena Text: math | 1424 | Not measured | Only GLM 4.7 measured |
| LMArena Text: mathematical work | 1411 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index15
| Benchmark | GLM 4.7 | Qwen3.5 Plus 2026-04-20 | Leader |
|---|---|---|---|
| MMLU Pro | 82.7% | 87.2% | Qwen3.5 Plus 2026-04-20 |
| LegalBench (Vals AI) | 83.4% | 85.1% | Qwen3.5 Plus 2026-04-20 |
| CaseLaw (Vals AI) | 54.9% | 59.7% | Qwen3.5 Plus 2026-04-20 |
| CorpFin (Vals AI) | 46.4% | 65.3% | Qwen3.5 Plus 2026-04-20 |
| TaxEval (Vals AI) | 68.8% | Not measured | Only GLM 4.7 measured |
| MortgageTax (Vals AI) | Not measured | 60.8% | Only Qwen3.5 Plus 2026-04-20 measured |
| MedQA (Vals AI) | 93.7% | 95.2% | Qwen3.5 Plus 2026-04-20 |
| MedCode (Vals AI) | 32.8% | Not measured | Only GLM 4.7 measured |
| MMLU Pro (Artificial Analysis run) | 85.6% | Not measured | Only GLM 4.7 measured |
| SimpleQA Verified | 32.2% | 25.4% | GLM 4.7 |
| LMArena Text: science | 1458 | Not measured | Only GLM 4.7 measured |
| LMArena Text: medicine and healthcare | 1469 | Not measured | Only GLM 4.7 measured |
| LMArena Text: legal and government | 1444 | Not measured | Only GLM 4.7 measured |
| LMArena Text: business and finance | 1429 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 6 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GLM 4.7 | Qwen3.5 Plus 2026-04-20 | Leader |
|---|---|---|---|
| Design Arena: websites | 1225 | Not measured | Only GLM 4.7 measured |
| Design Arena: UI components | 1197 | Not measured | Only GLM 4.7 measured |
| Design Arena: game dev | 1188 | Not measured | Only GLM 4.7 measured |
| Design Arena: data viz | 1196 | Not measured | Only GLM 4.7 measured |
| Design Arena: 3D | 1191 | 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 index02
| Benchmark | GLM 4.7 | Qwen3.5 Plus 2026-04-20 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 30.4% | Only Qwen3.5 Plus 2026-04-20 measured |
| MedScribe (Vals AI) | 68.6% | Not measured | Only GLM 4.7 measured |
| IFBench | 67.9% | Not measured | Only GLM 4.7 measured |
| Long context reasoning (AA-LCR) | 71.0% | Not measured | Only GLM 4.7 measured |
| DTBench | Not measured | 80.5% | Only Qwen3.5 Plus 2026-04-20 measured |
| LMCA | Not measured | 36.4% | Only Qwen3.5 Plus 2026-04-20 measured |
| CL-bench | 15.9% | 19.8% | Qwen3.5 Plus 2026-04-20 |
| CL-bench Life | 10.9% | 12.4% | Qwen3.5 Plus 2026-04-20 |
| LMArena Text | 1435 | Not measured | Only GLM 4.7 measured |
| LMArena Text: instruction following | 1412 | Not measured | Only GLM 4.7 measured |
| LMArena Text: creative writing | 1401 | Not measured | Only GLM 4.7 measured |
| LMArena Text: English | 1453 | Not measured | Only GLM 4.7 measured |
| LMArena Text: non-English | 1417 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Chinese | 1495 | Not measured | Only GLM 4.7 measured |
| LMArena Text: German | 1426 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Japanese | 1439 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Korean | 1399 | Not measured | Only GLM 4.7 measured |
| LMArena Text: French | 1432 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Spanish | 1434 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Russian | 1423 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Polish | 1435 | Not measured | Only GLM 4.7 measured |
| LMArena Text: multi-turn | 1446 | Not measured | Only GLM 4.7 measured |
| LMArena Text: longer queries | 1432 | Not measured | Only GLM 4.7 measured |
| LMArena Text: writing and language | 1411 | Not measured | Only GLM 4.7 measured |
| LMArena Text: entertainment and media | 1401 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 23 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.7 | Qwen3.5 Plus 2026-04-20 | 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 Plus 2026-04-20 costs less
Repository review
50K input + 3K output tokens
Qwen3.5 Plus 2026-04-20 costs less
Agent loop
200K input + 10K output tokens
Qwen3.5 Plus 2026-04-20 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 | Qwen3.5 Plus 2026-04-20 |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | No public weights listed |
| Released | December 22, 2025 | April 27, 2026 |
| Context window | 205K | 1M |
| Input $/Mtok | $0.60 | $0.30 |
| Output $/Mtok | $2.20 | $1.80 |
| Coding index | 55.1 | 39.5 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | $0.29 | $1.35 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.7 better than Qwen3.5 Plus 2026-04-20 for coding?
Pick GLM 4.7 for coding: it scores 55.1 on the Vibe Coding Index against 39.5 for Qwen3.5 Plus 2026-04-20. Pick Qwen3.5 Plus 2026-04-20 for output tokens 1.2x cheaper ($1.80 vs $2.20 per million) and a larger context window (1M against 205K tokens).
Which is cheaper, GLM 4.7 or Qwen3.5 Plus 2026-04-20?
GLM 4.7 costs $0.60 per million input tokens and $2.20 per million output tokens; Qwen3.5 Plus 2026-04-20 costs $0.30 and $1.80. Per solved SWE-bench Verified task, GLM 4.7 costs $0.29 and Qwen3.5 Plus 2026-04-20 $1.35.
Which has the bigger context window, GLM 4.7 or Qwen3.5 Plus 2026-04-20?
Qwen3.5 Plus 2026-04-20, with 1M tokens against 205K.
How many benchmarks is the GLM 4.7 vs Qwen3.5 Plus 2026-04-20 comparison based on?
18 benchmarks have a result for both models as of October 23, 2026. GLM 4.7's coding index rests on 2 independent benchmarks and Qwen3.5 Plus 2026-04-20's on 2. Where only one model has a result, the page says so instead of naming a winner.