GLM 5 vs Qwen3 235B A22B Thinking 2507
GLM 5 leads Qwen3 235B A22B Thinking 2507 on the Artificial Analysis Intelligence Index, 27.9 to 12.7, at about the same output price ($3.20 vs $3.50 per million). Updated October 22, 2026; 38 benchmarks have a result for both.
GLM 5 or Qwen3 235B A22B Thinking 2507: which to pick
Only GLM 5 has a coding score, 41.7 on the Vibe Coding Index; Qwen3 235B A22B Thinking 2507 has none, so the two cannot be ranked on coding.
Pick Qwen3 235B A22B Thinking 2507 for work measured by LMArena Text: expert or OTIS Mock AIME, where it leads.
- Shared results
- 38
- GLM 5 only
- 38
- Qwen3 235B A22B Thinking 2507 only
- 8
- Like-for-like categories
- 8 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 5 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GLM 5
GLM 5 leads, 27.9 against 12.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3 235B A22B Thinking 2507 has a AA Agentic Index result, 1.3.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 5 has a GPQA Diamond result, 83.3%.
Directional only
Math
AIME
No call
Only GLM 5 has a AIME result, 91.7%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5 has a MMLU Pro result, 86.0%.
Directional only
Web design
Design Arena: websites
GLM 5
GLM 5 leads, 1247 against 1052.
Same board, both measured
Long documents
Listed context window
GLM 5
GLM 5 takes 205K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 235B A22B Thinking 2507
GLM 5 $0.0026, Qwen3 235B A22B Thinking 2507 $0.0022 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5 has the number, $0.63.
Directional only
At a glance
- Coding index
- GLM 5 41.7 from 3 benchmarks, Qwen3 235B A22B Thinking 2507 not benchmarked.
- Widest category
- Web design, 30.9 points apart on average over 6 shared benchmarks.
- Widest single gap
- Design Arena: UI components: GLM 5 1227 against Qwen3 235B A22B Thinking 2507 942, 35.6 points apart on a 0 to 100 scale.
- Output price
- GLM 5 $3.20 against Qwen3 235B A22B Thinking 2507 $3.50 per million output tokens, 1.1x apart.
- Context window
- GLM 5 takes 205K tokens, Qwen3 235B A22B Thinking 2507 128K.
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 | Bars | Qwen3 235B A22B Thinking 2507 | Ties |
|---|---|---|---|---|
| Coding | 3 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 3 | 1 | ||
| Math | 2 | 1 | ||
| Knowledge | 3 | 1 | ||
| Web design | 6 | 0 | ||
| General | 16 | 0 | ||
| All shared boards | 35 | 3 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
| Benchmark | GLM 5 | Qwen3 235B A22B Thinking 2507 | Leader |
|---|---|---|---|
| SWE-bench Verified | 72.8% | Not measured | Only GLM 5 measured |
| SWE-bench Multilingual | 69.7% | Not measured | Only GLM 5 measured |
| LMArena WebDev | 1435 | Not measured | Only GLM 5 measured |
| AA Coding Index | Not measured | 22.1 | Only Qwen3 235B A22B Thinking 2507 measured |
| LiveCodeBench (Vals AI) | 81.9% | Not measured | Only GLM 5 measured |
| SWE-bench Verified (Vals AI) | 71.4% | Not measured | Only GLM 5 measured |
| Vibe Code Bench (Vals AI) | 23.4% | Not measured | Only GLM 5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 43.2% | Not measured | Only GLM 5 measured |
| Terminal-Bench | 52.4% | Not measured | Only GLM 5 measured |
| WeirdML | 48.2% | 41.0% | GLM 5 |
| ALE-Bench | 765.6 | Not measured | Only GLM 5 measured |
| LMArena Text: coding | 1461 | 1424 | GLM 5 |
| LMArena Text: software and IT | 1460 | 1431 | GLM 5 |
| LMArena WebDev: React | 1424 | Not measured | Only GLM 5 measured |
| LMArena WebDev: HTML | 1450 | Not measured | Only GLM 5 measured |
| Neither model has a result on the other 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 5 | Qwen3 235B A22B Thinking 2507 | Leader |
|---|---|---|---|
| AA Intelligence Index | 27.9 | 12.7 | GLM 5 |
| Epoch Capabilities Index | 145.8 | Not measured | Only GLM 5 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 5 | Qwen3 235B A22B Thinking 2507 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.3 | Only Qwen3 235B A22B Thinking 2507 measured |
| Design Arena: agents building full-stack apps | 1123 | Not measured | Only GLM 5 measured |
| Design Arena: agents building mobile apps | 1136 | Not measured | Only GLM 5 measured |
| Design Arena: agents building Android apps | 1147 | Not measured | Only GLM 5 measured |
| Design Arena: agents building Godot games | 1144 | Not measured | Only GLM 5 measured |
| Design Arena: HTML slides | 1098 | Not measured | Only GLM 5 measured |
| τ²-Bench Telecom | 98.2% | Not measured | Only GLM 5 measured |
| Vending-Bench 2 | $4,432 | -$11 | GLM 5 |
| Neither model has a result on the other 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index31
| Benchmark | GLM 5 | Qwen3 235B A22B Thinking 2507 | Leader |
|---|---|---|---|
| GPQA Diamond | 83.3% | Not measured | Only GLM 5 measured |
| GPQA Diamond (Artificial Analysis run) | 82.0% | Not measured | Only GLM 5 measured |
| Humanity's Last Exam (Artificial Analysis run) | 29.3% | Not measured | Only GLM 5 measured |
| GPQA Diamond (Epoch AI run) | 87.8% | 80.1% | GLM 5 |
| ARC-AGI | 44.7% | Not measured | Only GLM 5 measured |
| ARC-AGI-2 | 4.9% | Not measured | Only GLM 5 measured |
| SimpleBench | 53.2% | Not measured | Only GLM 5 measured |
| Mystery game puzzles | Not measured | 9.0% | Only Qwen3 235B A22B Thinking 2507 measured |
| LMArena Text: hard prompts | 1452 | 1416 | GLM 5 |
| LMArena Text: expert | 1454 | 1463 | Qwen3 235B A22B Thinking 2507 |
| LMArena Text: hard prompts in English | 1460 | 1432 | GLM 5 |
| Kagi LLM Benchmark | 75.0% | Not measured | Only GLM 5 measured |
| Neither model has a result on the other 10 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index21
| Benchmark | GLM 5 | Qwen3 235B A22B Thinking 2507 | Leader |
|---|---|---|---|
| AIME | 91.7% | Not measured | Only GLM 5 measured |
| OTIS Mock AIME | 80.0% | 86.7% | Qwen3 235B A22B Thinking 2507 |
| LMArena Text: math | 1440 | 1412 | GLM 5 |
| LMArena Text: mathematical work | 1451 | 1435 | GLM 5 |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index31
| Benchmark | GLM 5 | Qwen3 235B A22B Thinking 2507 | Leader |
|---|---|---|---|
| MMLU Pro | 86.0% | Not measured | Only GLM 5 measured |
| LegalBench (Vals AI) | 84.1% | Not measured | Only GLM 5 measured |
| CaseLaw (Vals AI) | 52.5% | Not measured | Only GLM 5 measured |
| CorpFin (Vals AI) | 62.9% | Not measured | Only GLM 5 measured |
| TaxEval (Vals AI) | 70.0% | Not measured | Only GLM 5 measured |
| MedQA (Vals AI) | 94.3% | Not measured | Only GLM 5 measured |
| SimpleQA Verified | Not measured | 40.4% | Only Qwen3 235B A22B Thinking 2507 measured |
| LMArena Text: science | 1460 | 1433 | GLM 5 |
| LMArena Text: medicine and healthcare | 1455 | 1456 | Qwen3 235B A22B Thinking 2507 |
| LMArena Text: legal and government | 1453 | 1415 | GLM 5 |
| LMArena Text: business and finance | 1430 | 1413 | GLM 5 |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GLM 5 | Qwen3 235B A22B Thinking 2507 | Leader |
|---|---|---|---|
| Design Arena: websites | 1247 | 1052 | GLM 5 |
| Design Arena: UI components | 1227 | 942 | GLM 5 |
| Design Arena: game dev | 1234 | 956 | GLM 5 |
| Design Arena: data viz | 1228 | 950 | GLM 5 |
| Design Arena: 3D | 1228 | 997 | GLM 5 |
| Design Arena: all code categories | 1246 | 1032 | GLM 5 |
| Design Arena: SVG | 1168 | Not measured | Only GLM 5 measured |
| Design Arena: ASCII art | 1147 | Not measured | Only GLM 5 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index160
| Benchmark | GLM 5 | Qwen3 235B A22B Thinking 2507 | Leader |
|---|---|---|---|
| IFBench | 72.3% | Not measured | Only GLM 5 measured |
| Long context reasoning (AA-LCR) | 75.7% | Not measured | Only GLM 5 measured |
| DTBench | Not measured | 80.3% | Only Qwen3 235B A22B Thinking 2507 measured |
| LMCA | Not measured | 29.3% | Only Qwen3 235B A22B Thinking 2507 measured |
| Fiction.LiveBench | Not measured | 75.0% | Only Qwen3 235B A22B Thinking 2507 measured |
| Creative writing (Lech Mazur) | Not measured | 82.4% | Only Qwen3 235B A22B Thinking 2507 measured |
| CL-bench | 18.7% | Not measured | Only GLM 5 measured |
| LMArena Text | 1446 | 1415 | GLM 5 |
| LMArena Text: instruction following | 1428 | 1386 | GLM 5 |
| LMArena Text: creative writing | 1438 | 1384 | GLM 5 |
| LMArena Text: English | 1458 | 1429 | GLM 5 |
| LMArena Text: non-English | 1430 | 1397 | GLM 5 |
| LMArena Text: Chinese | 1511 | 1481 | GLM 5 |
| LMArena Text: German | 1447 | 1408 | GLM 5 |
| LMArena Text: Japanese | 1416 | 1386 | GLM 5 |
| LMArena Text: Korean | 1423 | 1375 | GLM 5 |
| LMArena Text: French | 1456 | Not measured | Only GLM 5 measured |
| LMArena Text: Spanish | 1454 | 1389 | GLM 5 |
| LMArena Text: Russian | 1436 | 1401 | GLM 5 |
| LMArena Text: Polish | 1434 | 1390 | GLM 5 |
| LMArena Text: multi-turn | 1456 | 1406 | GLM 5 |
| LMArena Text: longer queries | 1446 | 1400 | GLM 5 |
| LMArena Text: writing and language | 1437 | 1388 | GLM 5 |
| LMArena Text: entertainment and media | 1426 | 1370 | GLM 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 5 | Qwen3 235B A22B Thinking 2507 | 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 235B A22B Thinking 2507 costs less
Repository review
50K input + 3K output tokens
Qwen3 235B A22B Thinking 2507 costs less
Agent loop
200K input + 10K output tokens
Qwen3 235B A22B Thinking 2507 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 | Qwen3 235B A22B Thinking 2507 |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | Open weights |
| Released | February 11, 2026 | July 25, 2025 |
| Context window | 205K | 128K |
| Input $/Mtok | $1.00 | $0.45 |
| Output $/Mtok | $3.20 | $3.50 |
| Coding index | 41.7 | Not benchmarked |
| Independent measurements behind it | 3 | 0 |
| Cost per solved task | $0.63 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5 better than Qwen3 235B A22B Thinking 2507 for coding?
Only GLM 5 has a coding score, 41.7 on the Vibe Coding Index; Qwen3 235B A22B Thinking 2507 has none, so the two cannot be ranked on coding. Pick Qwen3 235B A22B Thinking 2507 for work measured by LMArena Text: expert or OTIS Mock AIME, where it leads.
Which is cheaper, GLM 5 or Qwen3 235B A22B Thinking 2507?
GLM 5 costs $1.00 per million input tokens and $3.20 per million output tokens; Qwen3 235B A22B Thinking 2507 costs $0.45 and $3.50. Cost per solved task is known for GLM 5 only.
Which has the bigger context window, GLM 5 or Qwen3 235B A22B Thinking 2507?
GLM 5, with 205K tokens against 128K.
How many benchmarks is the GLM 5 vs Qwen3 235B A22B Thinking 2507 comparison based on?
38 benchmarks have a result for both models as of October 22, 2026. GLM 5's coding index rests on 3 independent benchmarks and Qwen3 235B A22B Thinking 2507's on 0. Where only one model has a result, the page says so instead of naming a winner.