GLM 4.6 vs Qwen2.5 Coder 32B Instruct
GLM 4.6 leads Qwen2.5 Coder 32B Instruct on the Artificial Analysis Intelligence Index, 18.5 to 6.7, and Qwen2.5 Coder 32B Instruct costs 2.2x less per output token ($1.00 vs $2.20 per million). Updated October 22, 2026; 28 benchmarks have a result for both.
GLM 4.6 or Qwen2.5 Coder 32B Instruct: which to pick
Only GLM 4.6 has a coding score, 8.8 on the Vibe Coding Index; Qwen2.5 Coder 32B Instruct has none, so the two cannot be ranked on coding.
Pick Qwen2.5 Coder 32B Instruct for output tokens 2.2x cheaper ($1.00 vs $2.20 per million).
- Shared results
- 28
- GLM 4.6 only
- 41
- Qwen2.5 Coder 32B Instruct only
- 7
- Like-for-like categories
- 6 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 4.6 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GLM 4.6
GLM 4.6 leads, 18.5 against 6.7.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GLM 4.6 has a GPQA Diamond result, 74.5%.
Directional only
Math
AIME
No call
Only GLM 4.6 has a AIME result, 92.7%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 4.6 has a MMLU Pro result, 82.2%.
Directional only
Web design
Design Arena: websites
No call
Only GLM 4.6 has a Design Arena: websites result, 1174.
Directional only
Long documents
Listed context window
GLM 4.6
GLM 4.6 takes 205K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen2.5 Coder 32B Instruct
GLM 4.6 $0.0017, Qwen2.5 Coder 32B Instruct $0.0012 per turn.
List prices
At a glance
- Coding index
- GLM 4.6 8.8 from 2 benchmarks, Qwen2.5 Coder 32B Instruct not benchmarked.
- Widest category
- General, 36.1 points apart on average over 11 shared benchmarks.
- Widest single gap
- LMArena Text: Chinese: GLM 4.6 1499 against Qwen2.5 Coder 32B Instruct 1222, 46.2 points apart on a 0 to 100 scale.
- Output price
- GLM 4.6 $2.20 against Qwen2.5 Coder 32B Instruct $1.00 per million output tokens, 2.2x apart.
- Context window
- GLM 4.6 takes 205K tokens, Qwen2.5 Coder 32B Instruct 33K.
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 4.6 | Bars | Qwen2.5 Coder 32B Instruct | Ties |
|---|---|---|---|---|
| Coding | 4 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 5 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 5 | 0 | ||
| General | 11 | 0 | ||
| All shared boards | 28 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
| Benchmark | GLM 4.6 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 16.4% | Only Qwen2.5 Coder 32B Instruct measured |
| SWE-bench Verified | 68.2% | 47.0% | GLM 4.6 |
| LMArena WebDev | 1341 | Not measured | Only GLM 4.6 measured |
| AA Coding Index | 45.8 | Not measured | Only GLM 4.6 measured |
| LiveCodeBench (Vals AI) | 81.0% | Not measured | Only GLM 4.6 measured |
| Vibe Code Bench (Vals AI) | 3.1% | Not measured | Only GLM 4.6 measured |
| LiveCodeBench (Artificial Analysis run) | 69.5% | 29.5% | GLM 4.6 |
| Terminal-Bench Hard (Artificial Analysis run) | 28.8% | Not measured | Only GLM 4.6 measured |
| Terminal-Bench | 24.5% | Not measured | Only GLM 4.6 measured |
| ALE-Bench | 340.8 | Not measured | Only GLM 4.6 measured |
| LMArena Text: coding | 1449 | 1276 | GLM 4.6 |
| LMArena Text: software and IT | 1455 | 1261 | GLM 4.6 |
| LMArena WebDev: HTML | 1350 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 26 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.6 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 18.5 | 6.7 | GLM 4.6 |
| Epoch Capabilities Index | Not measured | 119.5 | Only Qwen2.5 Coder 32B Instruct measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GLM 4.6 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Design Arena: agents building full-stack apps | 1029 | Not measured | Only GLM 4.6 measured |
| Design Arena: agents building mobile apps | 1096 | Not measured | Only GLM 4.6 measured |
| Design Arena: agents building Android apps | 1084 | Not measured | Only GLM 4.6 measured |
| Design Arena: agents building Godot games | 1180 | Not measured | Only GLM 4.6 measured |
| τ²-Bench Telecom | 76.9% | Not measured | Only GLM 4.6 measured |
| Berkeley Function Calling Leaderboard (BFCL) | 72.4% | Not measured | Only GLM 4.6 measured |
| BrowseComp-Plus | 48.2% | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 34 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 4.6 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond | 74.5% | Not measured | Only GLM 4.6 measured |
| GPQA Diamond (Artificial Analysis run) | 78.0% | 41.7% | GLM 4.6 |
| Humanity's Last Exam (Artificial Analysis run) | 14.5% | 3.5% | GLM 4.6 |
| LMArena Text: hard prompts | 1440 | 1251 | GLM 4.6 |
| LMArena Text: expert | 1432 | 1221 | GLM 4.6 |
| LMArena Text: hard prompts in English | 1445 | 1269 | GLM 4.6 |
| Kagi LLM Benchmark | 45.7% | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 4.6 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| AIME | 92.7% | Not measured | Only GLM 4.6 measured |
| MGSM (Vals AI) | 89.8% | Not measured | Only GLM 4.6 measured |
| AA Math Index | 86.0 | Not measured | Only GLM 4.6 measured |
| AIME 2025 (Artificial Analysis run) | 86.0% | Not measured | Only GLM 4.6 measured |
| GSM8K | Not measured | 93.0% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: math | 1432 | 1251 | GLM 4.6 |
| LMArena Text: mathematical work | 1447 | 1243 | GLM 4.6 |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 4.6 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| MMLU Pro | 82.2% | Not measured | Only GLM 4.6 measured |
| LegalBench (Vals AI) | 79.6% | Not measured | Only GLM 4.6 measured |
| CorpFin (Vals AI) | 56.8% | Not measured | Only GLM 4.6 measured |
| TaxEval (Vals AI) | 66.2% | Not measured | Only GLM 4.6 measured |
| MedQA (Vals AI) | 92.2% | Not measured | Only GLM 4.6 measured |
| MMLU Pro (Artificial Analysis run) | 82.9% | 63.5% | GLM 4.6 |
| MMLU | Not measured | 79.1% | Only Qwen2.5 Coder 32B Instruct measured |
| AI2 Reasoning Challenge | Not measured | 70.5% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: science | 1456 | 1221 | GLM 4.6 |
| LMArena Text: medicine and healthcare | 1455 | 1187 | GLM 4.6 |
| LMArena Text: legal and government | 1448 | 1257 | GLM 4.6 |
| LMArena Text: business and finance | 1416 | 1224 | GLM 4.6 |
| Neither model has a result on the other 6 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GLM 4.6 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 1174 | Not measured | Only GLM 4.6 measured |
| Design Arena: UI components | 1145 | Not measured | Only GLM 4.6 measured |
| Design Arena: game dev | 1150 | Not measured | Only GLM 4.6 measured |
| Design Arena: data viz | 1161 | Not measured | Only GLM 4.6 measured |
| Design Arena: 3D | 1128 | Not measured | Only GLM 4.6 measured |
| Design Arena: all code categories | 1165 | Not measured | Only GLM 4.6 measured |
| Design Arena: SVG | 1108 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index110
| Benchmark | GLM 4.6 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| IFBench | 43.4% | Not measured | Only GLM 4.6 measured |
| Long context reasoning (AA-LCR) | 54.0% | Not measured | Only GLM 4.6 measured |
| HellaSwag | Not measured | 83.0% | Only Qwen2.5 Coder 32B Instruct measured |
| WinoGrande | Not measured | 80.8% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text | 1440 | 1230 | GLM 4.6 |
| LMArena Text: instruction following | 1410 | 1223 | GLM 4.6 |
| LMArena Text: creative writing | 1412 | 1174 | GLM 4.6 |
| LMArena Text: English | 1449 | 1248 | GLM 4.6 |
| LMArena Text: non-English | 1426 | 1205 | GLM 4.6 |
| LMArena Text: Chinese | 1499 | 1222 | GLM 4.6 |
| LMArena Text: German | 1447 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Japanese | 1393 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Korean | 1400 | Not measured | Only GLM 4.6 measured |
| LMArena Text: French | 1459 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Spanish | 1436 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Russian | 1418 | 1228 | GLM 4.6 |
| LMArena Text: Polish | 1447 | Not measured | Only GLM 4.6 measured |
| LMArena Text: multi-turn | 1427 | 1222 | GLM 4.6 |
| LMArena Text: longer queries | 1422 | 1251 | GLM 4.6 |
| LMArena Text: writing and language | 1415 | 1191 | GLM 4.6 |
| LMArena Text: entertainment and media | 1409 | 1150 | GLM 4.6 |
| Neither model has a result on the other 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6 | Qwen2.5 Coder 32B 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
Qwen2.5 Coder 32B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen2.5 Coder 32B Instruct costs less
Agent loop
200K input + 10K output tokens
Same cost
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 4.6 | Qwen2.5 Coder 32B Instruct |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | Open weights |
| Released | September 30, 2025 | November 11, 2024 |
| Context window | 205K | 33K |
| Input $/Mtok | $0.60 | $0.66 |
| Output $/Mtok | $2.20 | $1.00 |
| Coding index | 8.8 | Not benchmarked |
| Independent measurements behind it | 2 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.6 better than Qwen2.5 Coder 32B Instruct for coding?
Only GLM 4.6 has a coding score, 8.8 on the Vibe Coding Index; Qwen2.5 Coder 32B Instruct has none, so the two cannot be ranked on coding. Pick Qwen2.5 Coder 32B Instruct for output tokens 2.2x cheaper ($1.00 vs $2.20 per million).
Which is cheaper, GLM 4.6 or Qwen2.5 Coder 32B Instruct?
GLM 4.6 costs $0.60 per million input tokens and $2.20 per million output tokens; Qwen2.5 Coder 32B Instruct costs $0.66 and $1.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.6 or Qwen2.5 Coder 32B Instruct?
GLM 4.6, with 205K tokens against 33K.
How many benchmarks is the GLM 4.6 vs Qwen2.5 Coder 32B Instruct comparison based on?
28 benchmarks have a result for both models as of October 22, 2026. GLM 4.6's coding index rests on 2 independent benchmarks and Qwen2.5 Coder 32B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.