GLM 4.6V vs Qwen2.5 Coder 32B Instruct
Updated October 21, 2026. 25 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
GLM 4.6V has the edge where output tokens cost 1.1x less, and where the context window is larger, 131K against 33K tokens.
- Shared results
- 25
- GLM 4.6V only
- 13
- Qwen2.5 Coder 32B Instruct only
- 10
- Like-for-like categories
- 5 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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 4.6V
GLM 4.6V leads, 11.2 against 6.7.
Same board, both measured
Long documents
Listed context window
GLM 4.6V
GLM 4.6V takes 131K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.6V
GLM 4.6V $0.0008, Qwen2.5 Coder 32B Instruct $0.0012 per turn.
List prices
At a glance
- Coding index
- GLM 4.6V not benchmarked, Qwen2.5 Coder 32B Instruct not benchmarked.
- Widest category
- General, 25.1 points apart on average over 11 shared benchmarks.
- Widest single gap
- LMArena Text: medicine and healthcare: GLM 4.6V 1389 against Qwen2.5 Coder 32B Instruct 1187, 33.8 points apart on a 0 to 100 scale.
- Output price
- GLM 4.6V $0.90 against Qwen2.5 Coder 32B Instruct $1.00 per million output tokens, 1.1x apart.
- Context window
- GLM 4.6V takes 131K 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 4.6V | Bars | Qwen2.5 Coder 32B Instruct | Ties |
|---|---|---|---|---|
| Coding | 3 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 5 | 0 | ||
| Knowledge | 5 | 0 | ||
| General | 11 | 0 | ||
| All shared boards | 25 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
| Benchmark | GLM 4.6V | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 16.4% | Only Qwen2.5 Coder 32B Instruct measured |
| SWE-bench Verified | Not measured | 47.0% | Only Qwen2.5 Coder 32B Instruct measured |
| LiveCodeBench (Artificial Analysis run) | 41.1% | 29.5% | GLM 4.6V |
| Terminal-Bench Hard (Artificial Analysis run) | 14.4% | Not measured | Only GLM 4.6V measured |
| LMArena Text: coding | 1389 | 1276 | GLM 4.6V |
| LMArena Text: software and IT | 1391 | 1261 | GLM 4.6V |
| Neither model has a result on the other 32 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.6V | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.2 | 6.7 | GLM 4.6V |
| 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 index1 board, none shared
| Benchmark | GLM 4.6V | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 31.6% | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 4.6V | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 71.9% | 41.7% | GLM 4.6V |
| Humanity's Last Exam (Artificial Analysis run) | 9.6% | 3.5% | GLM 4.6V |
| LMArena Text: hard prompts | 1367 | 1251 | GLM 4.6V |
| LMArena Text: expert | 1371 | 1221 | GLM 4.6V |
| LMArena Text: hard prompts in English | 1391 | 1269 | GLM 4.6V |
| LMArena Vision: diagrams | 1182 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: homework | 1155 | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 14 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GLM 4.6V | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| AA Math Index | 85.3 | Not measured | Only GLM 4.6V measured |
| AIME 2025 (Artificial Analysis run) | 85.3% | Not measured | Only GLM 4.6V measured |
| GSM8K | Not measured | 93.0% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: math | Not measured | 1251 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: mathematical work | Not measured | 1243 | Only Qwen2.5 Coder 32B Instruct measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 4.6V | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 79.9% | 63.5% | GLM 4.6V |
| 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 | 1389 | 1221 | GLM 4.6V |
| LMArena Text: medicine and healthcare | 1389 | 1187 | GLM 4.6V |
| LMArena Text: legal and government | 1393 | 1257 | GLM 4.6V |
| LMArena Text: business and finance | 1359 | 1224 | GLM 4.6V |
| Neither model has a result on the other 10 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6V | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index110
| Benchmark | GLM 4.6V | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| IFBench | 30.1% | Not measured | Only GLM 4.6V measured |
| Long context reasoning (AA-LCR) | 48.7% | Not measured | Only GLM 4.6V 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 | 1376 | 1230 | GLM 4.6V |
| LMArena Text: instruction following | 1350 | 1223 | GLM 4.6V |
| LMArena Text: creative writing | 1347 | 1174 | GLM 4.6V |
| LMArena Vision | 1164 | Not measured | Only GLM 4.6V measured |
| LMArena Text: English | 1395 | 1248 | GLM 4.6V |
| LMArena Text: non-English | 1358 | 1205 | GLM 4.6V |
| LMArena Text: Chinese | 1425 | 1222 | GLM 4.6V |
| LMArena Text: Russian | 1339 | 1228 | GLM 4.6V |
| LMArena Text: multi-turn | 1359 | 1222 | GLM 4.6V |
| LMArena Text: longer queries | 1357 | 1251 | GLM 4.6V |
| LMArena Text: writing and language | 1347 | 1191 | GLM 4.6V |
| LMArena Text: entertainment and media | 1343 | 1150 | GLM 4.6V |
| LMArena Vision: English | 1179 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: Chinese | 1217 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: reading text | 1171 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: creative writing | 1113 | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6V | 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
GLM 4.6V costs less
Repository review
50K input + 3K output tokens
GLM 4.6V costs less
Agent loop
200K input + 10K output tokens
GLM 4.6V 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 4.6V | Qwen2.5 Coder 32B Instruct |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | Open weights |
| Released | December 8, 2025 | November 11, 2024 |
| Context window | 131K | 33K |
| Input $/Mtok | $0.30 | $0.66 |
| Output $/Mtok | $0.90 | $1.00 |
| 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 4.6V better than Qwen2.5 Coder 32B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. GLM 4.6V has the edge where output tokens cost 1.1x less, and where the context window is larger, 131K against 33K tokens.
Which is cheaper, GLM 4.6V or Qwen2.5 Coder 32B Instruct?
GLM 4.6V costs $0.30 per million input tokens and $0.90 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 4.6V or Qwen2.5 Coder 32B Instruct?
GLM 4.6V, with 131K tokens against 33K.
How many benchmarks is the GLM 4.6V vs Qwen2.5 Coder 32B Instruct comparison based on?
25 benchmarks have a result for both models as of October 21, 2026. GLM 4.6V's coding index rests on 0 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.