GLM 4.6 vs Qwen2.5 VL 72B Instruct
Qwen2.5 VL 72B Instruct costs 2.2x less per output token than GLM 4.6 ($1.00 vs $2.20 per million). Updated October 22, 2026; 1 benchmarks have a result for both.
GLM 4.6 or Qwen2.5 VL 72B Instruct: which to pick
Only GLM 4.6 has a coding score, 8.8 on the Vibe Coding Index; Qwen2.5 VL 72B Instruct has none, so the two cannot be ranked on coding.
Pick Qwen2.5 VL 72B Instruct for output tokens 2.2x cheaper ($1.00 vs $2.20 per million).
- Shared results
- 1
- GLM 4.6 only
- 68
- Qwen2.5 VL 72B Instruct only
- 5
- Like-for-like categories
- 1 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
No call
Only GLM 4.6 has a AA Intelligence Index result, 18.5.
Directional only
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 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen2.5 VL 72B Instruct
GLM 4.6 $0.0017, Qwen2.5 VL 72B Instruct $0.0013 per turn.
List prices
At a glance
- Coding index
- GLM 4.6 8.8 from 2 benchmarks, Qwen2.5 VL 72B Instruct not benchmarked.
- Widest category
- Reasoning, 9.7 points apart on average over 1 shared benchmark.
- Widest single gap
- Kagi LLM Benchmark: GLM 4.6 45.7% against Qwen2.5 VL 72B Instruct 36.0%, 9.7 points apart on a 0 to 100 scale.
- Output price
- GLM 4.6 $2.20 against Qwen2.5 VL 72B Instruct $1.00 per million output tokens, 2.2x apart.
- Context window
- GLM 4.6 takes 205K tokens, Qwen2.5 VL 72B Instruct 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 4.6 | Bars | Qwen2.5 VL 72B Instruct | Ties |
|---|---|---|---|---|
| Reasoning | 1 | 0 | ||
| All shared boards | 1 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index12 boards, none shared
| Benchmark | GLM 4.6 | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| SWE-bench Verified | 68.2% | Not measured | Only GLM 4.6 measured |
| 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% | Not measured | Only GLM 4.6 measured |
| 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 | Not measured | Only GLM 4.6 measured |
| LMArena Text: software and IT | 1455 | Not measured | Only GLM 4.6 measured |
| LMArena WebDev: HTML | 1350 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 27 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GLM 4.6 | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 18.5 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GLM 4.6 | Qwen2.5 VL 72B 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 |
| OSWorld | Not measured | 5.0% | Only Qwen2.5 VL 72B Instruct 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 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.6 | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond | 74.5% | Not measured | Only GLM 4.6 measured |
| GPQA Diamond (Artificial Analysis run) | 78.0% | Not measured | Only GLM 4.6 measured |
| Humanity's Last Exam (Artificial Analysis run) | 14.5% | Not measured | Only GLM 4.6 measured |
| LMArena Text: hard prompts | 1440 | Not measured | Only GLM 4.6 measured |
| LMArena Text: expert | 1432 | Not measured | Only GLM 4.6 measured |
| LMArena Text: hard prompts in English | 1445 | Not measured | Only GLM 4.6 measured |
| Kagi LLM Benchmark | 45.7% | 36.0% | GLM 4.6 |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GLM 4.6 | Qwen2.5 VL 72B 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 |
| LMArena Text: math | 1432 | Not measured | Only GLM 4.6 measured |
| LMArena Text: mathematical work | 1447 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | GLM 4.6 | Qwen2.5 VL 72B 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% | Not measured | Only GLM 4.6 measured |
| GeoBench | Not measured | 62.0% | Only Qwen2.5 VL 72B Instruct measured |
| LMArena Text: science | 1456 | Not measured | Only GLM 4.6 measured |
| LMArena Text: medicine and healthcare | 1455 | Not measured | Only GLM 4.6 measured |
| LMArena Text: legal and government | 1448 | Not measured | Only GLM 4.6 measured |
| LMArena Text: business and finance | 1416 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GLM 4.6 | Qwen2.5 VL 72B 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 | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index22 boards, none shared
| Benchmark | GLM 4.6 | Qwen2.5 VL 72B 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 |
| LMArena Text | 1440 | Not measured | Only GLM 4.6 measured |
| LMArena Text: instruction following | 1410 | Not measured | Only GLM 4.6 measured |
| LMArena Text: creative writing | 1412 | Not measured | Only GLM 4.6 measured |
| LMArena Vision | Not measured | 1107 | Only Qwen2.5 VL 72B Instruct measured |
| LMArena Text: English | 1449 | Not measured | Only GLM 4.6 measured |
| LMArena Text: non-English | 1426 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Chinese | 1499 | Not measured | Only GLM 4.6 measured |
| 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 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Polish | 1447 | Not measured | Only GLM 4.6 measured |
| LMArena Text: multi-turn | 1427 | Not measured | Only GLM 4.6 measured |
| LMArena Text: longer queries | 1422 | Not measured | Only GLM 4.6 measured |
| LMArena Text: writing and language | 1415 | Not measured | Only GLM 4.6 measured |
| LMArena Text: entertainment and media | 1409 | Not measured | Only GLM 4.6 measured |
| LMArena Vision: English | Not measured | 1115 | Only Qwen2.5 VL 72B Instruct measured |
| LMArena Vision: Chinese | Not measured | 1107 | Only Qwen2.5 VL 72B Instruct measured |
| Neither model has a result on the other 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6 | Qwen2.5 VL 72B 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 VL 72B Instruct costs less
Repository review
50K input + 3K output tokens
GLM 4.6 costs less
Agent loop
200K input + 10K output tokens
GLM 4.6 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 4.6 | Qwen2.5 VL 72B Instruct |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | Open weights |
| Released | September 30, 2025 | February 1, 2025 |
| Context window | 205K | 128K |
| Input $/Mtok | $0.60 | $0.80 |
| 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 VL 72B Instruct for coding?
Only GLM 4.6 has a coding score, 8.8 on the Vibe Coding Index; Qwen2.5 VL 72B Instruct has none, so the two cannot be ranked on coding. Pick Qwen2.5 VL 72B Instruct for output tokens 2.2x cheaper ($1.00 vs $2.20 per million).
Which is cheaper, GLM 4.6 or Qwen2.5 VL 72B Instruct?
GLM 4.6 costs $0.60 per million input tokens and $2.20 per million output tokens; Qwen2.5 VL 72B Instruct costs $0.80 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 VL 72B Instruct?
GLM 4.6, with 205K tokens against 128K.
How many benchmarks is the GLM 4.6 vs Qwen2.5 VL 72B Instruct comparison based on?
1 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 VL 72B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.