GLM 5 Turbo vs Qwen2.5 VL 72B Instruct
Qwen2.5 VL 72B Instruct costs 4.0x less per output token than GLM 5 Turbo ($1.00 vs $4.00 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $4.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $1.00
GLM 5 Turbo or Qwen2.5 VL 72B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 5 Turbo for a larger context window (203K against 128K tokens).
No benchmark has tested both yet. GLM 5 Turbo has results on Terminal-Bench Hard (Artificial Analysis run), ALE-Bench and AA Intelligence Index; Qwen2.5 VL 72B Instruct on OSWorld, Kagi LLM Benchmark and GeoBench. Compare them on price and context instead.
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
No call
Only GLM 5 Turbo has a AA Intelligence Index result, 26.6.
Directional only
Web design
Design Arena: websites
No call
Only GLM 5 Turbo has a Design Arena: websites result, 1262.
Directional only
Long documents
Listed context window
GLM 5 Turbo
GLM 5 Turbo takes 203K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen2.5 VL 72B Instruct
GLM 5 Turbo $0.0032, Qwen2.5 VL 72B Instruct $0.0013 per turn.
List prices
At a glance
- Coding index
- GLM 5 Turbo not benchmarked, Qwen2.5 VL 72B Instruct not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- GLM 5 Turbo $4.00 against Qwen2.5 VL 72B Instruct $1.00 per million output tokens, 4.0x apart.
- Context window
- GLM 5 Turbo takes 203K tokens, Qwen2.5 VL 72B Instruct 128K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index2 boards measured
| Benchmark | GLM 5 Turbo | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| Terminal-Bench Hard (Artificial Analysis run) | 33.3% | Not measured | Only GLM 5 Turbo measured |
| ALE-Bench | 634.0 | Not measured | Only GLM 5 Turbo measured |
| Neither model has a result on the other 41 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GLM 5 Turbo | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 26.6 | Not measured | Only GLM 5 Turbo measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GLM 5 Turbo | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 98.5% | Not measured | Only GLM 5 Turbo measured |
| OSWorld | Not measured | 5.0% | Only Qwen2.5 VL 72B Instruct measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | GLM 5 Turbo | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 84.7% | Not measured | Only GLM 5 Turbo measured |
| Humanity's Last Exam (Artificial Analysis run) | 27.8% | Not measured | Only GLM 5 Turbo measured |
| Kagi LLM Benchmark | Not measured | 36.0% | Only Qwen2.5 VL 72B Instruct measured |
| Neither model has a result on the other 20 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GLM 5 Turbo | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| GeoBench | Not measured | 62.0% | Only Qwen2.5 VL 72B Instruct measured |
| Neither model has a result on the other 19 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards measured
| Benchmark | GLM 5 Turbo | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 1262 | Not measured | Only GLM 5 Turbo measured |
| Design Arena: UI components | 1260 | Not measured | Only GLM 5 Turbo measured |
| Design Arena: game dev | 1248 | Not measured | Only GLM 5 Turbo measured |
| Design Arena: data viz | 1264 | Not measured | Only GLM 5 Turbo measured |
| Design Arena: 3D | 1247 | Not measured | Only GLM 5 Turbo measured |
| Design Arena: all code categories | 1261 | Not measured | Only GLM 5 Turbo measured |
| Design Arena: SVG | 1205 | Not measured | Only GLM 5 Turbo measured |
| Design Arena: ASCII art | 1160 | Not measured | Only GLM 5 Turbo measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | GLM 5 Turbo | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| IFBench | 73.2% | Not measured | Only GLM 5 Turbo measured |
| Long context reasoning (AA-LCR) | 71.7% | Not measured | Only GLM 5 Turbo measured |
| LMArena Vision | Not measured | 1107 | Only Qwen2.5 VL 72B Instruct 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 43 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
Qwen2.5 VL 72B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen2.5 VL 72B Instruct 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 Turbo | Qwen2.5 VL 72B Instruct |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | No public weights listed | Open weights |
| Released | March 15, 2026 | February 1, 2025 |
| Context window | 203K | 128K |
| Input $/Mtok | $1.20 | $0.80 |
| Output $/Mtok | $4.00 | $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 5 Turbo better than Qwen2.5 VL 72B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 5 Turbo for a larger context window (203K against 128K tokens).
Which is cheaper, GLM 5 Turbo or Qwen2.5 VL 72B Instruct?
GLM 5 Turbo costs $1.20 per million input tokens and $4.00 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 5 Turbo or Qwen2.5 VL 72B Instruct?
GLM 5 Turbo, with 203K tokens against 128K.
Has any benchmark tested both GLM 5 Turbo and Qwen2.5 VL 72B Instruct?
No benchmark has tested both yet. GLM 5 Turbo has results on Terminal-Bench Hard (Artificial Analysis run), ALE-Bench and AA Intelligence Index; Qwen2.5 VL 72B Instruct on OSWorld, Kagi LLM Benchmark and GeoBench. Compare them on price and context instead. Data as of October 22, 2026.