GLM 5.2 vs Qwen2.5 72B Instruct
GLM 5.2 leads Qwen2.5 72B Instruct on the Artificial Analysis Intelligence Index, 33.7 to 7.7, and Qwen2.5 72B Instruct costs 11.0x less per output token ($0.40 vs $4.40 per million). Updated October 22, 2026; 39 benchmarks have a result for both.
GLM 5.2 or Qwen2.5 72B Instruct: which to pick
Only GLM 5.2 has a coding score, 61.1 on the Vibe Coding Index; Qwen2.5 72B Instruct has none, so the two cannot be ranked on coding.
Pick Qwen2.5 72B Instruct for output tokens 11.0x cheaper ($0.40 vs $4.40 per million).
- Shared results
- 39
- GLM 5.2 only
- 59
- Qwen2.5 72B Instruct only
- 14
- Like-for-like categories
- 7 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 5.2 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GLM 5.2
GLM 5.2 leads, 33.7 against 7.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GLM 5.2 has a AA Agentic Index result, 38.4.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 5.2 has a GPQA Diamond result, 85.6%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5.2 has a MMLU Pro result, 86.7%.
Directional only
Web design
Design Arena: websites
No call
Only GLM 5.2 has a Design Arena: websites result, 1285.
Directional only
Long documents
Listed context window
GLM 5.2
GLM 5.2 takes 1.05M tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen2.5 72B Instruct
GLM 5.2 $0.0036, Qwen2.5 72B Instruct $0.0006 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5.2 has the number, $0.86.
Directional only
At a glance
- Coding index
- GLM 5.2 61.1 from 4 benchmarks, Qwen2.5 72B Instruct not benchmarked.
- Widest category
- Agentic work, 64.6 points apart on average over 1 shared benchmark.
- Widest single gap
- OTIS Mock AIME: GLM 5.2 86.4% against Qwen2.5 72B Instruct 8.1%, 78.3 points apart on a 0 to 100 scale.
- Output price
- GLM 5.2 $4.40 against Qwen2.5 72B Instruct $0.40 per million output tokens, 11.0x apart.
- Context window
- GLM 5.2 takes 1.05M tokens, Qwen2.5 72B 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 5.2 | Bars | Qwen2.5 72B Instruct | Ties |
|---|---|---|---|---|
| Coding | 4 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 6 | 0 | ||
| Math | 3 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 19 | 0 | ||
| All shared boards | 39 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
| Benchmark | GLM 5.2 | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| LMArena WebDev | 1604 | Not measured | Only GLM 5.2 measured |
| AA Coding Index | 68.8 | Not measured | Only GLM 5.2 measured |
| LiveCodeBench (Vals AI) | 69.5% | Not measured | Only GLM 5.2 measured |
| SWE-bench Verified (Vals AI) | 82.8% | Not measured | Only GLM 5.2 measured |
| Vibe Code Bench (Vals AI) | 64.0% | Not measured | Only GLM 5.2 measured |
| Code migration (Vals AI) | 37.9% | Not measured | Only GLM 5.2 measured |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only GLM 5.2 measured |
| Terminal-Bench 2.1 (Vals AI) | 67.8% | Not measured | Only GLM 5.2 measured |
| SkillsBench (Vals AI) | 45.1% | Not measured | Only GLM 5.2 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 27.6% | Only Qwen2.5 72B Instruct measured |
| SciCode | 51.2% | Not measured | Only GLM 5.2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 50.8% | 4.5% | GLM 5.2 |
| FrontierCode | 24.5% | Not measured | Only GLM 5.2 measured |
| WeirdML | 70.1% | 16.0% | GLM 5.2 |
| ALE-Bench | 1047.0 | Not measured | Only GLM 5.2 measured |
| LMArena Text: coding | 1487 | 1292 | GLM 5.2 |
| LMArena Text: software and IT | 1486 | 1292 | GLM 5.2 |
| LMArena WebDev: React | 1617 | Not measured | Only GLM 5.2 measured |
| LMArena WebDev: HTML | 1565 | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 20 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5.2 | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 33.7 | 7.7 | GLM 5.2 |
| Epoch Capabilities Index | 151.9 | 129.0 | GLM 5.2 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 5.2 | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| AA Agentic Index | 38.4 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building web apps | 1222 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building full-stack apps | 1241 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building mobile apps | 1159 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building Android apps | 1178 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building Godot games | 1142 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building games | 1206 | Not measured | Only GLM 5.2 measured |
| Design Arena: HTML slides | 1125 | Not measured | Only GLM 5.2 measured |
| Design Arena: PowerPoint slides | 1170 | Not measured | Only GLM 5.2 measured |
| SREBench (Vals AI) | 0.0% | Not measured | Only GLM 5.2 measured |
| Finance Agent (Vals AI) | 49.7% | Not measured | Only GLM 5.2 measured |
| Legal Research Bench (Vals AI) | 31.3% | Not measured | Only GLM 5.2 measured |
| Excel Modeling Benchmark (Vals AI) | 61.5% | Not measured | Only GLM 5.2 measured |
| τ²-Bench Telecom | 99.1% | 34.5% | GLM 5.2 |
| APEX-Agents | 45.2% | Not measured | Only GLM 5.2 measured |
| METR time horizons | Not measured | 35.8% | Only Qwen2.5 72B Instruct measured |
| BALROG | Not measured | 16.2% | Only Qwen2.5 72B Instruct measured |
| PostTrainBench | 31.7% | Not measured | Only GLM 5.2 measured |
| TheAgentCompany | Not measured | 5.7% | Only Qwen2.5 72B Instruct measured |
| Vending-Bench 2 | $8,314 | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 21 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GLM 5.2 | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond | 85.6% | Not measured | Only GLM 5.2 measured |
| GPQA Diamond (Artificial Analysis run) | 89.5% | 49.1% | GLM 5.2 |
| Humanity's Last Exam (Artificial Analysis run) | 41.1% | 3.6% | GLM 5.2 |
| GPQA Diamond (Epoch AI run) | 91.9% | 49.1% | GLM 5.2 |
| ARC-AGI | 77.0% | Not measured | Only GLM 5.2 measured |
| ARC-AGI-2 | 22.8% | Not measured | Only GLM 5.2 measured |
| SimpleBench | 58.8% | Not measured | Only GLM 5.2 measured |
| Chess puzzles | 22.0% | Not measured | Only GLM 5.2 measured |
| Mystery game puzzles | 19.0% | Not measured | Only GLM 5.2 measured |
| EBR-bench | 9.5% | Not measured | Only GLM 5.2 measured |
| BIG-Bench Hard | Not measured | 79.8% | Only Qwen2.5 72B Instruct measured |
| LMArena Text: hard prompts | 1481 | 1271 | GLM 5.2 |
| LMArena Text: expert | 1487 | 1245 | GLM 5.2 |
| LMArena Text: hard prompts in English | 1481 | 1279 | GLM 5.2 |
| Kagi LLM Benchmark | 62.6% | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 7 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GLM 5.2 | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 14.0 | Only Qwen2.5 72B Instruct measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 14.0% | Only Qwen2.5 72B Instruct measured |
| OTIS Mock AIME | 86.4% | 8.1% | GLM 5.2 |
| FrontierMath, tiers 1 to 3 | 59.2% | Not measured | Only GLM 5.2 measured |
| FrontierMath, tier 4 | 29.3% | Not measured | Only GLM 5.2 measured |
| ProofBench | 35.0% | Not measured | Only GLM 5.2 measured |
| MATH level 5 | Not measured | 63.2% | Only Qwen2.5 72B Instruct measured |
| LMArena Text: math | 1483 | 1283 | GLM 5.2 |
| LMArena Text: mathematical work | 1489 | 1290 | GLM 5.2 |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 5.2 | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| MMLU Pro | 86.7% | Not measured | Only GLM 5.2 measured |
| LegalBench (Vals AI) | 84.1% | Not measured | Only GLM 5.2 measured |
| CorpFin (Vals AI) | 66.1% | Not measured | Only GLM 5.2 measured |
| TaxEval (Vals AI) | 73.3% | Not measured | Only GLM 5.2 measured |
| MedCode (Vals AI) | 40.8% | Not measured | Only GLM 5.2 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 72.0% | Only Qwen2.5 72B Instruct measured |
| SimpleQA Verified | 34.2% | Not measured | Only GLM 5.2 measured |
| MMLU | Not measured | 85.3% | Only Qwen2.5 72B Instruct measured |
| AI2 Reasoning Challenge | Not measured | 94.5% | Only Qwen2.5 72B Instruct measured |
| LMArena Text: science | 1489 | 1275 | GLM 5.2 |
| LMArena Text: medicine and healthcare | 1475 | 1258 | GLM 5.2 |
| LMArena Text: legal and government | 1486 | 1288 | GLM 5.2 |
| LMArena Text: business and finance | 1451 | 1253 | GLM 5.2 |
| Neither model has a result on the other 5 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GLM 5.2 | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 1285 | Not measured | Only GLM 5.2 measured |
| Design Arena: UI components | 1294 | Not measured | Only GLM 5.2 measured |
| Design Arena: game dev | 1268 | Not measured | Only GLM 5.2 measured |
| Design Arena: data viz | 1285 | Not measured | Only GLM 5.2 measured |
| Design Arena: 3D | 1287 | Not measured | Only GLM 5.2 measured |
| Design Arena: all code categories | 1287 | Not measured | Only GLM 5.2 measured |
| Design Arena: SVG | 1213 | Not measured | Only GLM 5.2 measured |
| Design Arena: ASCII art | 1191 | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index190
| Benchmark | GLM 5.2 | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 83.5% | Not measured | Only GLM 5.2 measured |
| IFBench | 73.3% | 36.9% | GLM 5.2 |
| Long context reasoning (AA-LCR) | 78.3% | Not measured | Only GLM 5.2 measured |
| DTBench | 93.6% | 62.9% | GLM 5.2 |
| LMCA | 45.8% | 13.4% | GLM 5.2 |
| Surface Evolver Bench | 55.6% | Not measured | Only GLM 5.2 measured |
| HellaSwag | Not measured | 84.8% | Only Qwen2.5 72B Instruct measured |
| WinoGrande | Not measured | 82.3% | Only Qwen2.5 72B Instruct measured |
| PIQA | Not measured | 82.6% | Only Qwen2.5 72B Instruct measured |
| LMArena Text | 1470 | 1269 | GLM 5.2 |
| LMArena Text: instruction following | 1466 | 1254 | GLM 5.2 |
| LMArena Text: creative writing | 1463 | 1221 | GLM 5.2 |
| LMArena Text: English | 1475 | 1283 | GLM 5.2 |
| LMArena Text: non-English | 1459 | 1252 | GLM 5.2 |
| LMArena Text: Chinese | 1522 | 1273 | GLM 5.2 |
| LMArena Text: German | 1470 | 1234 | GLM 5.2 |
| LMArena Text: Japanese | 1450 | 1180 | GLM 5.2 |
| LMArena Text: Korean | 1440 | 1188 | GLM 5.2 |
| LMArena Text: French | 1479 | 1280 | GLM 5.2 |
| LMArena Text: Spanish | 1478 | 1255 | GLM 5.2 |
| LMArena Text: Russian | 1468 | 1264 | GLM 5.2 |
| LMArena Text: Polish | 1456 | Not measured | Only GLM 5.2 measured |
| LMArena Text: multi-turn | 1469 | 1272 | GLM 5.2 |
| LMArena Text: longer queries | 1480 | 1281 | GLM 5.2 |
| LMArena Text: writing and language | 1463 | 1247 | GLM 5.2 |
| LMArena Text: entertainment and media | 1450 | 1211 | GLM 5.2 |
| Neither model has a result on the other 20 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.2 | Qwen2.5 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 72B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen2.5 72B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen2.5 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 5.2 | Qwen2.5 72B Instruct |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | Open weights |
| Released | June 16, 2026 | September 19, 2024 |
| Context window | 1.05M | 33K |
| Input $/Mtok | $1.40 | $0.36 |
| Output $/Mtok | $4.40 | $0.40 |
| Coding index | 61.1 | Not benchmarked |
| Independent measurements behind it | 4 | 0 |
| Cost per solved task | $0.86 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.2 better than Qwen2.5 72B Instruct for coding?
Only GLM 5.2 has a coding score, 61.1 on the Vibe Coding Index; Qwen2.5 72B Instruct has none, so the two cannot be ranked on coding. Pick Qwen2.5 72B Instruct for output tokens 11.0x cheaper ($0.40 vs $4.40 per million).
Which is cheaper, GLM 5.2 or Qwen2.5 72B Instruct?
GLM 5.2 costs $1.40 per million input tokens and $4.40 per million output tokens; Qwen2.5 72B Instruct costs $0.36 and $0.40. Cost per solved task is known for GLM 5.2 only.
Which has the bigger context window, GLM 5.2 or Qwen2.5 72B Instruct?
GLM 5.2, with 1.05M tokens against 33K.
How many benchmarks is the GLM 5.2 vs Qwen2.5 72B Instruct comparison based on?
39 benchmarks have a result for both models as of October 22, 2026. GLM 5.2's coding index rests on 4 independent benchmarks and Qwen2.5 72B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.