GLM 5.2 vs Inkling
GLM 5.2 leads Inkling on coding, 61.1 to 31.5 on the Vibe Coding Index, at about the same output price ($4.40 vs $4.05 per million). Updated October 22, 2026; 84 benchmarks have a result for both.
GLM 5.2 or Inkling: which to pick
Pick GLM 5.2 for coding: it scores 61.1 on the Vibe Coding Index against 31.5 for Inkling, though on 4 benchmarks against 6.
Pick Inkling for output tokens 1.1x cheaper ($4.05 vs $4.40 per million) and a lower cost per solved task ($0.76 against $0.86).
- Shared results
- 84
- GLM 5.2 only
- 20
- Inkling only
- 22
- Like-for-like categories
- 8 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
GLM 5.2
GLM 5.2 scores 61.1 against 31.5.
4 and 6 benchmarks behind the scores
Intelligence
AA Intelligence Index
GLM 5.2
GLM 5.2 leads, 33.7 against 25.0.
Same board, both measured
Agentic work
AA Agentic Index
GLM 5.2
GLM 5.2 leads, 38.4 against 22.5.
Same board, both measured
Reasoning
GPQA Diamond
Inkling
Inkling leads, 87.1% against 85.6%.
Same board, both measured
Knowledge
MMLU Pro
GLM 5.2
GLM 5.2 leads, 86.7% against 86.3%.
Same board, both measured
Web design
Design Arena: websites
GLM 5.2
GLM 5.2 leads, 1285 against 1224.
Same board, both measured
General
Vals Index
No call
Only Inkling has a Vals Index result, 29.0%.
Directional only
Long documents
Listed context window
GLM 5.2
GLM 5.2 takes 1.05M tokens against 524K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Inkling
GLM 5.2 $0.0036, Inkling $0.0030 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Inkling
GLM 5.2 $0.86, Inkling $0.76.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5.2 61.1 from 4 benchmarks, Inkling 31.5 from 6 benchmarks.
- Widest category
- Coding, 17.9 points apart on average over 17 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): GLM 5.2 64.0% against Inkling 19.2%, 44.8 points apart on a 0 to 100 scale.
- Output price
- GLM 5.2 $4.40 against Inkling $4.05 per million output tokens, 1.1x apart.
- Context window
- GLM 5.2 takes 1.05M tokens, Inkling 524K.
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 | Inkling | Ties |
|---|---|---|---|---|
| Coding | 16 | 1 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 7 | 0 | 1 tie | |
| Reasoning | 8 | 3 | ||
| Math | 5 | 1 | ||
| Knowledge | 6 | 4 | ||
| Web design | 8 | 0 | ||
| General | 20 | 2 | ||
| All shared boards | 72 | 11 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index161
| Benchmark | GLM 5.2 | Inkling | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 64.0% | Only Inkling measured |
| LMArena WebDev | 1604 | 1414 | GLM 5.2 |
| AA Coding Index | 68.8 | 52.1 | GLM 5.2 |
| LiveCodeBench (Vals AI) | 69.5% | 85.5% | Inkling |
| SWE-bench Verified (Vals AI) | 82.8% | 77.6% | GLM 5.2 |
| Terminal-Bench 4 (Vals AI) | Not measured | 0.5% | Only Inkling measured |
| Vibe Code Bench (Vals AI) | 64.0% | 19.2% | GLM 5.2 |
| Code migration (Vals AI) | 37.9% | 11.8% | GLM 5.2 |
| ProgramBench (Vals AI) | 0.5% | 0.0% | GLM 5.2 |
| IOI (Vals AI) | Not measured | 14.9% | Only Inkling measured |
| Terminal-Bench 2.1 (Vals AI) | 67.8% | 47.6% | GLM 5.2 |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 7.3% | Only Inkling measured |
| SkillsBench (Vals AI) | 45.1% | 26.1% | GLM 5.2 |
| CyberBench (Vals AI) | Not measured | 66.8% | Only Inkling measured |
| SciCode | 51.2% | 47.0% | GLM 5.2 |
| Terminal-Bench Hard (Artificial Analysis run) | 50.8% | Not measured | Only GLM 5.2 measured |
| FrontierCode | 24.5% | 14.0% | GLM 5.2 |
| FrontierSWE | Not measured | 4.1% | Only Inkling measured |
| WeirdML | 70.1% | 32.3% | GLM 5.2 |
| ALE-Bench | 1047.0 | 946.0 | GLM 5.2 |
| LMArena Text: coding | 1487 | 1466 | GLM 5.2 |
| LMArena Text: software and IT | 1486 | 1465 | GLM 5.2 |
| LMArena WebDev: React | 1617 | 1404 | GLM 5.2 |
| LMArena WebDev: HTML | 1565 | 1399 | GLM 5.2 |
| SWE-Bench Pro V2 | Not measured | 89.9% | Only Inkling measured |
| SWE Atlas: Codebase QnA | 48.1% | Not measured | Only GLM 5.2 measured |
| SWE Atlas: Refactoring | 42.4% | Not measured | Only GLM 5.2 measured |
| SWE Atlas: Test Writing | 41.5% | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 15 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5.2 | Inkling | Leader |
|---|---|---|---|
| AA Intelligence Index | 33.7 | 25.0 | GLM 5.2 |
| Epoch Capabilities Index | 151.9 | 148.4 | GLM 5.2 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | GLM 5.2 | Inkling | Leader |
|---|---|---|---|
| AA Agentic Index | 38.4 | 22.5 | GLM 5.2 |
| Design Arena: agents building web apps | 1222 | 1196 | GLM 5.2 |
| 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 | 1177 | 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 | 1114 | Not measured | Only GLM 5.2 measured |
| Design Arena: PowerPoint slides | 1167 | Not measured | Only GLM 5.2 measured |
| LiveBench research | Not measured | 59.9% | Only Inkling measured |
| LiveBench browser | Not measured | 16.0% | Only Inkling measured |
| LiveBench long agentic | Not measured | 14.1% | Only Inkling measured |
| SREBench (Vals AI) | 0.0% | 0.0% | Tie |
| Finance Agent (Vals AI) | 49.7% | 46.6% | GLM 5.2 |
| Tax Agent Bench (Vals AI) | Not measured | 15.3% | Only Inkling measured |
| Legal Research Bench (Vals AI) | 31.3% | 28.4% | GLM 5.2 |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 3.8% | Only Inkling measured |
| Excel Modeling Benchmark (Vals AI) | 61.5% | 38.4% | GLM 5.2 |
| τ²-Bench Telecom | 99.1% | Not measured | Only GLM 5.2 measured |
| APEX-Agents | 45.2% | 33.8% | GLM 5.2 |
| PostTrainBench | 31.7% | Not measured | Only GLM 5.2 measured |
| Vending-Bench 2 | $8,314 | Not measured | Only GLM 5.2 measured |
| MCP Atlas | 77.8% | 76.0% | GLM 5.2 |
| Neither model has a result on the other 19 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index83
| Benchmark | GLM 5.2 | Inkling | Leader |
|---|---|---|---|
| GPQA Diamond | 85.6% | 87.1% | Inkling |
| LiveBench reasoning | Not measured | 78.3% | Only Inkling measured |
| MMMU Pro (Vals AI) | Not measured | 78.5% | Only Inkling measured |
| GPQA Diamond (Artificial Analysis run) | 89.5% | 87.2% | GLM 5.2 |
| Humanity's Last Exam (Artificial Analysis run) | 41.1% | 31.9% | GLM 5.2 |
| GPQA Diamond (Epoch AI run) | 91.9% | 88.3% | GLM 5.2 |
| ARC-AGI | 77.0% | 79.5% | Inkling |
| ARC-AGI-2 | 22.8% | 36.5% | Inkling |
| SimpleBench | 58.8% | 50.0% | GLM 5.2 |
| Chess puzzles | 22.0% | 21.0% | GLM 5.2 |
| Mystery game puzzles | 19.0% | Not measured | Only GLM 5.2 measured |
| EBR-bench | 9.5% | Not measured | Only GLM 5.2 measured |
| LMArena Text: hard prompts | 1481 | 1451 | GLM 5.2 |
| LMArena Text: expert | 1487 | 1466 | GLM 5.2 |
| LMArena Text: hard prompts in English | 1481 | 1448 | GLM 5.2 |
| Kagi LLM Benchmark | 62.6% | Not measured | Only GLM 5.2 measured |
| Humanity's Last Exam (Scale run) | Not measured | 11.2% | Only Inkling measured |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index51
| Benchmark | GLM 5.2 | Inkling | Leader |
|---|---|---|---|
| OTIS Mock AIME | 86.4% | 88.9% | Inkling |
| FrontierMath, tiers 1 to 3 | 59.2% | 33.3% | GLM 5.2 |
| FrontierMath, tier 4 | 29.3% | 4.9% | GLM 5.2 |
| ProofBench | 35.0% | 0.0% | GLM 5.2 |
| LMArena Text: math | 1483 | 1479 | GLM 5.2 |
| LMArena Text: mathematical work | 1489 | 1460 | GLM 5.2 |
| MathArena | 22.7% | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index64
| Benchmark | GLM 5.2 | Inkling | Leader |
|---|---|---|---|
| MMLU Pro | 86.7% | 86.3% | GLM 5.2 |
| LegalBench (Vals AI) | 84.1% | 83.0% | GLM 5.2 |
| CorpFin (Vals AI) | 66.1% | 68.6% | Inkling |
| TaxEval (Vals AI) | 73.3% | 75.3% | Inkling |
| MortgageTax (Vals AI) | Not measured | 64.0% | Only Inkling measured |
| MedCode (Vals AI) | 40.8% | 41.2% | Inkling |
| SimpleQA Verified | 34.2% | 40.3% | Inkling |
| LMArena Text: science | 1489 | 1455 | GLM 5.2 |
| LMArena Text: medicine and healthcare | 1475 | 1447 | GLM 5.2 |
| LMArena Text: legal and government | 1486 | 1448 | GLM 5.2 |
| LMArena Text: business and finance | 1451 | 1438 | GLM 5.2 |
| Neither model has a result on the other 9 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | GLM 5.2 | Inkling | Leader |
|---|---|---|---|
| Design Arena: websites | 1285 | 1224 | GLM 5.2 |
| Design Arena: UI components | 1294 | 1199 | GLM 5.2 |
| Design Arena: game dev | 1267 | 1165 | GLM 5.2 |
| Design Arena: data viz | 1286 | 1157 | GLM 5.2 |
| Design Arena: 3D | 1287 | 1160 | GLM 5.2 |
| Design Arena: all code categories | 1287 | 1205 | GLM 5.2 |
| Design Arena: SVG | 1213 | 1108 | GLM 5.2 |
| Design Arena: ASCII art | 1191 | 1089 | GLM 5.2 |
| LiveBench design | Not measured | 58.3% | Only Inkling measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index202
| Benchmark | GLM 5.2 | Inkling | Leader |
|---|---|---|---|
| Vals Index | Not measured | 29.0% | Only Inkling measured |
| LiveBench data analysis | Not measured | 72.8% | Only Inkling measured |
| Public Benefits Bench (Vals AI) | Not measured | 58.5% | Only Inkling measured |
| SAGE (Vals AI) | Not measured | 36.5% | Only Inkling measured |
| MedScribe (Vals AI) | 83.5% | 85.4% | Inkling |
| Vals Multimodal Index | Not measured | 49.4% | Only Inkling measured |
| IFBench | 73.3% | Not measured | Only GLM 5.2 measured |
| Long context reasoning (AA-LCR) | 78.3% | 77.3% | GLM 5.2 |
| DTBench | 93.6% | 87.5% | GLM 5.2 |
| LMCA | 45.8% | 37.6% | GLM 5.2 |
| Surface Evolver Bench | 55.6% | Not measured | Only GLM 5.2 measured |
| LMArena Text | 1470 | 1442 | GLM 5.2 |
| LMArena Text: instruction following | 1466 | 1425 | GLM 5.2 |
| LMArena Text: creative writing | 1463 | 1390 | GLM 5.2 |
| LMArena Text: English | 1475 | 1442 | GLM 5.2 |
| LMArena Text: non-English | 1459 | 1434 | GLM 5.2 |
| LMArena Text: Chinese | 1522 | 1493 | GLM 5.2 |
| LMArena Text: German | 1470 | 1445 | GLM 5.2 |
| LMArena Text: Japanese | 1450 | 1429 | GLM 5.2 |
| LMArena Text: Korean | 1440 | 1403 | GLM 5.2 |
| LMArena Text: French | 1479 | 1455 | GLM 5.2 |
| LMArena Text: Spanish | 1478 | 1449 | GLM 5.2 |
| LMArena Text: Russian | 1468 | 1430 | GLM 5.2 |
| LMArena Text: Polish | 1456 | 1450 | GLM 5.2 |
| LMArena Text: multi-turn | 1469 | 1437 | GLM 5.2 |
| LMArena Text: longer queries | 1480 | 1435 | GLM 5.2 |
| LMArena Text: writing and language | 1463 | 1404 | GLM 5.2 |
| LMArena Text: entertainment and media | 1450 | 1394 | GLM 5.2 |
| EQ-Bench 4 | 1222 | 1226 | Inkling |
| Neither model has a result on the other 19 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.2 | Inkling | 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
Inkling costs less
Repository review
50K input + 3K output tokens
Inkling costs less
Agent loop
200K input + 10K output tokens
Inkling 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 | Thinking Machines Inkling |
|---|---|---|
| Maker | Z.ai | Thinking Machines |
| Weights | Open weights | Open weights |
| Released | June 16, 2026 | July 17, 2026 |
| Context window | 1.05M | 524K |
| Input $/Mtok | $1.40 | $1.00 |
| Output $/Mtok | $4.40 | $4.05 |
| Coding index | 61.1 | 31.5 |
| Independent measurements behind it | 4 | 6 |
| Cost per solved task | $0.86 | $0.76 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.2 better than Inkling for coding?
Pick GLM 5.2 for coding: it scores 61.1 on the Vibe Coding Index against 31.5 for Inkling, though on 4 benchmarks against 6. Pick Inkling for output tokens 1.1x cheaper ($4.05 vs $4.40 per million) and a lower cost per solved task ($0.76 against $0.86).
Which is cheaper, GLM 5.2 or Inkling?
GLM 5.2 costs $1.40 per million input tokens and $4.40 per million output tokens; Inkling costs $1.00 and $4.05. Per solved SWE-bench Verified task, GLM 5.2 costs $0.86 and Inkling $0.76.
Which has the bigger context window, GLM 5.2 or Inkling?
GLM 5.2, with 1.05M tokens against 524K.
How many benchmarks is the GLM 5.2 vs Inkling comparison based on?
84 benchmarks have a result for both models as of October 22, 2026. GLM 5.2's coding index rests on 4 independent benchmarks and Inkling's on 6. Where only one model has a result, the page says so instead of naming a winner.