GLM 4.7 vs Inkling
Updated October 21, 2026. 61 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick Inkling for coding: it scores 49.7 on the Vibe Coding Index against 33.6 for GLM 4.7.
Pick GLM 4.7 when output tokens cost 2.0x less, or when a solved task costs $0.29 against $0.76.
- Shared results
- 61
- GLM 4.7 only
- 19
- Inkling only
- 41
- Like-for-like categories
- 7 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, four public coding benchmarks
Inkling
Inkling scores 49.7 against 33.6.
1 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
Inkling
Inkling leads, 25.0 against 22.2.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Inkling has a AA Agentic Index result, 22.5.
Directional only
Reasoning
GPQA Diamond
Inkling
Inkling leads, 87.1% against 80.0%.
Same board, both measured
Math
AIME
No call
Only GLM 4.7 has a AIME result, 93.3%.
Directional only
Knowledge
MMLU Pro
Inkling
Inkling leads, 86.3% against 82.7%.
Same board, both measured
Web design
Design Arena: websites
GLM 4.7
GLM 4.7 leads, 1226 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
Inkling
Inkling takes 524K tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.7
GLM 4.7 $0.0015, Inkling $0.0030 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 4.7
GLM 4.7 $0.29, Inkling $0.76.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 4.7 33.6 from 1 benchmark, Inkling 49.7 from 2 benchmarks.
- Widest category
- Math, 7.2 points apart on average over 4 shared benchmarks.
- Widest single gap
- CorpFin (Vals AI): GLM 4.7 46.4% against Inkling 68.6%, 22.2 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 $1.98 against Inkling $4.05 per million output tokens, 2.0x apart.
- Context window
- GLM 4.7 takes 205K 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 4.7 | Bars | Inkling | Ties |
|---|---|---|---|---|
| Coding | 3 | 6 | ||
| Intelligence | 0 | 2 | ||
| Reasoning | 1 | 7 | 1 tie | |
| Math | 1 | 3 | ||
| Knowledge | 3 | 7 | ||
| Web design | 7 | 1 | ||
| General | 7 | 12 | ||
| All shared boards | 22 | 38 | 1 tie |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index36
| Benchmark | GLM 4.7 | Inkling | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 64.0% | Only Inkling measured |
| LMArena WebDev | 1435 | 1414 | GLM 4.7 |
| AA Coding Index | 45.3 | 52.1 | Inkling |
| LiveCodeBench (Vals AI) | 82.2% | 85.5% | Inkling |
| SWE-bench Verified (Vals AI) | 69.4% | 77.6% | Inkling |
| Terminal-Bench 4 (Vals AI) | Not measured | 0.5% | Only Inkling measured |
| Vibe Code Bench (Vals AI) | Not measured | 19.2% | Only Inkling measured |
| Code migration (Vals AI) | Not measured | 11.8% | Only Inkling measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only Inkling measured |
| IOI (Vals AI) | Not measured | 14.9% | Only Inkling measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 47.6% | Only Inkling measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 7.3% | Only Inkling measured |
| SkillsBench (Vals AI) | Not measured | 26.1% | Only Inkling measured |
| CyberBench (Vals AI) | Not measured | 66.8% | Only Inkling measured |
| LiveCodeBench (Artificial Analysis run) | 89.4% | Not measured | Only GLM 4.7 measured |
| SciCode | Not measured | 47.0% | Only Inkling measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.8% | Not measured | Only GLM 4.7 measured |
| Terminal-Bench | 33.4% | Not measured | Only GLM 4.7 measured |
| FrontierCode | Not measured | 14.0% | Only Inkling measured |
| FrontierSWE | Not measured | 4.1% | Only Inkling measured |
| WeirdML | Not measured | 32.3% | Only Inkling measured |
| ALE-Bench | 399.5 | 946.0 | Inkling |
| LMArena Text: coding | 1454 | 1466 | Inkling |
| LMArena Text: software and IT | 1453 | 1465 | Inkling |
| LMArena WebDev: React | 1420 | 1404 | GLM 4.7 |
| LMArena WebDev: HTML | 1447 | 1399 | GLM 4.7 |
| Neither model has a result on the other 12 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 4.7 | Inkling | Leader |
|---|---|---|---|
| AA Intelligence Index | 22.2 | 25.0 | Inkling |
| Epoch Capabilities Index | 143.6 | 148.4 | Inkling |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index18 boards, none shared
| Benchmark | GLM 4.7 | Inkling | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 22.5 | Only Inkling measured |
| Design Arena: agents building web apps | Not measured | 1196 | Only Inkling measured |
| Design Arena: agents building full-stack apps | 1050 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building mobile apps | 1105 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building Android apps | 1118 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building Godot games | 1058 | Not measured | Only GLM 4.7 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) | Not measured | 0.0% | Only Inkling measured |
| Finance Agent (Vals AI) | Not measured | 46.6% | Only Inkling measured |
| Tax Agent Bench (Vals AI) | Not measured | 15.3% | Only Inkling measured |
| Legal Research Bench (Vals AI) | Not measured | 28.4% | Only Inkling measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 3.8% | Only Inkling measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 38.4% | Only Inkling measured |
| τ²-Bench Telecom | 95.9% | Not measured | Only GLM 4.7 measured |
| APEX-Agents | Not measured | 33.8% | Only Inkling measured |
| Vending-Bench 2 | $2,377 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 21 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index17
| Benchmark | GLM 4.7 | Inkling | Leader |
|---|---|---|---|
| GPQA Diamond | 80.0% | 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) | 85.9% | 87.2% | Inkling |
| Humanity's Last Exam (Artificial Analysis run) | 27.4% | 31.9% | Inkling |
| GPQA Diamond (Epoch AI run) | 83.3% | 88.3% | Inkling |
| ARC-AGI | Not measured | 79.5% | Only Inkling measured |
| ARC-AGI-2 | Not measured | 36.5% | Only Inkling measured |
| SimpleBench | 47.7% | 50.0% | Inkling |
| Chess puzzles | 21.0% | 21.0% | Tie |
| LMArena Text: hard prompts | 1443 | 1451 | Inkling |
| LMArena Text: expert | 1424 | 1466 | Inkling |
| LMArena Text: hard prompts in English | 1452 | 1448 | GLM 4.7 |
| Neither model has a result on the other 8 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index13
| Benchmark | GLM 4.7 | Inkling | Leader |
|---|---|---|---|
| AIME | 93.3% | Not measured | Only GLM 4.7 measured |
| MGSM (Vals AI) | 88.2% | Not measured | Only GLM 4.7 measured |
| AA Math Index | 95.0 | Not measured | Only GLM 4.7 measured |
| AIME 2025 (Artificial Analysis run) | 95.0% | Not measured | Only GLM 4.7 measured |
| OTIS Mock AIME | 83.3% | 88.9% | Inkling |
| FrontierMath, tiers 1 to 3 | Not measured | 33.3% | Only Inkling measured |
| FrontierMath, tier 4 | Not measured | 4.9% | Only Inkling measured |
| ProofBench | 6.0% | 0.0% | GLM 4.7 |
| LMArena Text: math | 1424 | 1479 | Inkling |
| LMArena Text: mathematical work | 1411 | 1460 | Inkling |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index37
| Benchmark | GLM 4.7 | Inkling | Leader |
|---|---|---|---|
| MMLU Pro | 82.7% | 86.3% | Inkling |
| LegalBench (Vals AI) | 83.4% | 83.0% | GLM 4.7 |
| CaseLaw (Vals AI) | 54.9% | Not measured | Only GLM 4.7 measured |
| CorpFin (Vals AI) | 46.4% | 68.6% | Inkling |
| TaxEval (Vals AI) | 68.8% | 75.3% | Inkling |
| MortgageTax (Vals AI) | Not measured | 64.0% | Only Inkling measured |
| MedQA (Vals AI) | 93.7% | Not measured | Only GLM 4.7 measured |
| MedCode (Vals AI) | 32.8% | 41.2% | Inkling |
| MMLU Pro (Artificial Analysis run) | 85.6% | Not measured | Only GLM 4.7 measured |
| SimpleQA Verified | 32.2% | 40.3% | Inkling |
| LMArena Text: science | 1457 | 1455 | GLM 4.7 |
| LMArena Text: medicine and healthcare | 1469 | 1447 | GLM 4.7 |
| LMArena Text: legal and government | 1444 | 1448 | Inkling |
| LMArena Text: business and finance | 1429 | 1438 | Inkling |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index71
| Benchmark | GLM 4.7 | Inkling | Leader |
|---|---|---|---|
| Design Arena: websites | 1226 | 1224 | GLM 4.7 |
| Design Arena: UI components | 1199 | 1200 | Inkling |
| Design Arena: game dev | 1189 | 1166 | GLM 4.7 |
| Design Arena: data viz | 1195 | 1160 | GLM 4.7 |
| Design Arena: 3D | 1191 | 1161 | GLM 4.7 |
| Design Arena: all code categories | 1217 | 1206 | GLM 4.7 |
| Design Arena: SVG | 1142 | 1109 | GLM 4.7 |
| Design Arena: ASCII art | 1165 | 1089 | GLM 4.7 |
| LiveBench design | Not measured | 58.3% | Only Inkling measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index712
| Benchmark | GLM 4.7 | 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) | 68.6% | 85.4% | Inkling |
| Vals Multimodal Index | Not measured | 49.4% | Only Inkling measured |
| IFBench | 67.9% | Not measured | Only GLM 4.7 measured |
| Long context reasoning (AA-LCR) | 71.0% | 77.3% | Inkling |
| DTBench | Not measured | 87.5% | Only Inkling measured |
| LMCA | Not measured | 37.6% | Only Inkling measured |
| CL-bench | 15.9% | Not measured | Only GLM 4.7 measured |
| CL-bench Life | 10.9% | Not measured | Only GLM 4.7 measured |
| LMArena Text | 1435 | 1442 | Inkling |
| LMArena Text: instruction following | 1412 | 1425 | Inkling |
| LMArena Text: creative writing | 1401 | 1390 | GLM 4.7 |
| LMArena Text: English | 1453 | 1442 | GLM 4.7 |
| LMArena Text: non-English | 1417 | 1434 | Inkling |
| LMArena Text: Chinese | 1495 | 1493 | GLM 4.7 |
| LMArena Text: German | 1426 | 1445 | Inkling |
| LMArena Text: Japanese | 1439 | 1429 | GLM 4.7 |
| LMArena Text: Korean | 1399 | 1403 | Inkling |
| LMArena Text: French | 1432 | 1455 | Inkling |
| LMArena Text: Spanish | 1434 | 1449 | Inkling |
| LMArena Text: Russian | 1423 | 1430 | Inkling |
| LMArena Text: Polish | 1435 | 1450 | Inkling |
| LMArena Text: multi-turn | 1446 | 1437 | GLM 4.7 |
| LMArena Text: longer queries | 1432 | 1435 | Inkling |
| LMArena Text: writing and language | 1411 | 1404 | GLM 4.7 |
| LMArena Text: entertainment and media | 1400 | 1394 | GLM 4.7 |
| Neither model has a result on the other 17 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.7 | 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
GLM 4.7 costs less
Repository review
50K input + 3K output tokens
GLM 4.7 costs less
Agent loop
200K input + 10K output tokens
GLM 4.7 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 4.7 | Thinking Machines Inkling |
|---|---|---|
| Maker | Z.ai | Thinking Machines |
| Weights | Open weights | Open weights |
| Released | December 22, 2025 | July 17, 2026 |
| Context window | 205K | 524K |
| Input $/Mtok | $0.54 | $1.00 |
| Output $/Mtok | $1.98 | $4.05 |
| Coding index | 33.6 | 49.7 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | $0.29 | $0.76 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.7 better than Inkling for coding?
Pick Inkling for coding: it scores 49.7 on the Vibe Coding Index against 33.6 for GLM 4.7. Pick GLM 4.7 when output tokens cost 2.0x less, or when a solved task costs $0.29 against $0.76.
Which is cheaper, GLM 4.7 or Inkling?
GLM 4.7 costs $0.54 per million input tokens and $1.98 per million output tokens; Inkling costs $1.00 and $4.05. Per solved SWE-bench Verified task, GLM 4.7 costs $0.29 and Inkling $0.76.
Which has the bigger context window, GLM 4.7 or Inkling?
Inkling, with 524K tokens against 205K.
How many benchmarks is the GLM 4.7 vs Inkling comparison based on?
61 benchmarks have a result for both models as of October 21, 2026. GLM 4.7's coding index rests on 1 independent benchmark and Inkling's on 2. Where only one model has a result, the page says so instead of naming a winner.