Model comparison
GLM-4.6V vs Inkling
Inkling is the stronger model overall, scoring 44.1 to 41.3 on the Noometry Index. GLM-4.6V costs 5.7× less per token, which makes it the better buy when Inkling's lead doesn't matter for your workload.
Last verified . 11 shared benchmarks.
Summary
- They share 11 benchmarks with published results for both. GLM-4.6V scores higher in 1 category and Inkling in 6 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Inkling leads 55.1 to 38.0.
- GLM-4.6V is cheaper at $0.30 / $0.90 per million input/output tokens, against $1.87 / $4.68 for Inkling.
- GLM-4.6V accepts more context: 128K tokens versus 66K.
Side by side
| GLM-4.6V | Inkling | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Thinking Machines Lab |
| Noometry Index | 41.3 | 44.1 |
| Released | 2025-12-08 | 2026-07-15 |
| Weights | Open | Open |
| Context window | 128K | 66K |
| Max output | 33K | 66K |
| Input $ / M tokens | $0.30 | $1.87 |
| Output $ / M tokens | $0.90 | $4.68 |
| Results tracked | 12 | 41 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GLM-4.6V leads
GLM-4.6V: 40.9 (#128), Inkling: 34.5 (#234)
| Benchmark | GLM-4.6V | Inkling |
|---|---|---|
| LMArena Coding | 1390 | 1464 |
| FrontierCode | — | 14% |
| LMArena WebDev | — | 1413 |
| FrontierSWE | — | 4.1% |
| SciCode | — | 47% |
| WeirdML | — | 32.3% |
| ALE-Bench | — | 946 |
Agentic & Tool Use Not comparable
GLM-4.6V: —, Inkling: 29.6 (#85)
| Benchmark | GLM-4.6V | Inkling |
|---|---|---|
| APEX-Agents | — | 33.8% |
| τ²-bench Banking | — | 25% |
Reasoning Inkling leads
GLM-4.6V: 27.6 (#115), Inkling: 40.4 (#56)
| Benchmark | GLM-4.6V | Inkling |
|---|---|---|
| LMArena Hard Prompts | 1368 | 1451 |
| ARC-AGI-2 | — | 36.5% |
| SimpleBench | — | 50% |
| ARC-AGI-1 | — | 79.5% |
| CritPt | — | 5.4% |
| Chess Puzzles | — | 21% |
| DTBench | — | 87.5% |
| LMCA | — | 37.6% |
| Epoch Capabilities Index | — | 148.54 |
Math Not comparable
GLM-4.6V: —, Inkling: 31.3 (#225)
| Benchmark | GLM-4.6V | Inkling |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 33.3% |
| FrontierMath Tier 4 | — | 4.9% |
| OTIS Mock AIME 2024-2025 | — | 88.9% |
| ProofBench | — | 0% |
| LMArena Math | — | 1479 |
Knowledge Inkling leads
GLM-4.6V: 38.0 (#149), Inkling: 55.1 (#49)
| Benchmark | GLM-4.6V | Inkling |
|---|---|---|
| LMArena Expert | 1371 | 1465 |
| GPQA Diamond | — | 88.3% |
| SimpleQA Verified | — | 40.3% |
Multimodal Not comparable
GLM-4.6V: 34.8 (#90), Inkling: —
| Benchmark | GLM-4.6V | Inkling |
|---|---|---|
| LMArena Vision | 1164 | — |
Multilingual Inkling leads
GLM-4.6V: 48.6 (#141), Inkling: 54.0 (#52)
| Benchmark | GLM-4.6V | Inkling |
|---|---|---|
| LMArena Non-English | 1359 | 1434 |
| LMArena Chinese | 1425 | 1490 |
| LMArena Russian | 1340 | 1429 |
| LMArena French | — | 1458 |
| LMArena German | — | 1446 |
| LMArena Japanese | — | 1429 |
| LMArena Korean | — | 1404 |
| LMArena Spanish | — | 1448 |
Instruction Following Inkling leads
GLM-4.6V: 71.4 (#151), Inkling: 75.1 (#71)
| Benchmark | GLM-4.6V | Inkling |
|---|---|---|
| LMArena Instruction Following | 1352 | 1426 |
Long Context Inkling leads
GLM-4.6V: 41.3 (#143), Inkling: 43.8 (#86)
| Benchmark | GLM-4.6V | Inkling |
|---|---|---|
| LMArena Longer Query | 1358 | 1434 |
Writing & Preference Inkling leads
GLM-4.6V: 56.6 (#137), Inkling: 65.2 (#51)
| Benchmark | GLM-4.6V | Inkling |
|---|---|---|
| LMArena Text | 1377 | 1441 |
| LMArena Creative Writing | 1347 | 1387 |
| LMArena Multi-Turn | 1360 | 1436 |
| EQ-Bench Creative Writing | — | 1611 |
| EQ-Bench 4 | — | 1226 |
Frequently asked questions
Is GLM-4.6V better than Inkling?
Inkling is the stronger model overall, scoring 44.1 to 41.3 on the Noometry Index. GLM-4.6V costs 5.7× less per token, which makes it the better buy when Inkling's lead doesn't matter for your workload.
Which is cheaper, GLM-4.6V or Inkling?
GLM-4.6V is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; Inkling lists at $1.87 and $4.68.
Is GLM-4.6V or Inkling better for coding?
GLM-4.6V scores higher on coding benchmarks: 40.9 versus 34.5 in the Noometry coding category.
Which has the bigger context window?
GLM-4.6V does, with 128K tokens against 66K.
How many benchmarks do GLM-4.6V and Inkling share?
11 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Inkling has 41.