Model comparison
GLM-5.2 vs Inkling-Small
GLM-5.2 is the stronger model overall, scoring 51.1 to 46.5 on the Noometry Index. Inkling-Small costs 3.4× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Last verified . 32 shared benchmarks.
Summary
- They share 32 benchmarks with published results for both. GLM-5.2 scores higher in 8 categories and Inkling-Small in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-5.2 leads 70.4 to 59.6.
- The biggest single-benchmark swing is ProofBench: 35% for GLM-5.2 and 6% for Inkling-Small.
- Inkling-Small is cheaper at $0.45 / $1.20 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- GLM-5.2 accepts more context: 1M tokens versus 524K.
Side by side
| GLM-5.2 | Inkling-Small | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Thinking Machines Lab |
| Noometry Index | 51.1 | 46.5 |
| Released | 2026-06-13 | 2026-07-15 |
| Weights | Open | Open |
| Context window | 1M | 524K |
| Max output | 131K | 1.05M |
| Input $ / M tokens | $1.40 | $0.45 |
| Output $ / M tokens | $4.40 | $1.20 |
| Results tracked | 51 | 33 |
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Category by category
Coding GLM-5.2 leads
GLM-5.2: 51.3 (#41), Inkling-Small: 43.6 (#85)
| Benchmark | GLM-5.2 | Inkling-Small |
|---|---|---|
| LMArena WebDev | 1603 | 1409 |
| SciCode | 50.5% | 48.7% |
| LMArena Coding | 1485 | 1451 |
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| WeirdML | 70.1% | — |
| ALE-Bench | 1,047 | — |
Agentic & Tool Use Not comparable
GLM-5.2: 32.4 (#63), Inkling-Small: —
| Benchmark | GLM-5.2 | Inkling-Small |
|---|---|---|
| APEX-Agents | 45.2% | — |
| τ²-bench Banking | 37.1% | — |
| PostTrainBench | 31.7% | — |
| GBAEval | 0% | — |
| Vending-Bench 2 | 8,314 | — |
Reasoning GLM-5.2 leads
GLM-5.2: 42.3 (#52), Inkling-Small: 38.6 (#63)
| Benchmark | GLM-5.2 | Inkling-Small |
|---|---|---|
| ARC-AGI-2 | 22.8% | 40.1% |
| ARC-AGI-1 | 77% | 84% |
| CritPt | 20.9% | 8.3% |
| Chess Puzzles | 21% | 18% |
| LMArena Hard Prompts | 1480 | 1423 |
| Mystery Game Puzzles | 19% | 6% |
| Epoch Capabilities Index | 151.78 | 150.15 |
| SimpleBench | 58.8% | — |
| Kagi LLM Benchmark | 62.6% | — |
| NYT Connections (extended) | 74.3% | — |
| EBR-Bench | 9.5% | — |
| DTBench | 93.6% | — |
| LMCA | 45.8% | — |
| Surface Evolver Bench | 55.6% | — |
Math GLM-5.2 leads
GLM-5.2: 55.7 (#43), Inkling-Small: 45.1 (#77)
| Benchmark | GLM-5.2 | Inkling-Small |
|---|---|---|
| FrontierMath (Tiers 1-3) | 59.2% | 46.3% |
| FrontierMath Tier 4 | 29.3% | 17.1% |
| OTIS Mock AIME 2024-2025 | 86.4% | 90% |
| ProofBench | 35% | 6% |
| LMArena Math | 1482 | 1459 |
| MathArena Final-Answer Competitions | 67.6% | — |
Knowledge GLM-5.2 leads
GLM-5.2: 57.1 (#40), Inkling-Small: 48.2 (#77)
| Benchmark | GLM-5.2 | Inkling-Small |
|---|---|---|
| GPQA Diamond | 91.9% | 88.5% |
| SimpleQA Verified | 34.2% | 19.1% |
| LMArena Expert | 1486 | 1442 |
Multimodal Not comparable
GLM-5.2: —, Inkling-Small: 39.1 (#62)
| Benchmark | GLM-5.2 | Inkling-Small |
|---|---|---|
| LMArena Vision | — | 1235 |
Multilingual GLM-5.2 leads
GLM-5.2: 55.8 (#26), Inkling-Small: 51.7 (#104)
| Benchmark | GLM-5.2 | Inkling-Small |
|---|---|---|
| LMArena Non-English | 1459 | 1402 |
| LMArena Chinese | 1519 | 1465 |
| LMArena French | 1479 | 1436 |
| LMArena German | 1468 | 1405 |
| LMArena Japanese | 1451 | 1405 |
| LMArena Korean | 1445 | 1363 |
| LMArena Russian | 1466 | 1391 |
| LMArena Spanish | 1477 | 1428 |
Instruction Following GLM-5.2 leads
GLM-5.2: 76.9 (#34), Inkling-Small: 73.8 (#114)
| Benchmark | GLM-5.2 | Inkling-Small |
|---|---|---|
| LMArena Instruction Following | 1465 | 1399 |
Long Context GLM-5.2 leads
GLM-5.2: 45.3 (#43), Inkling-Small: 42.7 (#118)
| Benchmark | GLM-5.2 | Inkling-Small |
|---|---|---|
| LMArena Longer Query | 1479 | 1401 |
Writing & Preference GLM-5.2 leads
GLM-5.2: 70.4 (#21), Inkling-Small: 59.6 (#107)
| Benchmark | GLM-5.2 | Inkling-Small |
|---|---|---|
| LMArena Text | 1470 | 1414 |
| LMArena Creative Writing | 1462 | 1331 |
| EQ-Bench Creative Writing | 1757 | 1491 |
| LMArena Multi-Turn | 1469 | 1418 |
| EQ-Bench 4 | 1222 | — |
Frequently asked questions
Is GLM-5.2 better than Inkling-Small?
GLM-5.2 is the stronger model overall, scoring 51.1 to 46.5 on the Noometry Index. Inkling-Small costs 3.4× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Which is cheaper, GLM-5.2 or Inkling-Small?
Inkling-Small is cheaper. It lists at $0.45 per million input tokens and $1.20 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.
Is GLM-5.2 or Inkling-Small better for coding?
GLM-5.2 scores higher on coding benchmarks: 51.3 versus 43.6 in the Noometry coding category.
Which has the bigger context window?
GLM-5.2 does, with 1M tokens against 524K.
How many benchmarks do GLM-5.2 and Inkling-Small share?
32 benchmarks have published results for both models. GLM-5.2 has 51 scored results on Noometry and Inkling-Small has 33.