Model comparison
GLM-5.3-Flash vs Inkling-Small
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 46.5 on the Noometry Index.
Last verified . 31 shared benchmarks.
Summary
- They share 31 benchmarks with published results for both. GLM-5.3-Flash scores higher in 9 categories and Inkling-Small in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5.3-Flash leads 58.4 to 48.2.
- The biggest single-benchmark swing is ARC-AGI-2: 65.8% for GLM-5.3-Flash and 40.1% for Inkling-Small.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.45 / $1.20 for Inkling-Small.
- GLM-5.3-Flash accepts more context: 1M tokens versus 524K.
Side by side
| GLM-5.3-Flash | Inkling-Small | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Thinking Machines Lab |
| Noometry Index | 51.8 | 46.5 |
| Released | 2026-08-20 | 2026-07-15 |
| Weights | Open | Open |
| Context window | 1M | 524K |
| Max output | 131K | 1.05M |
| Input $ / M tokens | $0.15 | $0.45 |
| Output $ / M tokens | $0.50 | $1.20 |
| Results tracked | 40 | 33 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-5.3-Flash: 53.1 (#31), Inkling-Small: 43.6 (#85)
| Benchmark | GLM-5.3-Flash | Inkling-Small |
|---|---|---|
| LMArena WebDev | 1609 | 1409 |
| SciCode | 51.6% | 48.7% |
| LMArena Coding | 1508 | 1451 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| CursorBench | 36.8% | — |
| FrontierSWE | 18.1% | — |
| ALE-Bench | 303.55 | — |
Agentic & Tool Use Not comparable
GLM-5.3-Flash: 34.2 (#47), Inkling-Small: —
| Benchmark | GLM-5.3-Flash | Inkling-Small |
|---|---|---|
| APEX-Agents | 52.8% | — |
| GDP.pdf | 14% | — |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 48.0 (#42), Inkling-Small: 38.6 (#63)
| Benchmark | GLM-5.3-Flash | Inkling-Small |
|---|---|---|
| ARC-AGI-2 | 65.8% | 40.1% |
| ARC-AGI-1 | 91% | 84% |
| CritPt | 15.4% | 8.3% |
| Chess Puzzles | 14% | 18% |
| LMArena Hard Prompts | 1491 | 1423 |
| Mystery Game Puzzles | 8% | 6% |
| Epoch Capabilities Index | 151.88 | 150.15 |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
Math GLM-5.3-Flash leads
GLM-5.3-Flash: 53.3 (#47), Inkling-Small: 45.1 (#77)
| Benchmark | GLM-5.3-Flash | Inkling-Small |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | 46.3% |
| FrontierMath Tier 4 | 17.1% | 17.1% |
| OTIS Mock AIME 2024-2025 | 93.9% | 90% |
| ProofBench | 21% | 6% |
| LMArena Math | 1500 | 1459 |
Knowledge GLM-5.3-Flash leads
GLM-5.3-Flash: 58.4 (#36), Inkling-Small: 48.2 (#77)
| Benchmark | GLM-5.3-Flash | Inkling-Small |
|---|---|---|
| GPQA Diamond | 90.2% | 88.5% |
| LMArena Expert | 1513 | 1442 |
| SimpleQA Verified | — | 19.1% |
Multimodal GLM-5.3-Flash leads
GLM-5.3-Flash: 42.8 (#27), Inkling-Small: 39.1 (#62)
| Benchmark | GLM-5.3-Flash | Inkling-Small |
|---|---|---|
| LMArena Vision | 1296 | 1235 |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.0 (#25), Inkling-Small: 51.7 (#104)
| Benchmark | GLM-5.3-Flash | Inkling-Small |
|---|---|---|
| LMArena Non-English | 1462 | 1402 |
| LMArena Chinese | 1527 | 1465 |
| LMArena French | 1496 | 1436 |
| LMArena German | 1470 | 1405 |
| LMArena Japanese | 1429 | 1405 |
| LMArena Korean | 1446 | 1363 |
| LMArena Russian | 1469 | 1391 |
| LMArena Spanish | 1471 | 1428 |
Instruction Following GLM-5.3-Flash leads
GLM-5.3-Flash: 77.5 (#20), Inkling-Small: 73.8 (#114)
| Benchmark | GLM-5.3-Flash | Inkling-Small |
|---|---|---|
| LMArena Instruction Following | 1478 | 1399 |
Long Context GLM-5.3-Flash leads
GLM-5.3-Flash: 45.4 (#39), Inkling-Small: 42.7 (#118)
| Benchmark | GLM-5.3-Flash | Inkling-Small |
|---|---|---|
| LMArena Longer Query | 1482 | 1401 |
Writing & Preference GLM-5.3-Flash leads
GLM-5.3-Flash: 65.3 (#50), Inkling-Small: 59.6 (#107)
| Benchmark | GLM-5.3-Flash | Inkling-Small |
|---|---|---|
| LMArena Text | 1471 | 1414 |
| LMArena Creative Writing | 1442 | 1331 |
| LMArena Multi-Turn | 1467 | 1418 |
| EQ-Bench Creative Writing | — | 1491 |
Frequently asked questions
Is GLM-5.3-Flash better than Inkling-Small?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 46.5 on the Noometry Index.
Which is cheaper, GLM-5.3-Flash or Inkling-Small?
GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; Inkling-Small lists at $0.45 and $1.20.
Is GLM-5.3-Flash or Inkling-Small better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 43.6 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3-Flash does, with 1M tokens against 524K.
How many benchmarks do GLM-5.3-Flash and Inkling-Small share?
31 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Inkling-Small has 33.