Model comparison
GLM-4.5-Air vs GLM-5.3-Flash
GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 38.7 on the Noometry Index.
Last verified . 17 shared benchmarks.
Summary
- They share 17 benchmarks with published results for both. GLM-4.5-Air scores higher in 0 categories and GLM-5.3-Flash in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5.3-Flash leads 58.4 to 34.6.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.20 / $1.10 for GLM-4.5-Air.
- GLM-5.3-Flash accepts more context: 1M tokens versus 131K.
Side by side
| GLM-4.5-Air | GLM-5.3-Flash | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Z.ai (Zhipu) |
| Noometry Index | 38.7 | 49.6 |
| Released | 2025-07-20 | 2026-08-20 |
| Weights | Open | Open |
| Context window | 131K | 1M |
| Max output | 98K | 131K |
| Input $ / M tokens | $0.20 | $0.15 |
| Output $ / M tokens | $1.10 | $0.50 |
| Results tracked | 27 | 36 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-4.5-Air: 34.5 (#225), GLM-5.3-Flash: 53.7 (#26)
| Benchmark | GLM-4.5-Air | GLM-5.3-Flash |
|---|---|---|
| LMArena Coding | 1397 | 1506 |
| DeepSWE | — | 63.4% |
| FrontierCode | — | 31.8% |
| LMArena WebDev | — | 1614 |
| SciCode | — | 51.6% |
| GSO | 2.9% | — |
| ALE-Bench | — | 303.55 |
Agentic & Tool Use Not comparable
GLM-4.5-Air: —, GLM-5.3-Flash: 34.3 (#41)
| Benchmark | GLM-4.5-Air | GLM-5.3-Flash |
|---|---|---|
| APEX-Agents | — | 52.8% |
| GDP.pdf | — | 14% |
Reasoning GLM-5.3-Flash leads
GLM-4.5-Air: 23.3 (#166), GLM-5.3-Flash: 31.7 (#69)
| Benchmark | GLM-4.5-Air | GLM-5.3-Flash |
|---|---|---|
| LMArena Hard Prompts | 1379 | 1490 |
| Kagi LLM Benchmark | 43% | — |
| CritPt | — | 15.4% |
| Chess Puzzles | — | 14% |
| Mystery Game Puzzles | — | 8% |
| Surface Evolver Bench | — | 52.5% |
| Bench to the Future 3 | — | 0.15 |
| Epoch Capabilities Index | — | 151.9 |
| ForecastBench | 59.2 | — |
Math GLM-5.3-Flash leads
GLM-4.5-Air: 33.8 (#166), GLM-5.3-Flash: 53.1 (#38)
| Benchmark | GLM-4.5-Air | GLM-5.3-Flash |
|---|---|---|
| LMArena Math | 1396 | 1499 |
| FrontierMath (Tiers 1-3) | — | 55.8% |
| FrontierMath Tier 4 | — | 17.1% |
| OTIS Mock AIME 2024-2025 | — | 93.9% |
| ProofBench | — | 21% |
| Omni-MATH | 39.1% | — |
Knowledge GLM-5.3-Flash leads
GLM-4.5-Air: 34.6 (#184), GLM-5.3-Flash: 58.4 (#33)
| Benchmark | GLM-4.5-Air | GLM-5.3-Flash |
|---|---|---|
| LMArena Expert | 1370 | 1512 |
| GPQA Diamond | — | 90.2% |
| Humanity's Last Exam | 8.1% | — |
| MMLU-Pro | 76.2% | — |
| Vectara Hallucination Rate | 9.3% | — |
| GPQA (HELM) | 59.4% | — |
Multimodal Not comparable
GLM-4.5-Air: —, GLM-5.3-Flash: 42.6 (#25)
| Benchmark | GLM-4.5-Air | GLM-5.3-Flash |
|---|---|---|
| LMArena Vision | — | 1304 |
Multilingual GLM-5.3-Flash leads
GLM-4.5-Air: 49.4 (#130), GLM-5.3-Flash: 56.2 (#24)
| Benchmark | GLM-4.5-Air | GLM-5.3-Flash |
|---|---|---|
| LMArena Non-English | 1366 | 1462 |
| LMArena Chinese | 1427 | 1529 |
| LMArena French | 1399 | 1495 |
| LMArena German | 1376 | 1457 |
| LMArena Japanese | 1348 | 1420 |
| LMArena Korean | 1306 | 1443 |
| LMArena Russian | 1373 | 1472 |
| LMArena Spanish | 1385 | 1471 |
Instruction Following GLM-5.3-Flash leads
GLM-4.5-Air: 69.8 (#165), GLM-5.3-Flash: 77.8 (#17)
| Benchmark | GLM-4.5-Air | GLM-5.3-Flash |
|---|---|---|
| LMArena Instruction Following | 1353 | 1480 |
| IFEval | 81.2% | — |
Long Context GLM-5.3-Flash leads
GLM-4.5-Air: 41.6 (#130), GLM-5.3-Flash: 45.3 (#41)
| Benchmark | GLM-4.5-Air | GLM-5.3-Flash |
|---|---|---|
| LMArena Longer Query | 1367 | 1479 |
Writing & Preference GLM-5.3-Flash leads
GLM-4.5-Air: 56.1 (#134), GLM-5.3-Flash: 65.4 (#49)
| Benchmark | GLM-4.5-Air | GLM-5.3-Flash |
|---|---|---|
| LMArena Text | 1384 | 1471 |
| LMArena Creative Writing | 1343 | 1440 |
| LMArena Multi-Turn | 1371 | 1471 |
| WildBench | 78.9% | — |
Frequently asked questions
Is GLM-4.5-Air better than GLM-5.3-Flash?
GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 38.7 on the Noometry Index.
Which is cheaper, GLM-4.5-Air or GLM-5.3-Flash?
GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; GLM-4.5-Air lists at $0.20 and $1.10.
Is GLM-4.5-Air or GLM-5.3-Flash better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.7 versus 34.5 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3-Flash does, with 1M tokens against 131K.
How many benchmarks do GLM-4.5-Air and GLM-5.3-Flash share?
17 benchmarks have published results for both models. GLM-4.5-Air has 27 scored results on Noometry and GLM-5.3-Flash has 36.