Model comparison
GLM-5.1 vs GLM-5.3-Flash
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 47.8 on the Noometry Index.
Last verified . 28 shared benchmarks.
Summary
- They share 28 benchmarks with published results for both. GLM-5.1 scores higher in 1 category and GLM-5.3-Flash in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where GLM-5.3-Flash leads 34.2 to 24.9.
- The biggest single-benchmark swing is FrontierMath (Tiers 1-3): 36.8% for GLM-5.1 and 55.8% for GLM-5.3-Flash.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $1.40 / $4.40 for GLM-5.1.
- GLM-5.3-Flash accepts more context: 1M tokens versus 200K.
Side by side
| GLM-5.1 | GLM-5.3-Flash | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Z.ai (Zhipu) |
| Noometry Index | 47.8 | 51.8 |
| Released | 2026-04-07 | 2026-08-20 |
| Weights | Open | Open |
| Context window | 200K | 1M |
| Max output | 131K | 131K |
| Input $ / M tokens | $1.40 | $0.15 |
| Output $ / M tokens | $4.40 | $0.50 |
| Results tracked | 41 | 40 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-5.1: 48.7 (#55), GLM-5.3-Flash: 53.1 (#31)
| Benchmark | GLM-5.1 | GLM-5.3-Flash |
|---|---|---|
| LMArena WebDev | 1508 | 1609 |
| SciCode | 43.8% | 51.6% |
| LMArena Coding | 1485 | 1508 |
| ALE-Bench | 887.1 | 303.55 |
| SWE-bench Verified | 74.2% | — |
| DeepSWE | — | 63.4% |
| FrontierCode | — | 31.8% |
| CursorBench | — | 36.8% |
| FrontierSWE | — | 18.1% |
| WeirdML | 57.1% | — |
Agentic & Tool Use GLM-5.3-Flash leads
GLM-5.1: 24.9 (#113), GLM-5.3-Flash: 34.2 (#47)
| Benchmark | GLM-5.1 | GLM-5.3-Flash |
|---|---|---|
| APEX-Agents | 40.9% | 52.8% |
| ExploitBench | 18.1% | — |
| GBAEval | 0% | — |
| GDP.pdf | — | 14% |
| Vending-Bench 2 | 5,634 | — |
Reasoning GLM-5.3-Flash leads
GLM-5.1: 39.1 (#60), GLM-5.3-Flash: 48.0 (#42)
| Benchmark | GLM-5.1 | GLM-5.3-Flash |
|---|---|---|
| CritPt | 4.6% | 15.4% |
| Chess Puzzles | 19% | 14% |
| LMArena Hard Prompts | 1472 | 1491 |
| Epoch Capabilities Index | 149.84 | 151.88 |
| ARC-AGI-2 | — | 65.8% |
| SimpleBench | 55.1% | — |
| NYT Connections (extended) | 77.7% | — |
| ARC-AGI-1 | — | 91% |
| Thematic Generalization | 69.8% | — |
| Mystery Game Puzzles | — | 8% |
| Surface Evolver Bench | — | 52.5% |
| Bench to the Future 3 | — | 0.15 |
Math GLM-5.3-Flash leads
GLM-5.1: 49.7 (#60), GLM-5.3-Flash: 53.3 (#47)
| Benchmark | GLM-5.1 | GLM-5.3-Flash |
|---|---|---|
| FrontierMath (Tiers 1-3) | 36.8% | 55.8% |
| OTIS Mock AIME 2024-2025 | 93.3% | 93.9% |
| ProofBench | 22.2% | 21% |
| LMArena Math | 1473 | 1500 |
| FrontierMath Tier 4 | — | 17.1% |
| MathArena Final-Answer Competitions | 67.1% | — |
| FrontierMath (Feb 2025 set) | 33.4% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |
Knowledge GLM-5.3-Flash leads
GLM-5.1: 54.9 (#50), GLM-5.3-Flash: 58.4 (#36)
| Benchmark | GLM-5.1 | GLM-5.3-Flash |
|---|---|---|
| GPQA Diamond | 89.9% | 90.2% |
| LMArena Expert | 1476 | 1513 |
| SimpleQA Verified | 34% | — |
Multimodal Not comparable
GLM-5.1: —, GLM-5.3-Flash: 42.8 (#27)
| Benchmark | GLM-5.1 | GLM-5.3-Flash |
|---|---|---|
| LMArena Vision | — | 1296 |
Multilingual GLM-5.3-Flash leads
GLM-5.1: 55.0 (#36), GLM-5.3-Flash: 56.0 (#25)
| Benchmark | GLM-5.1 | GLM-5.3-Flash |
|---|---|---|
| LMArena Non-English | 1447 | 1462 |
| LMArena Chinese | 1515 | 1527 |
| LMArena French | 1474 | 1496 |
| LMArena German | 1465 | 1470 |
| LMArena Japanese | 1434 | 1429 |
| LMArena Korean | 1418 | 1446 |
| LMArena Russian | 1454 | 1469 |
| LMArena Spanish | 1469 | 1471 |
Instruction Following GLM-5.3-Flash leads
GLM-5.1: 76.3 (#42), GLM-5.3-Flash: 77.5 (#20)
| Benchmark | GLM-5.1 | GLM-5.3-Flash |
|---|---|---|
| LMArena Instruction Following | 1451 | 1478 |
Long Context Too close to call
GLM-5.1: 44.9 (#53), GLM-5.3-Flash: 45.4 (#39)
| Benchmark | GLM-5.1 | GLM-5.3-Flash |
|---|---|---|
| LMArena Longer Query | 1466 | 1482 |
Writing & Preference GLM-5.1 leads
GLM-5.1: 66.9 (#31), GLM-5.3-Flash: 65.3 (#50)
| Benchmark | GLM-5.1 | GLM-5.3-Flash |
|---|---|---|
| LMArena Text | 1461 | 1471 |
| LMArena Creative Writing | 1453 | 1442 |
| LMArena Multi-Turn | 1472 | 1467 |
| EQ-Bench Creative Writing | 1592 | — |
Frequently asked questions
Is GLM-5.1 better than GLM-5.3-Flash?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 47.8 on the Noometry Index.
Which is cheaper, GLM-5.1 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-5.1 lists at $1.40 and $4.40.
Is GLM-5.1 or GLM-5.3-Flash better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 48.7 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3-Flash does, with 1M tokens against 200K.
How many benchmarks do GLM-5.1 and GLM-5.3-Flash share?
28 benchmarks have published results for both models. GLM-5.1 has 41 scored results on Noometry and GLM-5.3-Flash has 40.