Model comparison
GLM-5.3-Flash vs Mercury
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 37.6 on the Noometry Index.
Last verified . 8 shared benchmarks.
Summary
- They share 8 benchmarks with published results for both. GLM-5.3-Flash scores higher in 6 categories and Mercury in 0 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.3-Flash leads 48.0 to 17.5.
- GLM-5.3-Flash has downloadable open weights; the other is API-only.
Side by side
| GLM-5.3-Flash | Mercury | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Inception |
| Noometry Index | 51.8 | 37.6 |
| Released | 2026-08-20 | — |
| Weights | Open | Proprietary |
| Context window | 1M | — |
| Max output | 131K | — |
| Input $ / M tokens | $0.15 | — |
| Output $ / M tokens | $0.50 | — |
| Results tracked | 40 | 9 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-5.3-Flash: 53.1 (#31), Mercury: 38.7 (#170)
| Benchmark | GLM-5.3-Flash | Mercury |
|---|---|---|
| LMArena Coding | 1508 | 1322 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| CursorBench | 36.8% | — |
| LMArena WebDev | 1609 | — |
| FrontierSWE | 18.1% | — |
| SciCode | 51.6% | — |
| ALE-Bench | 303.55 | — |
Agentic & Tool Use Not comparable
GLM-5.3-Flash: 34.2 (#47), Mercury: —
| Benchmark | GLM-5.3-Flash | Mercury |
|---|---|---|
| APEX-Agents | 52.8% | — |
| GDP.pdf | 14% | — |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 48.0 (#42), Mercury: 17.5 (#293)
| Benchmark | GLM-5.3-Flash | Mercury |
|---|---|---|
| LMArena Hard Prompts | 1491 | 1285 |
| ARC-AGI-2 | 65.8% | — |
| Kagi LLM Benchmark | — | 21.6% |
| ARC-AGI-1 | 91% | — |
| 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 | 151.88 | — |
Math Not comparable
GLM-5.3-Flash: 53.3 (#47), Mercury: —
| Benchmark | GLM-5.3-Flash | Mercury |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| OTIS Mock AIME 2024-2025 | 93.9% | — |
| ProofBench | 21% | — |
| LMArena Math | 1500 | — |
Knowledge Not comparable
GLM-5.3-Flash: 58.4 (#36), Mercury: —
| Benchmark | GLM-5.3-Flash | Mercury |
|---|---|---|
| GPQA Diamond | 90.2% | — |
| LMArena Expert | 1513 | — |
Multimodal Not comparable
GLM-5.3-Flash: 42.8 (#27), Mercury: —
| Benchmark | GLM-5.3-Flash | Mercury |
|---|---|---|
| LMArena Vision | 1296 | — |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.0 (#25), Mercury: 41.6 (#206)
| Benchmark | GLM-5.3-Flash | Mercury |
|---|---|---|
| LMArena Non-English | 1462 | 1260 |
| LMArena Chinese | 1527 | — |
| LMArena French | 1496 | — |
| LMArena German | 1470 | — |
| LMArena Japanese | 1429 | — |
| LMArena Korean | 1446 | — |
| LMArena Russian | 1469 | — |
| LMArena Spanish | 1471 | — |
Instruction Following GLM-5.3-Flash leads
GLM-5.3-Flash: 77.5 (#20), Mercury: 65.2 (#224)
| Benchmark | GLM-5.3-Flash | Mercury |
|---|---|---|
| LMArena Instruction Following | 1478 | 1239 |
Long Context GLM-5.3-Flash leads
GLM-5.3-Flash: 45.4 (#39), Mercury: 38.4 (#198)
| Benchmark | GLM-5.3-Flash | Mercury |
|---|---|---|
| LMArena Longer Query | 1482 | 1266 |
Writing & Preference GLM-5.3-Flash leads
GLM-5.3-Flash: 65.3 (#50), Mercury: 46.2 (#221)
| Benchmark | GLM-5.3-Flash | Mercury |
|---|---|---|
| LMArena Text | 1471 | 1282 |
| LMArena Creative Writing | 1442 | 1191 |
| LMArena Multi-Turn | 1467 | 1282 |
Frequently asked questions
Is GLM-5.3-Flash better than Mercury?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 37.6 on the Noometry Index.
Is GLM-5.3-Flash or Mercury better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 38.7 in the Noometry coding category.
How many benchmarks do GLM-5.3-Flash and Mercury share?
8 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Mercury has 9.