Model comparison
DBRX vs GLM-5.3-Flash
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 29.4 on the Noometry Index.
Last verified . 18 shared benchmarks.
DatabricksSummary
- They share 18 benchmarks with published results for both. DBRX 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 14.9.
- The biggest single-benchmark swing is GPQA Diamond: 32.9% for DBRX and 90.2% for GLM-5.3-Flash.
Side by side
| DBRX | GLM-5.3-Flash | |
|---|---|---|
| Provider | Databricks | Z.ai (Zhipu) |
| Noometry Index | 29.4 | 51.8 |
| Released | 2024-03-27 | 2026-08-20 |
| Weights | Open | Open |
| Context window | — | 1M |
| Max output | — | 131K |
| Input $ / M tokens | — | $0.15 |
| Output $ / M tokens | — | $0.50 |
| Results tracked | 21 | 40 |
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Category by category
Coding GLM-5.3-Flash leads
DBRX: 32.9 (#266), GLM-5.3-Flash: 53.1 (#31)
| Benchmark | DBRX | GLM-5.3-Flash |
|---|---|---|
| LMArena Coding | 1132 | 1508 |
| DeepSWE | — | 63.4% |
| FrontierCode | — | 31.8% |
| CursorBench | — | 36.8% |
| LMArena WebDev | — | 1609 |
| FrontierSWE | — | 18.1% |
| SciCode | — | 51.6% |
| ALE-Bench | — | 303.55 |
| HumanEval+ | 70.1% | — |
| MBPP+ | 55.8% | — |
Agentic & Tool Use Not comparable
DBRX: —, GLM-5.3-Flash: 34.2 (#47)
| Benchmark | DBRX | GLM-5.3-Flash |
|---|---|---|
| APEX-Agents | — | 52.8% |
| GDP.pdf | — | 14% |
Reasoning GLM-5.3-Flash leads
DBRX: 21.4 (#222), GLM-5.3-Flash: 48.0 (#42)
| Benchmark | DBRX | GLM-5.3-Flash |
|---|---|---|
| LMArena Hard Prompts | 1113 | 1491 |
| ARC-AGI-2 | — | 65.8% |
| 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 GLM-5.3-Flash leads
DBRX: 24.3 (#269), GLM-5.3-Flash: 53.3 (#47)
| Benchmark | DBRX | GLM-5.3-Flash |
|---|---|---|
| LMArena Math | 1145 | 1500 |
| FrontierMath (Tiers 1-3) | — | 55.8% |
| FrontierMath Tier 4 | — | 17.1% |
| OTIS Mock AIME 2024-2025 | — | 93.9% |
| ProofBench | — | 21% |
| MATH Level 5 | 11.7% | — |
Knowledge GLM-5.3-Flash leads
DBRX: 14.9 (#294), GLM-5.3-Flash: 58.4 (#36)
| Benchmark | DBRX | GLM-5.3-Flash |
|---|---|---|
| GPQA Diamond | 32.9% | 90.2% |
| LMArena Expert | 1076 | 1513 |
Multimodal Not comparable
DBRX: —, GLM-5.3-Flash: 42.8 (#27)
| Benchmark | DBRX | GLM-5.3-Flash |
|---|---|---|
| LMArena Vision | — | 1296 |
Multilingual GLM-5.3-Flash leads
DBRX: 29.3 (#268), GLM-5.3-Flash: 56.0 (#25)
| Benchmark | DBRX | GLM-5.3-Flash |
|---|---|---|
| LMArena Non-English | 1071 | 1462 |
| LMArena Chinese | 1068 | 1527 |
| LMArena French | 1096 | 1496 |
| LMArena German | 1057 | 1470 |
| LMArena Japanese | 990 | 1429 |
| LMArena Korean | 993 | 1446 |
| LMArena Russian | 1078 | 1469 |
| LMArena Spanish | 1064 | 1471 |
Instruction Following GLM-5.3-Flash leads
DBRX: 57.5 (#270), GLM-5.3-Flash: 77.5 (#20)
| Benchmark | DBRX | GLM-5.3-Flash |
|---|---|---|
| LMArena Instruction Following | 1112 | 1478 |
Long Context GLM-5.3-Flash leads
DBRX: 33.7 (#258), GLM-5.3-Flash: 45.4 (#39)
| Benchmark | DBRX | GLM-5.3-Flash |
|---|---|---|
| LMArena Longer Query | 1112 | 1482 |
Writing & Preference GLM-5.3-Flash leads
DBRX: 33.6 (#275), GLM-5.3-Flash: 65.3 (#50)
| Benchmark | DBRX | GLM-5.3-Flash |
|---|---|---|
| LMArena Text | 1119 | 1471 |
| LMArena Creative Writing | 1104 | 1442 |
| LMArena Multi-Turn | 1111 | 1467 |
Frequently asked questions
Is DBRX better than GLM-5.3-Flash?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 29.4 on the Noometry Index.
Is DBRX or GLM-5.3-Flash better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 32.9 in the Noometry coding category.
How many benchmarks do DBRX and GLM-5.3-Flash share?
18 benchmarks have published results for both models. DBRX has 21 scored results on Noometry and GLM-5.3-Flash has 40.