Model comparison
DBRX vs GLM-4.6
GLM-4.6 is the stronger model overall, scoring 41.4 to 29.4 on the Noometry Index.
Last verified . 17 shared benchmarks.
DatabricksSummary
- They share 17 benchmarks with published results for both. DBRX scores higher in 0 categories and GLM-4.6 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-4.6 leads 61.1 to 33.6.
Side by side
| DBRX | GLM-4.6 | |
|---|---|---|
| Provider | Databricks | Z.ai (Zhipu) |
| Noometry Index | 29.4 | 41.4 |
| Released | 2024-03-27 | 2025-09-30 |
| Weights | Open | Open |
| Context window | — | 205K |
| Max output | — | 131K |
| Input $ / M tokens | — | $0.60 |
| Output $ / M tokens | — | $2.20 |
| Results tracked | 21 | 29 |
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Category by category
Coding GLM-4.6 leads
DBRX: 32.9 (#266), GLM-4.6: 40.1 (#148)
| Benchmark | DBRX | GLM-4.6 |
|---|---|---|
| LMArena Coding | 1132 | 1449 |
| SWE-bench Verified (bash only) | — | 55.4% |
| LMArena WebDev | — | 1340 |
| SciCode | — | 38.4% |
| ALE-Bench | — | 340.82 |
| HumanEval+ | 70.1% | — |
| MBPP+ | 55.8% | — |
Agentic & Tool Use Not comparable
DBRX: —, GLM-4.6: 32.3 (#66)
| Benchmark | DBRX | GLM-4.6 |
|---|---|---|
| Terminal-Bench | — | 24.5% |
| Berkeley Function Calling Leaderboard | — | 72.4% |
Reasoning GLM-4.6 leads
DBRX: 21.4 (#222), GLM-4.6: 23.7 (#172)
| Benchmark | DBRX | GLM-4.6 |
|---|---|---|
| LMArena Hard Prompts | 1113 | 1440 |
| Kagi LLM Benchmark | — | 47.4% |
| CritPt | — | 1.1% |
Math GLM-4.6 leads
DBRX: 24.3 (#269), GLM-4.6: 39.1 (#111)
| Benchmark | DBRX | GLM-4.6 |
|---|---|---|
| LMArena Math | 1145 | 1432 |
| MATH Level 5 | 11.7% | — |
| FrontierMath (Feb 2025 set) | — | 3.8% |
| FrontierMath Tier 4 (v1) | — | 2.1% |
Knowledge GLM-4.6 leads
DBRX: 14.9 (#294), GLM-4.6: 40.2 (#124)
| Benchmark | DBRX | GLM-4.6 |
|---|---|---|
| LMArena Expert | 1076 | 1431 |
| GPQA Diamond | 32.9% | — |
| Vectara Hallucination Rate | — | 9.5% |
Multilingual GLM-4.6 leads
DBRX: 29.3 (#268), GLM-4.6: 53.5 (#66)
| Benchmark | DBRX | GLM-4.6 |
|---|---|---|
| LMArena Non-English | 1071 | 1426 |
| LMArena Chinese | 1068 | 1499 |
| LMArena French | 1096 | 1459 |
| LMArena German | 1057 | 1447 |
| LMArena Japanese | 990 | 1393 |
| LMArena Korean | 993 | 1400 |
| LMArena Russian | 1078 | 1419 |
| LMArena Spanish | 1064 | 1436 |
Instruction Following GLM-4.6 leads
DBRX: 57.5 (#270), GLM-4.6: 74.3 (#98)
| Benchmark | DBRX | GLM-4.6 |
|---|---|---|
| LMArena Instruction Following | 1112 | 1410 |
Long Context GLM-4.6 leads
DBRX: 33.7 (#258), GLM-4.6: 43.4 (#94)
| Benchmark | DBRX | GLM-4.6 |
|---|---|---|
| LMArena Longer Query | 1112 | 1422 |
Writing & Preference GLM-4.6 leads
DBRX: 33.6 (#275), GLM-4.6: 61.1 (#90)
| Benchmark | DBRX | GLM-4.6 |
|---|---|---|
| LMArena Text | 1119 | 1440 |
| LMArena Creative Writing | 1104 | 1411 |
| LMArena Multi-Turn | 1111 | 1427 |
| EQ-Bench Creative Writing | — | 1411 |
Frequently asked questions
Is DBRX better than GLM-4.6?
GLM-4.6 is the stronger model overall, scoring 41.4 to 29.4 on the Noometry Index.
Is DBRX or GLM-4.6 better for coding?
GLM-4.6 scores higher on coding benchmarks: 40.1 versus 32.9 in the Noometry coding category.
How many benchmarks do DBRX and GLM-4.6 share?
17 benchmarks have published results for both models. DBRX has 21 scored results on Noometry and GLM-4.6 has 29.