Model comparison
DeepSeek LLM 67B vs GLM-4.5
GLM-4.5 is the stronger model overall, scoring 42.0 to 24.9 on the Noometry Index.
Last verified . 10 shared benchmarks.
DeepSeekSummary
- They share 10 benchmarks with published results for both. DeepSeek LLM 67B scores higher in 0 categories and GLM-4.5 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-4.5 leads 39.0 to 8.7.
Side by side
| DeepSeek LLM 67B | GLM-4.5 | |
|---|---|---|
| Provider | DeepSeek | Z.ai (Zhipu) |
| Noometry Index | 24.9 | 42.0 |
| Released | 2023-11-29 | 2025-07-27 |
| Weights | Open | Open |
| Context window | — | 131K |
| Max output | — | 98K |
| Input $ / M tokens | — | $0.60 |
| Output $ / M tokens | — | $2.20 |
| Results tracked | 15 | 27 |
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Category by category
Coding GLM-4.5 leads
DeepSeek LLM 67B: 31.9 (#278), GLM-4.5: 41.4 (#125)
| Benchmark | DeepSeek LLM 67B | GLM-4.5 |
|---|---|---|
| LMArena Coding | 1096 | 1434 |
| SWE-bench Verified (bash only) | — | 54.2% |
| WeirdML | — | 40.6% |
| ALE-Bench | — | 344.82 |
| AlgoTune | — | 1.52 |
Reasoning GLM-4.5 leads
DeepSeek LLM 67B: 16.5 (#304), GLM-4.5: 28.6 (#100)
| Benchmark | DeepSeek LLM 67B | GLM-4.5 |
|---|---|---|
| LMArena Hard Prompts | 1070 | 1429 |
| Kagi LLM Benchmark | — | 57.9% |
| Chess Puzzles | 0% | — |
| Epoch Capabilities Index | 110.5 | — |
Math GLM-4.5 leads
DeepSeek LLM 67B: 8.7 (#324), GLM-4.5: 39.0 (#116)
| Benchmark | DeepSeek LLM 67B | GLM-4.5 |
|---|---|---|
| LMArena Math | 1108 | 1427 |
| OTIS Mock AIME 2024-2025 | 0.8% | — |
| MATH Level 5 | 6.4% | — |
Knowledge GLM-4.5 leads
DeepSeek LLM 67B: 7.0 (#313), GLM-4.5: 35.9 (#179)
| Benchmark | DeepSeek LLM 67B | GLM-4.5 |
|---|---|---|
| GPQA Diamond | 24.6% | — |
| Humanity's Last Exam | — | 8.3% |
| Confabulations | — | 11.3% |
| LMArena Expert | — | 1433 |
Multilingual GLM-4.5 leads
DeepSeek LLM 67B: 29.4 (#267), GLM-4.5: 52.8 (#77)
| Benchmark | DeepSeek LLM 67B | GLM-4.5 |
|---|---|---|
| LMArena Non-English | 1073 | 1417 |
| LMArena Chinese | 1132 | 1465 |
| LMArena French | — | 1418 |
| LMArena German | — | 1407 |
| LMArena Japanese | — | 1415 |
| LMArena Korean | — | 1380 |
| LMArena Russian | — | 1414 |
| LMArena Spanish | — | 1454 |
Instruction Following GLM-4.5 leads
DeepSeek LLM 67B: 55.4 (#277), GLM-4.5: 74.1 (#104)
| Benchmark | DeepSeek LLM 67B | GLM-4.5 |
|---|---|---|
| LMArena Instruction Following | 1079 | 1404 |
Long Context GLM-4.5 leads
DeepSeek LLM 67B: 33.1 (#265), GLM-4.5: 38.2 (#201)
| Benchmark | DeepSeek LLM 67B | GLM-4.5 |
|---|---|---|
| LMArena Longer Query | 1092 | 1412 |
| Fiction.LiveBench | — | 58.3% |
Writing & Preference GLM-4.5 leads
DeepSeek LLM 67B: 31.6 (#282), GLM-4.5: 57.5 (#127)
| Benchmark | DeepSeek LLM 67B | GLM-4.5 |
|---|---|---|
| LMArena Text | 1105 | 1430 |
| LMArena Creative Writing | 1067 | 1395 |
| LMArena Multi-Turn | 1082 | 1415 |
| Short-Story Creative Writing | — | 73.4% |
| EQ-Bench Creative Writing | — | 1343 |
Frequently asked questions
Is DeepSeek LLM 67B better than GLM-4.5?
GLM-4.5 is the stronger model overall, scoring 42.0 to 24.9 on the Noometry Index.
Is DeepSeek LLM 67B or GLM-4.5 better for coding?
GLM-4.5 scores higher on coding benchmarks: 41.4 versus 31.9 in the Noometry coding category.
How many benchmarks do DeepSeek LLM 67B and GLM-4.5 share?
10 benchmarks have published results for both models. DeepSeek LLM 67B has 15 scored results on Noometry and GLM-4.5 has 27.