Model comparison
DeepSeek-V2 (MoE-236B, May 2024) vs GLM-5.2
GLM-5.2 has enough public results to be ranked (#44); DeepSeek-V2 (MoE-236B, May 2024) does not yet, so treat this comparison as directional.
Last verified . 1 shared benchmarks.
DeepSeekSummary
- They share 1 benchmark with published results for both. DeepSeek-V2 (MoE-236B, May 2024) scores higher in 0 categories and GLM-5.2 in 1 category; one gap is clear of the uncertainty.
- The widest gap is in coding, where GLM-5.2 leads 51.3 to 40.4.
Side by side
| DeepSeek-V2 (MoE-236B, May 2024) | GLM-5.2 | |
|---|---|---|
| Provider | DeepSeek | Z.ai (Zhipu) |
| Noometry Index | 40.3 | 51.1 |
| Released | 2024-05-07 | 2026-06-13 |
| Weights | Open | Open |
| Context window | — | 1M |
| Max output | — | 131K |
| Input $ / M tokens | — | $1.40 |
| Output $ / M tokens | — | $4.40 |
| Results tracked | 10 | 51 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GLM-5.2 leads
DeepSeek-V2 (MoE-236B, May 2024): 40.4 (#139), GLM-5.2: 51.3 (#41)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GLM-5.2 |
|---|---|---|
| SWE-bench Verified | — | 78.7% |
| DeepSWE | — | 43.8% |
| FrontierCode | — | 24.5% |
| LMArena WebDev | — | 1603 |
| SciCode | — | 50.5% |
| WeirdML | — | 70.1% |
| BigCodeBench Instruct | 48.9% | — |
| LMArena Coding | — | 1485 |
| BigCodeBench Complete | 59.4% | — |
| ALE-Bench | — | 1,047 |
Agentic & Tool Use Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, GLM-5.2: 32.4 (#63)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GLM-5.2 |
|---|---|---|
| APEX-Agents | — | 45.2% |
| τ²-bench Banking | — | 37.1% |
| PostTrainBench | — | 31.7% |
| GBAEval | — | 0% |
| Vending-Bench 2 | — | 8,314 |
Reasoning Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, GLM-5.2: 42.3 (#52)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GLM-5.2 |
|---|---|---|
| Epoch Capabilities Index | 124.77 | 151.78 |
| ARC-AGI-2 | — | 22.8% |
| SimpleBench | — | 58.8% |
| Kagi LLM Benchmark | — | 62.6% |
| NYT Connections (extended) | — | 74.3% |
| ARC-AGI-1 | — | 77% |
| CritPt | — | 20.9% |
| Chess Puzzles | — | 21% |
| EBR-Bench | — | 9.5% |
| LMArena Hard Prompts | — | 1480 |
| Mystery Game Puzzles | — | 19% |
| DTBench | — | 93.6% |
| LMCA | — | 45.8% |
| Surface Evolver Bench | — | 55.6% |
| BIG-Bench Hard | 78.8% | — |
| HellaSwag | 87.1% | — |
| PIQA | 83.9% | — |
| WinoGrande | 86.3% | — |
Math Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, GLM-5.2: 55.7 (#43)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GLM-5.2 |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 59.2% |
| FrontierMath Tier 4 | — | 29.3% |
| MathArena Final-Answer Competitions | — | 67.6% |
| OTIS Mock AIME 2024-2025 | — | 86.4% |
| ProofBench | — | 35% |
| LMArena Math | — | 1482 |
Knowledge Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, GLM-5.2: 57.1 (#40)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GLM-5.2 |
|---|---|---|
| GPQA Diamond | — | 91.9% |
| SimpleQA Verified | — | 34.2% |
| LMArena Expert | — | 1486 |
| ARC (AI2) Challenge | 92.2% | — |
| MMLU | 78.4% | — |
| TriviaQA | 80% | — |
Multilingual Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, GLM-5.2: 55.8 (#26)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GLM-5.2 |
|---|---|---|
| LMArena Non-English | — | 1459 |
| LMArena Chinese | — | 1519 |
| LMArena French | — | 1479 |
| LMArena German | — | 1468 |
| LMArena Japanese | — | 1451 |
| LMArena Korean | — | 1445 |
| LMArena Russian | — | 1466 |
| LMArena Spanish | — | 1477 |
Instruction Following Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, GLM-5.2: 76.9 (#34)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GLM-5.2 |
|---|---|---|
| LMArena Instruction Following | — | 1465 |
Long Context Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, GLM-5.2: 45.3 (#43)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GLM-5.2 |
|---|---|---|
| LMArena Longer Query | — | 1479 |
Writing & Preference Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, GLM-5.2: 70.4 (#21)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GLM-5.2 |
|---|---|---|
| LMArena Text | — | 1470 |
| LMArena Creative Writing | — | 1462 |
| EQ-Bench Creative Writing | — | 1757 |
| EQ-Bench 4 | — | 1222 |
| LMArena Multi-Turn | — | 1469 |
Frequently asked questions
Is DeepSeek-V2 (MoE-236B, May 2024) better than GLM-5.2?
GLM-5.2 has enough public results to be ranked (#44); DeepSeek-V2 (MoE-236B, May 2024) does not yet, so treat this comparison as directional.
Is DeepSeek-V2 (MoE-236B, May 2024) or GLM-5.2 better for coding?
GLM-5.2 scores higher on coding benchmarks: 51.3 versus 40.4 in the Noometry coding category.
How many benchmarks do DeepSeek-V2 (MoE-236B, May 2024) and GLM-5.2 share?
1 benchmark has published results for both models. DeepSeek-V2 (MoE-236B, May 2024) has 10 scored results on Noometry and GLM-5.2 has 51.