# DeepSeek-V2.5 (Sep 2024) vs GLM-5.3

> GLM-5.3 is the stronger model overall, scoring 54.8 to 37.6 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v2-5-vs-glm-5-3
- Last updated: 2026-10-10
- Shared benchmarks: 17

## Summary

- They share 17 benchmarks with published results for both. DeepSeek-V2.5 (Sep 2024) scores higher in 0 categories and GLM-5.3 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in coding, where GLM-5.3 leads 59.5 to 31.7.

## Snapshot

| | DeepSeek-V2.5 (Sep 2024) | GLM-5.3 |
|---|---|---|
| Provider | DeepSeek | Z.ai (Zhipu) |
| Noometry Index | 37.6 | 54.8 |
| Rank | 200 | 26 |
| Context | — | 1M |
| Input $/M | — | $1.40 |
| Output $/M | — | $4.40 |
| Weights | Open | Open |

## Coding

- DeepSeek-V2.5 (Sep 2024): 31.7 (#281)
- GLM-5.3: 59.5 (#14)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | GLM-5.3 |
|---|---|---|
| LMArena Coding | 1309 | 1496 |
| DeepSWE | — | 69% |
| FrontierCode | — | 40.1% |
| Aider Polyglot | 17.8% | — |
| CursorBench | — | 42.6% |
| LMArena WebDev | — | 1622 |
| FrontierSWE | — | 30.2% |
| SciCode | — | 59% |
| WeirdML | — | 75.4% |
| BigCodeBench Instruct | 48.6% | — |
| BigCodeBench Complete | 53.2% | — |
| ALE-Bench | — | 1,317 |
| HumanEval+ | 83.5% | — |
| MBPP+ | 74.1% | — |

## Agentic & Tool Use

- DeepSeek-V2.5 (Sep 2024): —
- GLM-5.3: 36.4 (#38)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | GLM-5.3 |
|---|---|---|
| APEX-Agents | — | 56.6% |
| Vending-Bench 2 | — | 8,164 |

## Reasoning

- DeepSeek-V2.5 (Sep 2024): 25.6 (#145)
- GLM-5.3: 46.1 (#46)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | GLM-5.3 |
|---|---|---|
| LMArena Hard Prompts | 1289 | 1489 |
| NYT Connections (extended) | — | 74.2% |
| CritPt | — | 19.1% |
| Chess Puzzles | — | 21% |
| Mystery Game Puzzles | — | 33% |
| DTBench | — | 87.7% |
| LMCA | — | 55.5% |
| Bench to the Future 3 | — | 0.15 |
| Epoch Capabilities Index | — | 155.61 |

## Math

- DeepSeek-V2.5 (Sep 2024): 35.9 (#177)
- GLM-5.3: 62.3 (#33)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | GLM-5.3 |
|---|---|---|
| LMArena Math | 1288 | 1489 |
| FrontierMath (Tiers 1-3) | — | 68.8% |
| FrontierMath Tier 4 | — | 29.3% |
| OTIS Mock AIME 2024-2025 | — | 91.1% |
| ProofBench | — | 49% |

## Knowledge

- DeepSeek-V2.5 (Sep 2024): 34.8 (#193)
- GLM-5.3: 58.3 (#37)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | GLM-5.3 |
|---|---|---|
| LMArena Expert | 1266 | 1516 |
| GPQA Diamond | — | 90.9% |
| SimpleQA Verified | — | 41% |

## Multilingual

- DeepSeek-V2.5 (Sep 2024): 42.5 (#193)
- GLM-5.3: 55.7 (#28)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | GLM-5.3 |
|---|---|---|
| LMArena Non-English | 1273 | 1457 |
| LMArena Chinese | 1318 | 1528 |
| LMArena French | 1289 | 1499 |
| LMArena German | 1258 | 1499 |
| LMArena Japanese | 1228 | 1453 |
| LMArena Korean | 1209 | 1472 |
| LMArena Russian | 1289 | 1463 |
| LMArena Spanish | 1248 | 1460 |

## Instruction Following

- DeepSeek-V2.5 (Sep 2024): 67.5 (#194)
- GLM-5.3: 77.5 (#23)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | GLM-5.3 |
|---|---|---|
| LMArena Instruction Following | 1280 | 1477 |

## Long Context

- DeepSeek-V2.5 (Sep 2024): 39.5 (#174)
- GLM-5.3: 45.4 (#41)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | GLM-5.3 |
|---|---|---|
| LMArena Longer Query | 1301 | 1482 |

## Writing & Preference

- DeepSeek-V2.5 (Sep 2024): 49.8 (#187)
- GLM-5.3: 75.7 (#6)

| Benchmark | DeepSeek-V2.5 (Sep 2024) | GLM-5.3 |
|---|---|---|
| LMArena Text | 1294 | 1471 |
| LMArena Creative Writing | 1285 | 1457 |
| LMArena Multi-Turn | 1297 | 1472 |
| EQ-Bench Creative Writing | — | 2075 |

## FAQ

### Is DeepSeek-V2.5 (Sep 2024) better than GLM-5.3?

GLM-5.3 is the stronger model overall, scoring 54.8 to 37.6 on the Noometry Index.

### Is DeepSeek-V2.5 (Sep 2024) or GLM-5.3 better for coding?

GLM-5.3 scores higher on coding benchmarks: 59.5 versus 31.7 in the Noometry coding category.

### How many benchmarks do DeepSeek-V2.5 (Sep 2024) and GLM-5.3 share?

17 benchmarks have published results for both models. DeepSeek-V2.5 (Sep 2024) has 22 scored results on Noometry and GLM-5.3 has 42.