# Deepseek Coder v2 vs GLM-5.3-Flash

> GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 35.3 on the Noometry Index.

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

## Summary

- They share 17 benchmarks with published results for both. Deepseek Coder v2 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 writing & preference, where GLM-5.3-Flash leads 65.4 to 38.4.

## Snapshot

| | Deepseek Coder v2 | GLM-5.3-Flash |
|---|---|---|
| Provider | DeepSeek | Z.ai (Zhipu) |
| Noometry Index | 35.3 | 49.6 |
| Rank | 217 | 45 |
| Context | — | 1M |
| Input $/M | — | $0.15 |
| Output $/M | — | $0.50 |
| Weights | Open | Open |

## Coding

- Deepseek Coder v2: 37.5 (#178)
- GLM-5.3-Flash: 53.7 (#26)

| Benchmark | Deepseek Coder v2 | GLM-5.3-Flash |
|---|---|---|
| LMArena Coding | 1251 | 1506 |
| DeepSWE | — | 63.4% |
| FrontierCode | — | 31.8% |
| LMArena WebDev | — | 1614 |
| SciCode | — | 51.6% |
| BigCodeBench Instruct | 48.2% | — |
| BigCodeBench Complete | 59.7% | — |
| ALE-Bench | — | 303.55 |
| HumanEval+ | 82.3% | — |
| MBPP+ | 75.1% | — |

## Agentic & Tool Use

- Deepseek Coder v2: —
- GLM-5.3-Flash: 34.3 (#41)

| Benchmark | Deepseek Coder v2 | GLM-5.3-Flash |
|---|---|---|
| APEX-Agents | — | 52.8% |
| GDP.pdf | — | 14% |

## Reasoning

- Deepseek Coder v2: 22.6 (#175)
- GLM-5.3-Flash: 31.7 (#69)

| Benchmark | Deepseek Coder v2 | GLM-5.3-Flash |
|---|---|---|
| LMArena Hard Prompts | 1207 | 1490 |
| 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 | — | 151.9 |
| WinoGrande | 83.7% | — |

## Math

- Deepseek Coder v2: 32.3 (#188)
- GLM-5.3-Flash: 53.1 (#38)

| Benchmark | Deepseek Coder v2 | GLM-5.3-Flash |
|---|---|---|
| LMArena Math | 1241 | 1499 |
| FrontierMath (Tiers 1-3) | — | 55.8% |
| FrontierMath Tier 4 | — | 17.1% |
| OTIS Mock AIME 2024-2025 | — | 93.9% |
| ProofBench | — | 21% |
| GSM8K | 94.5% | — |

## Knowledge

- Deepseek Coder v2: 31.7 (#209)
- GLM-5.3-Flash: 58.4 (#33)

| Benchmark | Deepseek Coder v2 | GLM-5.3-Flash |
|---|---|---|
| LMArena Expert | 1181 | 1512 |
| GPQA Diamond | — | 90.2% |
| ARC (AI2) Challenge | 64.3% | — |

## Multimodal

- Deepseek Coder v2: —
- GLM-5.3-Flash: 42.6 (#25)

| Benchmark | Deepseek Coder v2 | GLM-5.3-Flash |
|---|---|---|
| LMArena Vision | — | 1304 |

## Multilingual

- Deepseek Coder v2: 36.4 (#235)
- GLM-5.3-Flash: 56.2 (#24)

| Benchmark | Deepseek Coder v2 | GLM-5.3-Flash |
|---|---|---|
| LMArena Non-English | 1182 | 1462 |
| LMArena Chinese | 1201 | 1529 |
| LMArena French | 1184 | 1495 |
| LMArena German | 1164 | 1457 |
| LMArena Japanese | 1126 | 1420 |
| LMArena Korean | 1104 | 1443 |
| LMArena Russian | 1188 | 1472 |
| LMArena Spanish | 1153 | 1471 |

## Instruction Following

- Deepseek Coder v2: 61.9 (#237)
- GLM-5.3-Flash: 77.8 (#17)

| Benchmark | Deepseek Coder v2 | GLM-5.3-Flash |
|---|---|---|
| LMArena Instruction Following | 1180 | 1480 |

## Long Context

- Deepseek Coder v2: 37.0 (#219)
- GLM-5.3-Flash: 45.3 (#41)

| Benchmark | Deepseek Coder v2 | GLM-5.3-Flash |
|---|---|---|
| LMArena Longer Query | 1219 | 1479 |

## Writing & Preference

- Deepseek Coder v2: 38.4 (#249)
- GLM-5.3-Flash: 65.4 (#49)

| Benchmark | Deepseek Coder v2 | GLM-5.3-Flash |
|---|---|---|
| LMArena Text | 1191 | 1471 |
| LMArena Creative Writing | 1120 | 1440 |
| LMArena Multi-Turn | 1176 | 1471 |

## FAQ

### Is Deepseek Coder v2 better than GLM-5.3-Flash?

GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 35.3 on the Noometry Index.

### Is Deepseek Coder v2 or GLM-5.3-Flash better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.7 versus 37.5 in the Noometry coding category.

### How many benchmarks do Deepseek Coder v2 and GLM-5.3-Flash share?

17 benchmarks have published results for both models. Deepseek Coder v2 has 24 scored results on Noometry and GLM-5.3-Flash has 36.