# DeepSeek V4 Flash vs GLM-5.3-Flash

> DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 49.6 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v4-flash-vs-glm-5-3-flash
- Last updated: 2026-10-10
- Shared benchmarks: 30

## Summary

- They share 30 benchmarks with published results for both. DeepSeek V4 Flash scores higher in 2 categories and GLM-5.3-Flash in 6 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek V4 Flash leads 53.7 to 31.7.
- The biggest single-benchmark swing is ProofBench: 56% for DeepSeek V4 Flash and 21% for GLM-5.3-Flash.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.15 / $0.60 for DeepSeek V4 Flash.

## Snapshot

| | DeepSeek V4 Flash | GLM-5.3-Flash |
|---|---|---|
| Provider | DeepSeek | Z.ai (Zhipu) |
| Noometry Index | 53.6 | 49.6 |
| Rank | 32 | 45 |
| Context | 1M | 1M |
| Input $/M | $0.15 | $0.15 |
| Output $/M | $0.60 | $0.50 |
| Weights | Open | Open |

## Coding

- DeepSeek V4 Flash: 47.6 (#56)
- GLM-5.3-Flash: 53.7 (#26)

| Benchmark | DeepSeek V4 Flash | GLM-5.3-Flash |
|---|---|---|
| FrontierCode | 18.8% | 31.8% |
| LMArena WebDev | 1581 | 1614 |
| SciCode | 49.9% | 51.6% |
| LMArena Coding | 1457 | 1506 |
| ALE-Bench | 1,306 | 303.55 |
| DeepSWE | — | 63.4% |
| WeirdML | 63% | — |

## Agentic & Tool Use

- DeepSeek V4 Flash: —
- GLM-5.3-Flash: 34.3 (#41)

| Benchmark | DeepSeek V4 Flash | GLM-5.3-Flash |
|---|---|---|
| APEX-Agents | — | 52.8% |
| GDP.pdf | — | 14% |

## Reasoning

- DeepSeek V4 Flash: 53.7 (#25)
- GLM-5.3-Flash: 31.7 (#69)

| Benchmark | DeepSeek V4 Flash | GLM-5.3-Flash |
|---|---|---|
| CritPt | 16.6% | 15.4% |
| Chess Puzzles | 33% | 14% |
| LMArena Hard Prompts | 1443 | 1490 |
| Mystery Game Puzzles | 34% | 8% |
| Epoch Capabilities Index | 154.51 | 151.9 |
| ARC-AGI-2 | 61.4% | — |
| SimpleBench | 61.1% | — |
| Kagi LLM Benchmark | 52.2% | — |
| NYT Connections (extended) | 89.6% | — |
| ARC-AGI-1 | 89% | — |
| DTBench | 90.9% | — |
| LMCA | 41.7% | — |
| Surface Evolver Bench | — | 52.5% |
| Bench to the Future 3 | — | 0.15 |

## Math

- DeepSeek V4 Flash: 60.0 (#31)
- GLM-5.3-Flash: 53.1 (#38)

| Benchmark | DeepSeek V4 Flash | GLM-5.3-Flash |
|---|---|---|
| FrontierMath (Tiers 1-3) | 57.5% | 55.8% |
| FrontierMath Tier 4 | 24.4% | 17.1% |
| OTIS Mock AIME 2024-2025 | 94.4% | 93.9% |
| ProofBench | 56% | 21% |
| LMArena Math | 1427 | 1499 |
| MathArena Final-Answer Competitions | 76.5% | — |

## Knowledge

- DeepSeek V4 Flash: 55.5 (#43)
- GLM-5.3-Flash: 58.4 (#33)

| Benchmark | DeepSeek V4 Flash | GLM-5.3-Flash |
|---|---|---|
| GPQA Diamond | 91% | 90.2% |
| LMArena Expert | 1441 | 1512 |
| SimpleQA Verified | 33.6% | — |

## Multimodal

- DeepSeek V4 Flash: —
- GLM-5.3-Flash: 42.6 (#25)

| Benchmark | DeepSeek V4 Flash | GLM-5.3-Flash |
|---|---|---|
| LMArena Vision | — | 1304 |

## Multilingual

- DeepSeek V4 Flash: 53.2 (#71)
- GLM-5.3-Flash: 56.2 (#24)

| Benchmark | DeepSeek V4 Flash | GLM-5.3-Flash |
|---|---|---|
| LMArena Non-English | 1420 | 1462 |
| LMArena Chinese | 1467 | 1529 |
| LMArena French | 1438 | 1495 |
| LMArena German | 1418 | 1457 |
| LMArena Japanese | 1403 | 1420 |
| LMArena Korean | 1383 | 1443 |
| LMArena Russian | 1428 | 1472 |
| LMArena Spanish | 1437 | 1471 |

## Instruction Following

- DeepSeek V4 Flash: 75.1 (#76)
- GLM-5.3-Flash: 77.8 (#17)

| Benchmark | DeepSeek V4 Flash | GLM-5.3-Flash |
|---|---|---|
| LMArena Instruction Following | 1421 | 1480 |

## Long Context

- DeepSeek V4 Flash: 43.8 (#81)
- GLM-5.3-Flash: 45.3 (#41)

| Benchmark | DeepSeek V4 Flash | GLM-5.3-Flash |
|---|---|---|
| LMArena Longer Query | 1435 | 1479 |

## Writing & Preference

- DeepSeek V4 Flash: 64.0 (#59)
- GLM-5.3-Flash: 65.4 (#49)

| Benchmark | DeepSeek V4 Flash | GLM-5.3-Flash |
|---|---|---|
| LMArena Text | 1432 | 1471 |
| LMArena Creative Writing | 1403 | 1440 |
| LMArena Multi-Turn | 1449 | 1471 |
| EQ-Bench Creative Writing | 1559 | — |

## FAQ

### Is DeepSeek V4 Flash better than GLM-5.3-Flash?

DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 49.6 on the Noometry Index.

### Which is cheaper, DeepSeek V4 Flash or GLM-5.3-Flash?

GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; DeepSeek V4 Flash lists at $0.15 and $0.60.

### Is DeepSeek V4 Flash or GLM-5.3-Flash better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.7 versus 47.6 in the Noometry coding category.

### Which has the bigger context window?

Both accept 1M tokens.

### How many benchmarks do DeepSeek V4 Flash and GLM-5.3-Flash share?

30 benchmarks have published results for both models. DeepSeek V4 Flash has 41 scored results on Noometry and GLM-5.3-Flash has 36.