# DeepSeek V4.1 Flash vs GLM-5.3

> GLM-5.3 is the stronger model overall, scoring 54.8 to 52.8 on the Noometry Index. DeepSeek V4.1 Flash costs 8.2× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v4-1-flash-vs-glm-5-3
- Last updated: 2026-10-10
- Shared benchmarks: 33

## Summary

- They share 33 benchmarks with published results for both. DeepSeek V4.1 Flash scores higher in 2 categories and GLM-5.3 in 7 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-5.3 leads 75.7 to 65.4.
- The biggest single-benchmark swing is APEX-Agents: 39.5% for DeepSeek V4.1 Flash and 56.6% for GLM-5.3.
- DeepSeek V4.1 Flash is cheaper at $0.15 / $0.60 per million input/output tokens, against $1.40 / $4.40 for GLM-5.3.

## Snapshot

| | DeepSeek V4.1 Flash | GLM-5.3 |
|---|---|---|
| Provider | DeepSeek | Z.ai (Zhipu) |
| Noometry Index | 52.8 | 54.8 |
| Rank | 38 | 26 |
| Context | 1M | 1M |
| Input $/M | $0.15 | $1.40 |
| Output $/M | $0.60 | $4.40 |
| Weights | Open | Open |

## Coding

- DeepSeek V4.1 Flash: 52.9 (#32)
- GLM-5.3: 59.5 (#14)

| Benchmark | DeepSeek V4.1 Flash | GLM-5.3 |
|---|---|---|
| LMArena WebDev | 1619 | 1622 |
| SciCode | 51.9% | 59% |
| LMArena Coding | 1506 | 1496 |
| ALE-Bench | 1,092 | 1,317 |
| DeepSWE | — | 69% |
| FrontierCode | — | 40.1% |
| CursorBench | — | 42.6% |
| FrontierSWE | — | 30.2% |
| WeirdML | — | 75.4% |

## Agentic & Tool Use

- DeepSeek V4.1 Flash: 31.2 (#69)
- GLM-5.3: 36.4 (#38)

| Benchmark | DeepSeek V4.1 Flash | GLM-5.3 |
|---|---|---|
| APEX-Agents | 39.5% | 56.6% |
| GDP.pdf | 19.8% | — |
| Vending-Bench 2 | — | 8,164 |

## Reasoning

- DeepSeek V4.1 Flash: 50.2 (#36)
- GLM-5.3: 46.1 (#46)

| Benchmark | DeepSeek V4.1 Flash | GLM-5.3 |
|---|---|---|
| NYT Connections (extended) | 89.6% | 74.2% |
| CritPt | 14.3% | 19.1% |
| LMArena Hard Prompts | 1483 | 1489 |
| Mystery Game Puzzles | 43% | 33% |
| DTBench | 89.9% | 87.7% |
| LMCA | 47% | 55.5% |
| Epoch Capabilities Index | 154.9 | 155.61 |
| Chess Puzzles | — | 21% |
| Surface Evolver Bench | 46.3% | — |
| Bench to the Future 3 | — | 0.15 |

## Math

- DeepSeek V4.1 Flash: 66.7 (#25)
- GLM-5.3: 62.3 (#33)

| Benchmark | DeepSeek V4.1 Flash | GLM-5.3 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 67.4% | 68.8% |
| FrontierMath Tier 4 | 26.8% | 29.3% |
| OTIS Mock AIME 2024-2025 | 98.3% | 91.1% |
| ProofBench | 54% | 49% |
| LMArena Math | 1477 | 1489 |

## Knowledge

- DeepSeek V4.1 Flash: 57.9 (#38)
- GLM-5.3: 58.3 (#37)

| Benchmark | DeepSeek V4.1 Flash | GLM-5.3 |
|---|---|---|
| GPQA Diamond | 89.8% | 90.9% |
| LMArena Expert | 1506 | 1516 |
| SimpleQA Verified | — | 41% |

## Multimodal

- DeepSeek V4.1 Flash: 39.1 (#61)
- GLM-5.3: —

| Benchmark | DeepSeek V4.1 Flash | GLM-5.3 |
|---|---|---|
| LMArena Vision | 1277 | — |
| Furniture Assembly | 34.2% | — |

## Multilingual

- DeepSeek V4.1 Flash: 55.0 (#35)
- GLM-5.3: 55.7 (#28)

| Benchmark | DeepSeek V4.1 Flash | GLM-5.3 |
|---|---|---|
| LMArena Non-English | 1448 | 1457 |
| LMArena Chinese | 1497 | 1528 |
| LMArena French | 1452 | 1499 |
| LMArena German | 1484 | 1499 |
| LMArena Japanese | 1412 | 1453 |
| LMArena Korean | 1452 | 1472 |
| LMArena Russian | 1471 | 1463 |
| LMArena Spanish | 1459 | 1460 |

## Instruction Following

- DeepSeek V4.1 Flash: 77.3 (#26)
- GLM-5.3: 77.5 (#23)

| Benchmark | DeepSeek V4.1 Flash | GLM-5.3 |
|---|---|---|
| LMArena Instruction Following | 1474 | 1477 |

## Long Context

- DeepSeek V4.1 Flash: 45.2 (#47)
- GLM-5.3: 45.4 (#41)

| Benchmark | DeepSeek V4.1 Flash | GLM-5.3 |
|---|---|---|
| LMArena Longer Query | 1475 | 1482 |

## Writing & Preference

- DeepSeek V4.1 Flash: 65.4 (#48)
- GLM-5.3: 75.7 (#6)

| Benchmark | DeepSeek V4.1 Flash | GLM-5.3 |
|---|---|---|
| LMArena Text | 1462 | 1471 |
| LMArena Creative Writing | 1435 | 1457 |
| EQ-Bench Creative Writing | 1540 | 2075 |
| LMArena Multi-Turn | 1457 | 1472 |

## FAQ

### Is DeepSeek V4.1 Flash better than GLM-5.3?

GLM-5.3 is the stronger model overall, scoring 54.8 to 52.8 on the Noometry Index. DeepSeek V4.1 Flash costs 8.2× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek V4.1 Flash or GLM-5.3?

DeepSeek V4.1 Flash is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; GLM-5.3 lists at $1.40 and $4.40.

### Is DeepSeek V4.1 Flash or GLM-5.3 better for coding?

GLM-5.3 scores higher on coding benchmarks: 59.5 versus 52.9 in the Noometry coding category.

### Which has the bigger context window?

Both accept 1M tokens.

### How many benchmarks do DeepSeek V4.1 Flash and GLM-5.3 share?

33 benchmarks have published results for both models. DeepSeek V4.1 Flash has 37 scored results on Noometry and GLM-5.3 has 42.