# DeepSeek V4 Flash vs GLM-5

> DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 45.9 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v4-flash-vs-glm-5
- Last updated: 2026-10-10
- Shared benchmarks: 31

## Summary

- They share 31 benchmarks with published results for both. DeepSeek V4 Flash scores higher in 3 categories and GLM-5 in 5 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek V4 Flash leads 53.7 to 27.6.
- The biggest single-benchmark swing is ARC-AGI-2: 61.4% for DeepSeek V4 Flash and 4.9% for GLM-5.
- DeepSeek V4 Flash is cheaper at $0.15 / $0.60 per million input/output tokens, against $1 / $3.20 for GLM-5.
- DeepSeek V4 Flash accepts more context: 1M tokens versus 205K.

## Snapshot

| | DeepSeek V4 Flash | GLM-5 |
|---|---|---|
| Provider | DeepSeek | Z.ai (Zhipu) |
| Noometry Index | 53.6 | 45.9 |
| Rank | 32 | 59 |
| Context | 1M | 205K |
| Input $/M | $0.15 | $1 |
| Output $/M | $0.60 | $3.20 |
| Weights | Open | Open |

## Coding

- DeepSeek V4 Flash: 47.6 (#56)
- GLM-5: 49.0 (#47)

| Benchmark | DeepSeek V4 Flash | GLM-5 |
|---|---|---|
| LMArena WebDev | 1581 | 1434 |
| WeirdML | 63% | 48.2% |
| LMArena Coding | 1457 | 1461 |
| ALE-Bench | 1,306 | 765.62 |
| SWE-bench Verified | — | 72.1% |
| FrontierCode | 18.8% | — |
| SWE-bench Verified (bash only) | — | 72.8% |
| SWE-bench Multilingual | — | 69.7% |
| SciCode | 49.9% | — |

## Agentic & Tool Use

- DeepSeek V4 Flash: —
- GLM-5: 30.9 (#65)

| Benchmark | DeepSeek V4 Flash | GLM-5 |
|---|---|---|
| Terminal-Bench | — | 52.4% |
| τ²-bench Airline | — | 82.5% |
| τ²-bench Banking | — | 9.8% |
| τ²-bench Retail | — | 73.7% |
| τ²-bench Telecom | — | 86.8% |
| Vending-Bench 2 | — | 4,432 |

## Reasoning

- DeepSeek V4 Flash: 53.7 (#25)
- GLM-5: 27.6 (#100)

| Benchmark | DeepSeek V4 Flash | GLM-5 |
|---|---|---|
| ARC-AGI-2 | 61.4% | 4.9% |
| SimpleBench | 61.1% | 53.2% |
| Kagi LLM Benchmark | 52.2% | 75% |
| NYT Connections (extended) | 89.6% | 74.8% |
| ARC-AGI-1 | 89% | 44.7% |
| Chess Puzzles | 33% | 10% |
| LMArena Hard Prompts | 1443 | 1451 |
| Epoch Capabilities Index | 154.51 | 145.84 |
| CritPt | 16.6% | — |
| Mystery Game Puzzles | 34% | — |
| DTBench | 90.9% | — |
| LMCA | 41.7% | — |
| ForecastBench | — | 61 |

## Math

- DeepSeek V4 Flash: 60.0 (#31)
- GLM-5: 44.8 (#66)

| Benchmark | DeepSeek V4 Flash | GLM-5 |
|---|---|---|
| MathArena Final-Answer Competitions | 76.5% | 65.7% |
| OTIS Mock AIME 2024-2025 | 94.4% | 80% |
| LMArena Math | 1427 | 1440 |
| FrontierMath (Tiers 1-3) | 57.5% | — |
| FrontierMath Tier 4 | 24.4% | — |
| ProofBench | 56% | — |
| FrontierMath (Feb 2025 set) | — | 16.4% |
| FrontierMath Tier 4 (v1) | — | 2.1% |

## Knowledge

- DeepSeek V4 Flash: 55.5 (#43)
- GLM-5: 52.2 (#59)

| Benchmark | DeepSeek V4 Flash | GLM-5 |
|---|---|---|
| GPQA Diamond | 91% | 87.8% |
| LMArena Expert | 1441 | 1454 |
| SimpleQA Verified | 33.6% | — |
| Vectara Hallucination Rate | — | 10.1% |

## Multilingual

- DeepSeek V4 Flash: 53.2 (#71)
- GLM-5: 53.9 (#57)

| Benchmark | DeepSeek V4 Flash | GLM-5 |
|---|---|---|
| LMArena Non-English | 1420 | 1430 |
| LMArena Chinese | 1467 | 1511 |
| LMArena French | 1438 | 1455 |
| LMArena German | 1418 | 1445 |
| LMArena Japanese | 1403 | 1416 |
| LMArena Korean | 1383 | 1421 |
| LMArena Russian | 1428 | 1436 |
| LMArena Spanish | 1437 | 1452 |

## Instruction Following

- DeepSeek V4 Flash: 75.1 (#76)
- GLM-5: 75.4 (#64)

| Benchmark | DeepSeek V4 Flash | GLM-5 |
|---|---|---|
| LMArena Instruction Following | 1421 | 1427 |

## Long Context

- DeepSeek V4 Flash: 43.8 (#81)
- GLM-5: 44.6 (#58)

| Benchmark | DeepSeek V4 Flash | GLM-5 |
|---|---|---|
| LMArena Longer Query | 1435 | 1446 |
| CL-bench | — | 18.7% |

## Writing & Preference

- DeepSeek V4 Flash: 64.0 (#59)
- GLM-5: 66.2 (#38)

| Benchmark | DeepSeek V4 Flash | GLM-5 |
|---|---|---|
| LMArena Text | 1432 | 1446 |
| LMArena Creative Writing | 1403 | 1438 |
| EQ-Bench Creative Writing | 1559 | 1601 |
| LMArena Multi-Turn | 1449 | 1456 |

## FAQ

### Is DeepSeek V4 Flash better than GLM-5?

DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 45.9 on the Noometry Index.

### Which is cheaper, DeepSeek V4 Flash or GLM-5?

DeepSeek V4 Flash is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; GLM-5 lists at $1 and $3.20.

### Is DeepSeek V4 Flash or GLM-5 better for coding?

GLM-5 scores higher on coding benchmarks: 49.0 versus 47.6 in the Noometry coding category.

### Which has the bigger context window?

DeepSeek V4 Flash does, with 1M tokens against 205K.

### How many benchmarks do DeepSeek V4 Flash and GLM-5 share?

31 benchmarks have published results for both models. DeepSeek V4 Flash has 41 scored results on Noometry and GLM-5 has 45.