# DeepSeek-R1 vs GLM-4.5

> DeepSeek-R1 and GLM-4.5 score almost the same on the Noometry Index (42.3 vs 42.0), so choose on price, context window or the category you care about most.

- Canonical page: https://noometry.com/compare/deepseek-r1-vs-glm-4-5
- Last updated: 2026-10-11
- Shared benchmarks: 25

## Summary

- They share 25 benchmarks with published results for both. DeepSeek-R1 scores higher in 5 categories and GLM-4.5 in 3 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-4.5 leads 28.6 to 18.6.
- The biggest single-benchmark swing is Fiction.LiveBench: 75% for DeepSeek-R1 and 58.3% for GLM-4.5.
- DeepSeek-R1 is cheaper at $0.50 / $2.15 per million input/output tokens, against $0.60 / $2.20 for GLM-4.5.
- DeepSeek-R1 accepts more context: 164K tokens versus 131K.
- GLM-4.5 has downloadable open weights; the other is API-only.

## Snapshot

| | DeepSeek-R1 | GLM-4.5 |
|---|---|---|
| Provider | DeepSeek | Z.ai (Zhipu) |
| Noometry Index | 42.3 | 42.0 |
| Rank | 115 | 122 |
| Context | 164K | 131K |
| Input $/M | $0.50 | $0.60 |
| Output $/M | $2.15 | $2.20 |
| Weights | Proprietary | Open |

## Coding

- DeepSeek-R1: 46.3 (#68)
- GLM-4.5: 41.4 (#125)

| Benchmark | DeepSeek-R1 | GLM-4.5 |
|---|---|---|
| WeirdML | 41.6% | 40.6% |
| LMArena Coding | 1427 | 1434 |
| ALE-Bench | 804.12 | 344.82 |
| AlgoTune | 1.7 | 1.52 |
| SWE-bench Verified (bash only) | — | 54.2% |
| Aider Polyglot | 71.4% | — |
| SciCode | 35.7% | — |
| LiveBench Coding | 66.7% | — |

## Agentic & Tool Use

- DeepSeek-R1: 30.7 (#75)
- GLM-4.5: —

| Benchmark | DeepSeek-R1 | GLM-4.5 |
|---|---|---|
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| METR Time Horizons | 53.8% | — |

## Reasoning

- DeepSeek-R1: 18.6 (#278)
- GLM-4.5: 28.6 (#100)

| Benchmark | DeepSeek-R1 | GLM-4.5 |
|---|---|---|
| Kagi LLM Benchmark | 69.4% | 57.9% |
| LMArena Hard Prompts | 1416 | 1429 |
| ARC-AGI-2 | 1.3% | — |
| SimpleBench | 40.8% | — |
| ARC-AGI-1 | 21.2% | — |
| CritPt | 1.1% | — |
| LiveBench Reasoning | 83.2% | — |
| LiveBench Data Analysis | 69.8% | — |
| Epoch Capabilities Index | 141.29 | — |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |

## Math

- DeepSeek-R1: 43.8 (#79)
- GLM-4.5: 39.0 (#116)

| Benchmark | DeepSeek-R1 | GLM-4.5 |
|---|---|---|
| LMArena Math | 1400 | 1427 |
| OTIS Mock AIME 2024-2025 | 66.4% | — |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| MATH Level 5 | 96.6% | — |

## Knowledge

- DeepSeek-R1: 44.5 (#87)
- GLM-4.5: 35.9 (#179)

| Benchmark | DeepSeek-R1 | GLM-4.5 |
|---|---|---|
| Confabulations | 12.7% | 11.3% |
| LMArena Expert | 1394 | 1433 |
| GPQA Diamond | 76.3% | — |
| Humanity's Last Exam | — | 8.3% |
| MMLU-Pro | 79.3% | — |
| Vectara Hallucination Rate | 11.3% | — |
| GPQA (HELM) | 66.6% | — |

## Multilingual

- DeepSeek-R1: 52.4 (#85)
- GLM-4.5: 52.8 (#77)

| Benchmark | DeepSeek-R1 | GLM-4.5 |
|---|---|---|
| LMArena Non-English | 1412 | 1417 |
| LMArena Chinese | 1442 | 1465 |
| LMArena French | 1417 | 1418 |
| LMArena German | 1404 | 1407 |
| LMArena Japanese | 1391 | 1415 |
| LMArena Korean | 1360 | 1380 |
| LMArena Russian | 1423 | 1414 |
| LMArena Spanish | 1411 | 1454 |

## Instruction Following

- DeepSeek-R1: 72.0 (#143)
- GLM-4.5: 74.1 (#104)

| Benchmark | DeepSeek-R1 | GLM-4.5 |
|---|---|---|
| LMArena Instruction Following | 1382 | 1404 |
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |

## Long Context

- DeepSeek-R1: 45.4 (#36)
- GLM-4.5: 38.2 (#201)

| Benchmark | DeepSeek-R1 | GLM-4.5 |
|---|---|---|
| Fiction.LiveBench | 75% | 58.3% |
| LMArena Longer Query | 1391 | 1412 |

## Writing & Preference

- DeepSeek-R1: 61.4 (#88)
- GLM-4.5: 57.5 (#127)

| Benchmark | DeepSeek-R1 | GLM-4.5 |
|---|---|---|
| LMArena Text | 1428 | 1430 |
| LMArena Creative Writing | 1405 | 1395 |
| Short-Story Creative Writing | 83% | 73.4% |
| EQ-Bench Creative Writing | 1500 | 1343 |
| LMArena Multi-Turn | 1405 | 1415 |
| WildBench | 82.8% | — |
| LiveBench Language | 48.5% | — |

## FAQ

### Is DeepSeek-R1 better than GLM-4.5?

DeepSeek-R1 and GLM-4.5 score almost the same on the Noometry Index (42.3 vs 42.0), so choose on price, context window or the category you care about most.

### Which is cheaper, DeepSeek-R1 or GLM-4.5?

DeepSeek-R1 is cheaper. It lists at $0.50 per million input tokens and $2.15 per million output tokens; GLM-4.5 lists at $0.60 and $2.20.

### Is DeepSeek-R1 or GLM-4.5 better for coding?

DeepSeek-R1 scores higher on coding benchmarks: 46.3 versus 41.4 in the Noometry coding category.

### Which has the bigger context window?

DeepSeek-R1 does, with 164K tokens against 131K.

### How many benchmarks do DeepSeek-R1 and GLM-4.5 share?

25 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and GLM-4.5 has 27.