# DeepSeek-V3.1 vs GLM-4.7

> DeepSeek-V3.1 and GLM-4.7 score almost the same on the Noometry Index (42.8 vs 42.0), so choose on price, context window or the category you care about most.

- Canonical page: https://noometry.com/compare/deepseek-v3-1-vs-glm-4-7
- Last updated: 2026-10-11
- Shared benchmarks: 21

## Summary

- They share 21 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 2 categories and GLM-4.7 in 6 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in long context, where GLM-4.7 leads 42.8 to 36.3.
- The biggest single-benchmark swing is SimpleBench: 40% for DeepSeek-V3.1 and 47.7% for GLM-4.7.
- DeepSeek-V3.1 is cheaper at $0.25 / $0.95 per million input/output tokens, against $0.60 / $2.20 for GLM-4.7.
- GLM-4.7 accepts more context: 205K tokens versus 164K.

## Snapshot

| | DeepSeek-V3.1 | GLM-4.7 |
|---|---|---|
| Provider | DeepSeek | Z.ai (Zhipu) |
| Noometry Index | 42.8 | 42.0 |
| Rank | 108 | 124 |
| Context | 164K | 205K |
| Input $/M | $0.25 | $0.60 |
| Output $/M | $0.95 | $2.20 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.1: 40.3 (#144)
- GLM-4.7: 44.0 (#79)

| Benchmark | DeepSeek-V3.1 | GLM-4.7 |
|---|---|---|
| LMArena Coding | 1417 | 1454 |
| LMArena WebDev | — | 1435 |
| SciCode | — | 45.1% |
| WeirdML | 38.4% | — |
| ALE-Bench | — | 399.48 |

## Agentic & Tool Use

- DeepSeek-V3.1: —
- GLM-4.7: 26.5 (#103)

| Benchmark | DeepSeek-V3.1 | GLM-4.7 |
|---|---|---|
| Terminal-Bench | — | 33.4% |
| Vending-Bench 2 | — | 2,377 |

## Reasoning

- DeepSeek-V3.1: 27.9 (#110)
- GLM-4.7: 24.3 (#164)

| Benchmark | DeepSeek-V3.1 | GLM-4.7 |
|---|---|---|
| SimpleBench | 40% | 47.7% |
| LMArena Hard Prompts | 1417 | 1443 |
| Epoch Capabilities Index | 139.92 | 143.51 |
| Kagi LLM Benchmark | 53.2% | — |
| CritPt | — | 1.7% |
| Chess Puzzles | — | 6% |
| DTBench | 82.7% | — |
| LMCA | 24.3% | — |
| ForecastBench | 58 | — |

## Math

- DeepSeek-V3.1: 38.9 (#122)
- GLM-4.7: 38.6 (#135)

| Benchmark | DeepSeek-V3.1 | GLM-4.7 |
|---|---|---|
| LMArena Math | 1420 | 1423 |
| OTIS Mock AIME 2024-2025 | — | 83.3% |
| ProofBench | — | 6% |
| FrontierMath (Feb 2025 set) | — | 2.4% |
| FrontierMath Tier 4 (v1) | — | 0% |

## Knowledge

- DeepSeek-V3.1: 43.7 (#90)
- GLM-4.7: 47.0 (#80)

| Benchmark | DeepSeek-V3.1 | GLM-4.7 |
|---|---|---|
| Vectara Hallucination Rate | 5.5% | 11.7% |
| LMArena Expert | 1405 | 1424 |
| GPQA Diamond | — | 83.3% |
| SimpleQA Verified | — | 32.2% |

## Multilingual

- DeepSeek-V3.1: 51.6 (#106)
- GLM-4.7: 52.8 (#79)

| Benchmark | DeepSeek-V3.1 | GLM-4.7 |
|---|---|---|
| LMArena Non-English | 1400 | 1417 |
| LMArena Chinese | 1469 | 1495 |
| LMArena French | 1447 | 1432 |
| LMArena German | 1411 | 1424 |
| LMArena Japanese | 1378 | 1439 |
| LMArena Korean | 1337 | 1399 |
| LMArena Russian | 1405 | 1423 |
| LMArena Spanish | 1431 | 1434 |

## Instruction Following

- DeepSeek-V3.1: 73.9 (#110)
- GLM-4.7: 74.4 (#95)

| Benchmark | DeepSeek-V3.1 | GLM-4.7 |
|---|---|---|
| LMArena Instruction Following | 1400 | 1411 |

## Long Context

- DeepSeek-V3.1: 36.3 (#232)
- GLM-4.7: 42.8 (#116)

| Benchmark | DeepSeek-V3.1 | GLM-4.7 |
|---|---|---|
| LMArena Longer Query | 1422 | 1432 |
| Fiction.LiveBench | 52.8% | — |
| CL-bench | — | 15.9% |
| CL-bench Life | — | 10.9% |

## Writing & Preference

- DeepSeek-V3.1: 60.3 (#98)
- GLM-4.7: 60.9 (#93)

| Benchmark | DeepSeek-V3.1 | GLM-4.7 |
|---|---|---|
| LMArena Text | 1420 | 1435 |
| LMArena Creative Writing | 1401 | 1401 |
| EQ-Bench Creative Writing | 1436 | 1413 |
| LMArena Multi-Turn | 1408 | 1446 |

## FAQ

### Is DeepSeek-V3.1 better than GLM-4.7?

DeepSeek-V3.1 and GLM-4.7 score almost the same on the Noometry Index (42.8 vs 42.0), so choose on price, context window or the category you care about most.

### Which is cheaper, DeepSeek-V3.1 or GLM-4.7?

DeepSeek-V3.1 is cheaper. It lists at $0.25 per million input tokens and $0.95 per million output tokens; GLM-4.7 lists at $0.60 and $2.20.

### Is DeepSeek-V3.1 or GLM-4.7 better for coding?

GLM-4.7 scores higher on coding benchmarks: 44.0 versus 40.3 in the Noometry coding category.

### Which has the bigger context window?

GLM-4.7 does, with 205K tokens against 164K.

### How many benchmarks do DeepSeek-V3.1 and GLM-4.7 share?

21 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and GLM-4.7 has 36.