# GLM-5.1 vs GPT-5-Codex

> GLM-5.1 is the stronger model overall, scoring 47.8 to 37.9 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-5-1-vs-gpt-5-codex
- Last updated: 2026-10-10
- Shared benchmarks: 1

## Summary

- They share 1 benchmark with published results for both. GLM-5.1 scores higher in 2 categories and GPT-5-Codex in 1 category; 3 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.1 leads 39.1 to 30.9.
- GLM-5.1 is cheaper at $1.40 / $4.40 per million input/output tokens, against $1.25 / $10 for GPT-5-Codex.
- GPT-5-Codex accepts more context: 400K tokens versus 200K.
- GLM-5.1 has downloadable open weights; the other is API-only.

## Snapshot

| | GLM-5.1 | GPT-5-Codex |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 47.8 | 37.9 |
| Rank | 59 | 192 |
| Context | 200K | 400K |
| Input $/M | $1.40 | $1.25 |
| Output $/M | $4.40 | $10 |
| Weights | Open | Proprietary |

## Coding

- GLM-5.1: 48.7 (#55)
- GPT-5-Codex: 42.4 (#103)

| Benchmark | GLM-5.1 | GPT-5-Codex |
|---|---|---|
| WeirdML | 57.1% | 54.5% |
| SWE-bench Verified | 74.2% | — |
| LMArena WebDev | 1508 | — |
| SciCode | 43.8% | — |
| LMArena Coding | 1485 | — |
| ALE-Bench | 887.1 | — |

## Agentic & Tool Use

- GLM-5.1: 24.9 (#113)
- GPT-5-Codex: 31.0 (#72)

| Benchmark | GLM-5.1 | GPT-5-Codex |
|---|---|---|
| Terminal-Bench | — | 44.3% |
| APEX-Agents | 40.9% | — |
| ExploitBench | 18.1% | — |
| GBAEval | 0% | — |
| Vending-Bench 2 | 5,634 | — |

## Reasoning

- GLM-5.1: 39.1 (#60)
- GPT-5-Codex: 30.9 (#83)

| Benchmark | GLM-5.1 | GPT-5-Codex |
|---|---|---|
| SimpleBench | 55.1% | — |
| Kagi LLM Benchmark | — | 70.3% |
| NYT Connections (extended) | 77.7% | — |
| CritPt | 4.6% | — |
| Chess Puzzles | 19% | — |
| Thematic Generalization | 69.8% | — |
| LMArena Hard Prompts | 1472 | — |
| Epoch Capabilities Index | 149.84 | — |

## Math

- GLM-5.1: 49.7 (#60)
- GPT-5-Codex: —

| Benchmark | GLM-5.1 | GPT-5-Codex |
|---|---|---|
| FrontierMath (Tiers 1-3) | 36.8% | — |
| MathArena Final-Answer Competitions | 67.1% | — |
| OTIS Mock AIME 2024-2025 | 93.3% | — |
| ProofBench | 22.2% | — |
| LMArena Math | 1473 | — |
| FrontierMath (Feb 2025 set) | 33.4% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |

## Knowledge

- GLM-5.1: 54.9 (#50)
- GPT-5-Codex: —

| Benchmark | GLM-5.1 | GPT-5-Codex |
|---|---|---|
| GPQA Diamond | 89.9% | — |
| SimpleQA Verified | 34% | — |
| LMArena Expert | 1476 | — |

## Multilingual

- GLM-5.1: 55.0 (#36)
- GPT-5-Codex: —

| Benchmark | GLM-5.1 | GPT-5-Codex |
|---|---|---|
| LMArena Non-English | 1447 | — |
| LMArena Chinese | 1515 | — |
| LMArena French | 1474 | — |
| LMArena German | 1465 | — |
| LMArena Japanese | 1434 | — |
| LMArena Korean | 1418 | — |
| LMArena Russian | 1454 | — |
| LMArena Spanish | 1469 | — |

## Instruction Following

- GLM-5.1: 76.3 (#42)
- GPT-5-Codex: —

| Benchmark | GLM-5.1 | GPT-5-Codex |
|---|---|---|
| LMArena Instruction Following | 1451 | — |

## Long Context

- GLM-5.1: 44.9 (#53)
- GPT-5-Codex: —

| Benchmark | GLM-5.1 | GPT-5-Codex |
|---|---|---|
| LMArena Longer Query | 1466 | — |

## Writing & Preference

- GLM-5.1: 66.9 (#31)
- GPT-5-Codex: —

| Benchmark | GLM-5.1 | GPT-5-Codex |
|---|---|---|
| LMArena Text | 1461 | — |
| LMArena Creative Writing | 1453 | — |
| EQ-Bench Creative Writing | 1592 | — |
| LMArena Multi-Turn | 1472 | — |

## FAQ

### Is GLM-5.1 better than GPT-5-Codex?

GLM-5.1 is the stronger model overall, scoring 47.8 to 37.9 on the Noometry Index.

### Which is cheaper, GLM-5.1 or GPT-5-Codex?

GLM-5.1 is cheaper. It lists at $1.40 per million input tokens and $4.40 per million output tokens; GPT-5-Codex lists at $1.25 and $10.

### Is GLM-5.1 or GPT-5-Codex better for coding?

GLM-5.1 scores higher on coding benchmarks: 48.7 versus 42.4 in the Noometry coding category.

### Which has the bigger context window?

GPT-5-Codex does, with 400K tokens against 200K.

### How many benchmarks do GLM-5.1 and GPT-5-Codex share?

1 benchmark has published results for both models. GLM-5.1 has 41 scored results on Noometry and GPT-5-Codex has 3.