Model comparison
GLM-5 vs GPT-5.2 Codex
GLM-5 is the stronger model overall, scoring 46.1 to 42.6 on the Noometry Index.
Last verified . 5 shared benchmarks.
Summary
- They share 5 benchmarks with published results for both. GLM-5 scores higher in 1 category and GPT-5.2 Codex in 1 category; 2 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where GPT-5.2 Codex leads 41.0 to 31.1.
- The biggest single-benchmark swing is Terminal-Bench: 52.4% for GLM-5 and 66.5% for GPT-5.2 Codex.
- GLM-5 is cheaper at $1 / $3.20 per million input/output tokens, against $1.75 / $14 for GPT-5.2 Codex.
- GPT-5.2 Codex accepts more context: 400K tokens versus 205K.
- GLM-5 has downloadable open weights; the other is API-only.
Side by side
| GLM-5 | GPT-5.2 Codex | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 46.1 | 42.6 |
| Released | 2026-02-11 | 2025-12-18 |
| Weights | Open | Proprietary |
| Context window | 205K | 400K |
| Max output | 131K | 128K |
| Input $ / M tokens | $1 | $1.75 |
| Output $ / M tokens | $3.20 | $14 |
| Results tracked | 45 | 5 |
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Category by category
Coding GLM-5 leads
GLM-5: 49.0 (#52), GPT-5.2 Codex: 45.5 (#71)
| Benchmark | GLM-5 | GPT-5.2 Codex |
|---|---|---|
| SWE-bench Verified (bash only) | 72.8% | 72.8% |
| LMArena WebDev | 1434 | 1339 |
| SWE-bench Multilingual | 69.7% | 66.3% |
| ALE-Bench | 765.62 | 1,300 |
| SWE-bench Verified | 72.1% | — |
| WeirdML | 48.2% | — |
| LMArena Coding | 1461 | — |
Agentic & Tool Use GPT-5.2 Codex leads
GLM-5: 31.1 (#71), GPT-5.2 Codex: 41.0 (#22)
| Benchmark | GLM-5 | GPT-5.2 Codex |
|---|---|---|
| Terminal-Bench | 52.4% | 66.5% |
| 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 Not comparable
GLM-5: 27.6 (#116), GPT-5.2 Codex: —
| Benchmark | GLM-5 | GPT-5.2 Codex |
|---|---|---|
| ARC-AGI-2 | 4.9% | — |
| SimpleBench | 53.2% | — |
| Kagi LLM Benchmark | 75% | — |
| NYT Connections (extended) | 74.8% | — |
| ARC-AGI-1 | 44.7% | — |
| Chess Puzzles | 10% | — |
| LMArena Hard Prompts | 1452 | — |
| Epoch Capabilities Index | 145.83 | — |
| ForecastBench | 61 | — |
Math Not comparable
GLM-5: 46.4 (#71), GPT-5.2 Codex: —
| Benchmark | GLM-5 | GPT-5.2 Codex |
|---|---|---|
| MathArena Final-Answer Competitions | 65.7% | — |
| OTIS Mock AIME 2024-2025 | 80% | — |
| LMArena Math | 1440 | — |
| FrontierMath (Feb 2025 set) | 16.4% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Not comparable
GLM-5: 52.3 (#64), GPT-5.2 Codex: —
| Benchmark | GLM-5 | GPT-5.2 Codex |
|---|---|---|
| GPQA Diamond | 87.8% | — |
| Vectara Hallucination Rate | 10.1% | — |
| LMArena Expert | 1454 | — |
Multilingual Not comparable
GLM-5: 53.7 (#58), GPT-5.2 Codex: —
| Benchmark | GLM-5 | GPT-5.2 Codex |
|---|---|---|
| LMArena Non-English | 1430 | — |
| LMArena Chinese | 1511 | — |
| LMArena French | 1455 | — |
| LMArena German | 1445 | — |
| LMArena Japanese | 1416 | — |
| LMArena Korean | 1423 | — |
| LMArena Russian | 1436 | — |
| LMArena Spanish | 1454 | — |
Instruction Following Not comparable
GLM-5: 75.2 (#67), GPT-5.2 Codex: —
| Benchmark | GLM-5 | GPT-5.2 Codex |
|---|---|---|
| LMArena Instruction Following | 1428 | — |
Long Context Not comparable
GLM-5: 44.7 (#60), GPT-5.2 Codex: —
| Benchmark | GLM-5 | GPT-5.2 Codex |
|---|---|---|
| CL-bench | 18.7% | — |
| LMArena Longer Query | 1446 | — |
Writing & Preference Not comparable
GLM-5: 66.0 (#38), GPT-5.2 Codex: —
| Benchmark | GLM-5 | GPT-5.2 Codex |
|---|---|---|
| LMArena Text | 1446 | — |
| LMArena Creative Writing | 1439 | — |
| EQ-Bench Creative Writing | 1601 | — |
| LMArena Multi-Turn | 1456 | — |
Frequently asked questions
Is GLM-5 better than GPT-5.2 Codex?
GLM-5 is the stronger model overall, scoring 46.1 to 42.6 on the Noometry Index.
Which is cheaper, GLM-5 or GPT-5.2 Codex?
GLM-5 is cheaper. It lists at $1 per million input tokens and $3.20 per million output tokens; GPT-5.2 Codex lists at $1.75 and $14.
Is GLM-5 or GPT-5.2 Codex better for coding?
GLM-5 scores higher on coding benchmarks: 49.0 versus 45.5 in the Noometry coding category.
Which has the bigger context window?
GPT-5.2 Codex does, with 400K tokens against 205K.
How many benchmarks do GLM-5 and GPT-5.2 Codex share?
5 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and GPT-5.2 Codex has 5.