Model comparison
GLM-4.7 vs GPT-5.3 Codex
GPT-5.3 Codex is the stronger model overall, scoring 45.8 to 42.0 on the Noometry Index. GLM-4.7 costs 4.8× less per token, which makes it the better buy when GPT-5.3 Codex's lead doesn't matter for your workload.
Last verified . 5 shared benchmarks.
Summary
- They share 5 benchmarks with published results for both. GLM-4.7 scores higher in 0 categories and GPT-5.3 Codex in 2 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where GPT-5.3 Codex leads 48.0 to 26.5.
- The biggest single-benchmark swing is Terminal-Bench: 33.4% for GLM-4.7 and 78.4% for GPT-5.3 Codex.
- GLM-4.7 is cheaper at $0.60 / $2.20 per million input/output tokens, against $1.75 / $14 for GPT-5.3 Codex.
- GPT-5.3 Codex accepts more context: 400K tokens versus 205K.
- GLM-4.7 has downloadable open weights; the other is API-only.
Side by side
| GLM-4.7 | GPT-5.3 Codex | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 42.0 | 45.8 |
| Released | 2025-12-22 | 2026-02-05 |
| Weights | Open | Proprietary |
| Context window | 205K | 400K |
| Max output | 131K | 128K |
| Input $ / M tokens | $0.60 | $1.75 |
| Output $ / M tokens | $2.20 | $14 |
| Results tracked | 36 | 8 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GPT-5.3 Codex leads
GLM-4.7: 44.0 (#79), GPT-5.3 Codex: 48.6 (#56)
| Benchmark | GLM-4.7 | GPT-5.3 Codex |
|---|---|---|
| LMArena WebDev | 1435 | 1409 |
| ALE-Bench | 399.48 | 1,655 |
| SWE-bench Verified | — | 74.8% |
| SciCode | 45.1% | — |
| WeirdML | — | 79.3% |
| LMArena Coding | 1454 | — |
Agentic & Tool Use GPT-5.3 Codex leads
GLM-4.7: 26.5 (#103), GPT-5.3 Codex: 48.0 (#9)
| Benchmark | GLM-4.7 | GPT-5.3 Codex |
|---|---|---|
| Terminal-Bench | 33.4% | 78.4% |
| Vending-Bench 2 | 2,377 | 5,940 |
| METR Time Horizons | — | 74.5% |
Reasoning Not comparable
GLM-4.7: 24.3 (#164), GPT-5.3 Codex: —
| Benchmark | GLM-4.7 | GPT-5.3 Codex |
|---|---|---|
| Epoch Capabilities Index | 143.51 | 156.77 |
| SimpleBench | 47.7% | — |
| CritPt | 1.7% | — |
| Chess Puzzles | 6% | — |
| LMArena Hard Prompts | 1443 | — |
Math Not comparable
GLM-4.7: 38.6 (#135), GPT-5.3 Codex: —
| Benchmark | GLM-4.7 | GPT-5.3 Codex |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 83.3% | — |
| ProofBench | 6% | — |
| LMArena Math | 1423 | — |
| FrontierMath (Feb 2025 set) | 2.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
Knowledge Not comparable
GLM-4.7: 47.0 (#80), GPT-5.3 Codex: —
| Benchmark | GLM-4.7 | GPT-5.3 Codex |
|---|---|---|
| GPQA Diamond | 83.3% | — |
| SimpleQA Verified | 32.2% | — |
| Vectara Hallucination Rate | 11.7% | — |
| LMArena Expert | 1424 | — |
Multilingual Not comparable
GLM-4.7: 52.8 (#79), GPT-5.3 Codex: —
| Benchmark | GLM-4.7 | GPT-5.3 Codex |
|---|---|---|
| LMArena Non-English | 1417 | — |
| LMArena Chinese | 1495 | — |
| LMArena French | 1432 | — |
| LMArena German | 1424 | — |
| LMArena Japanese | 1439 | — |
| LMArena Korean | 1399 | — |
| LMArena Russian | 1423 | — |
| LMArena Spanish | 1434 | — |
Instruction Following Not comparable
GLM-4.7: 74.4 (#95), GPT-5.3 Codex: —
| Benchmark | GLM-4.7 | GPT-5.3 Codex |
|---|---|---|
| LMArena Instruction Following | 1411 | — |
Long Context Not comparable
GLM-4.7: 42.8 (#116), GPT-5.3 Codex: —
| Benchmark | GLM-4.7 | GPT-5.3 Codex |
|---|---|---|
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |
| LMArena Longer Query | 1432 | — |
Writing & Preference Not comparable
GLM-4.7: 60.9 (#93), GPT-5.3 Codex: —
| Benchmark | GLM-4.7 | GPT-5.3 Codex |
|---|---|---|
| LMArena Text | 1435 | — |
| LMArena Creative Writing | 1401 | — |
| EQ-Bench Creative Writing | 1413 | — |
| LMArena Multi-Turn | 1446 | — |
Frequently asked questions
Is GLM-4.7 better than GPT-5.3 Codex?
GPT-5.3 Codex is the stronger model overall, scoring 45.8 to 42.0 on the Noometry Index. GLM-4.7 costs 4.8× less per token, which makes it the better buy when GPT-5.3 Codex's lead doesn't matter for your workload.
Which is cheaper, GLM-4.7 or GPT-5.3 Codex?
GLM-4.7 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; GPT-5.3 Codex lists at $1.75 and $14.
Is GLM-4.7 or GPT-5.3 Codex better for coding?
GPT-5.3 Codex scores higher on coding benchmarks: 48.6 versus 44.0 in the Noometry coding category.
Which has the bigger context window?
GPT-5.3 Codex does, with 400K tokens against 205K.
How many benchmarks do GLM-4.7 and GPT-5.3 Codex share?
5 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and GPT-5.3 Codex has 8.