Model comparison
GLM-5.3 vs GPT-5.3 Codex
GLM-5.3 is the stronger model overall, scoring 54.8 to 45.8 on the Noometry Index.
Last verified . 5 shared benchmarks.
Summary
- They share 5 benchmarks with published results for both. GLM-5.3 scores higher in 1 category and GPT-5.3 Codex in 1 category; 2 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where GPT-5.3 Codex leads 48.0 to 36.4.
- GLM-5.3 is cheaper at $1.40 / $4.40 per million input/output tokens, against $1.75 / $14 for GPT-5.3 Codex.
- GLM-5.3 accepts more context: 1M tokens versus 400K.
- GLM-5.3 has downloadable open weights; the other is API-only.
Side by side
| GLM-5.3 | GPT-5.3 Codex | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 54.8 | 45.8 |
| Released | 2026-08-14 | 2026-02-05 |
| Weights | Open | Proprietary |
| Context window | 1M | 400K |
| Max output | 131K | 128K |
| Input $ / M tokens | $1.40 | $1.75 |
| Output $ / M tokens | $4.40 | $14 |
| Results tracked | 42 | 8 |
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Category by category
Coding GLM-5.3 leads
GLM-5.3: 59.5 (#14), GPT-5.3 Codex: 48.6 (#56)
| Benchmark | GLM-5.3 | GPT-5.3 Codex |
|---|---|---|
| LMArena WebDev | 1622 | 1409 |
| WeirdML | 75.4% | 79.3% |
| ALE-Bench | 1,317 | 1,655 |
| SWE-bench Verified | — | 74.8% |
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| CursorBench | 42.6% | — |
| FrontierSWE | 30.2% | — |
| SciCode | 59% | — |
| LMArena Coding | 1496 | — |
Agentic & Tool Use GPT-5.3 Codex leads
GLM-5.3: 36.4 (#38), GPT-5.3 Codex: 48.0 (#9)
| Benchmark | GLM-5.3 | GPT-5.3 Codex |
|---|---|---|
| Vending-Bench 2 | 8,164 | 5,940 |
| Terminal-Bench | — | 78.4% |
| APEX-Agents | 56.6% | — |
| METR Time Horizons | — | 74.5% |
Reasoning Not comparable
GLM-5.3: 46.1 (#46), GPT-5.3 Codex: —
| Benchmark | GLM-5.3 | GPT-5.3 Codex |
|---|---|---|
| Epoch Capabilities Index | 155.61 | 156.77 |
| NYT Connections (extended) | 74.2% | — |
| CritPt | 19.1% | — |
| Chess Puzzles | 21% | — |
| LMArena Hard Prompts | 1489 | — |
| Mystery Game Puzzles | 33% | — |
| DTBench | 87.7% | — |
| LMCA | 55.5% | — |
| Bench to the Future 3 | 0.15 | — |
Math Not comparable
GLM-5.3: 62.3 (#33), GPT-5.3 Codex: —
| Benchmark | GLM-5.3 | GPT-5.3 Codex |
|---|---|---|
| FrontierMath (Tiers 1-3) | 68.8% | — |
| FrontierMath Tier 4 | 29.3% | — |
| OTIS Mock AIME 2024-2025 | 91.1% | — |
| ProofBench | 49% | — |
| LMArena Math | 1489 | — |
Knowledge Not comparable
GLM-5.3: 58.3 (#37), GPT-5.3 Codex: —
| Benchmark | GLM-5.3 | GPT-5.3 Codex |
|---|---|---|
| GPQA Diamond | 90.9% | — |
| SimpleQA Verified | 41% | — |
| LMArena Expert | 1516 | — |
Multilingual Not comparable
GLM-5.3: 55.7 (#28), GPT-5.3 Codex: —
| Benchmark | GLM-5.3 | GPT-5.3 Codex |
|---|---|---|
| LMArena Non-English | 1457 | — |
| LMArena Chinese | 1528 | — |
| LMArena French | 1499 | — |
| LMArena German | 1499 | — |
| LMArena Japanese | 1453 | — |
| LMArena Korean | 1472 | — |
| LMArena Russian | 1463 | — |
| LMArena Spanish | 1460 | — |
Instruction Following Not comparable
GLM-5.3: 77.5 (#23), GPT-5.3 Codex: —
| Benchmark | GLM-5.3 | GPT-5.3 Codex |
|---|---|---|
| LMArena Instruction Following | 1477 | — |
Long Context Not comparable
GLM-5.3: 45.4 (#41), GPT-5.3 Codex: —
| Benchmark | GLM-5.3 | GPT-5.3 Codex |
|---|---|---|
| LMArena Longer Query | 1482 | — |
Writing & Preference Not comparable
GLM-5.3: 75.7 (#6), GPT-5.3 Codex: —
| Benchmark | GLM-5.3 | GPT-5.3 Codex |
|---|---|---|
| LMArena Text | 1471 | — |
| LMArena Creative Writing | 1457 | — |
| EQ-Bench Creative Writing | 2075 | — |
| LMArena Multi-Turn | 1472 | — |
Frequently asked questions
Is GLM-5.3 better than GPT-5.3 Codex?
GLM-5.3 is the stronger model overall, scoring 54.8 to 45.8 on the Noometry Index.
Which is cheaper, GLM-5.3 or GPT-5.3 Codex?
GLM-5.3 is cheaper. It lists at $1.40 per million input tokens and $4.40 per million output tokens; GPT-5.3 Codex lists at $1.75 and $14.
Is GLM-5.3 or GPT-5.3 Codex better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 48.6 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3 does, with 1M tokens against 400K.
How many benchmarks do GLM-5.3 and GPT-5.3 Codex share?
5 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and GPT-5.3 Codex has 8.