Model comparison
GLM-5.3 vs GPT-5.4 nano
GLM-5.3 is the stronger model overall, scoring 54.8 to 41.9 on the Noometry Index. GPT-5.4 nano costs 4.6× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.
Last verified . 32 shared benchmarks.
Summary
- They share 32 benchmarks with published results for both. GLM-5.3 scores higher in 8 categories and GPT-5.4 nano in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.3 leads 46.1 to 23.7.
- The biggest single-benchmark swing is ProofBench: 49% for GLM-5.3 and 5% for GPT-5.4 nano.
- GPT-5.4 nano is cheaper at $0.20 / $1.25 per million input/output tokens, against $1.40 / $4.40 for GLM-5.3.
- 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-5.4 nano | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 54.8 | 41.9 |
| Released | 2026-08-14 | 2026-03-17 |
| Weights | Open | Proprietary |
| Context window | 1M | 400K |
| Max output | 131K | 128K |
| Input $ / M tokens | $1.40 | $0.20 |
| Output $ / M tokens | $4.40 | $1.25 |
| Results tracked | 42 | 40 |
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Category by category
Coding GLM-5.3 leads
GLM-5.3: 59.5 (#14), GPT-5.4 nano: 43.6 (#84)
| Benchmark | GLM-5.3 | GPT-5.4 nano |
|---|---|---|
| SciCode | 59% | 46.9% |
| WeirdML | 75.4% | 49.2% |
| LMArena Coding | 1496 | 1405 |
| ALE-Bench | 1,317 | 1,005 |
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| CursorBench | 42.6% | — |
| LMArena WebDev | 1622 | — |
| FrontierSWE | 30.2% | — |
Agentic & Tool Use Not comparable
GLM-5.3: 36.4 (#38), GPT-5.4 nano: —
| Benchmark | GLM-5.3 | GPT-5.4 nano |
|---|---|---|
| APEX-Agents | 56.6% | — |
| Vending-Bench 2 | 8,164 | — |
Reasoning GLM-5.3 leads
GLM-5.3: 46.1 (#46), GPT-5.4 nano: 23.7 (#173)
| Benchmark | GLM-5.3 | GPT-5.4 nano |
|---|---|---|
| CritPt | 19.1% | 9.3% |
| Chess Puzzles | 21% | 30% |
| LMArena Hard Prompts | 1489 | 1381 |
| Mystery Game Puzzles | 33% | 9% |
| DTBench | 87.7% | 80.3% |
| LMCA | 55.5% | 36.9% |
| Epoch Capabilities Index | 155.61 | 145.81 |
| ARC-AGI-2 | — | 5.7% |
| Kagi LLM Benchmark | — | 39.7% |
| NYT Connections (extended) | 74.2% | — |
| ARC-AGI-1 | — | 51.5% |
| Bench to the Future 3 | 0.15 | — |
| ForecastBench | — | 57.3 |
Math GLM-5.3 leads
GLM-5.3: 62.3 (#33), GPT-5.4 nano: 40.9 (#88)
| Benchmark | GLM-5.3 | GPT-5.4 nano |
|---|---|---|
| FrontierMath (Tiers 1-3) | 68.8% | 44.9% |
| FrontierMath Tier 4 | 29.3% | 12.2% |
| OTIS Mock AIME 2024-2025 | 91.1% | 87.8% |
| ProofBench | 49% | 5% |
| LMArena Math | 1489 | 1406 |
| FrontierMath (Feb 2025 set) | — | 25.9% |
| FrontierMath Tier 4 (v1) | — | 6.3% |
Knowledge GLM-5.3 leads
GLM-5.3: 58.3 (#37), GPT-5.4 nano: 41.9 (#103)
| Benchmark | GLM-5.3 | GPT-5.4 nano |
|---|---|---|
| GPQA Diamond | 90.9% | 78.5% |
| SimpleQA Verified | 41% | 11.7% |
| LMArena Expert | 1516 | 1396 |
| Vectara Hallucination Rate | — | 3.1% |
Multimodal Not comparable
GLM-5.3: —, GPT-5.4 nano: 36.7 (#78)
| Benchmark | GLM-5.3 | GPT-5.4 nano |
|---|---|---|
| LMArena Vision | — | 1196 |
Multilingual GLM-5.3 leads
GLM-5.3: 55.7 (#28), GPT-5.4 nano: 48.6 (#140)
| Benchmark | GLM-5.3 | GPT-5.4 nano |
|---|---|---|
| LMArena Non-English | 1457 | 1359 |
| LMArena Chinese | 1528 | 1392 |
| LMArena French | 1499 | 1396 |
| LMArena German | 1499 | 1367 |
| LMArena Japanese | 1453 | 1343 |
| LMArena Korean | 1472 | 1320 |
| LMArena Russian | 1463 | 1363 |
| LMArena Spanish | 1460 | 1371 |
Instruction Following GLM-5.3 leads
GLM-5.3: 77.5 (#23), GPT-5.4 nano: 71.9 (#144)
| Benchmark | GLM-5.3 | GPT-5.4 nano |
|---|---|---|
| LMArena Instruction Following | 1477 | 1362 |
Long Context GLM-5.3 leads
GLM-5.3: 45.4 (#41), GPT-5.4 nano: 41.6 (#137)
| Benchmark | GLM-5.3 | GPT-5.4 nano |
|---|---|---|
| LMArena Longer Query | 1482 | 1366 |
Writing & Preference GLM-5.3 leads
GLM-5.3: 75.7 (#6), GPT-5.4 nano: 55.7 (#142)
| Benchmark | GLM-5.3 | GPT-5.4 nano |
|---|---|---|
| LMArena Text | 1471 | 1372 |
| LMArena Creative Writing | 1457 | 1314 |
| LMArena Multi-Turn | 1472 | 1382 |
| EQ-Bench Creative Writing | 2075 | — |
Frequently asked questions
Is GLM-5.3 better than GPT-5.4 nano?
GLM-5.3 is the stronger model overall, scoring 54.8 to 41.9 on the Noometry Index. GPT-5.4 nano costs 4.6× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.
Which is cheaper, GLM-5.3 or GPT-5.4 nano?
GPT-5.4 nano is cheaper. It lists at $0.20 per million input tokens and $1.25 per million output tokens; GLM-5.3 lists at $1.40 and $4.40.
Is GLM-5.3 or GPT-5.4 nano better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 43.6 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-5.4 nano share?
32 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and GPT-5.4 nano has 40.