Model comparison
GLM-5.3-Flash vs GPT-5.4
GPT-5.4 is the stronger model overall, scoring 59.4 to 51.8 on the Noometry Index. GLM-5.3-Flash costs 24× less per token, which makes it the better buy when GPT-5.4's lead doesn't matter for your workload.
Last verified . 34 shared benchmarks.
Summary
- They share 34 benchmarks with published results for both. GLM-5.3-Flash scores higher in 2 categories and GPT-5.4 in 8 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.4 leads 73.5 to 53.3.
- The biggest single-benchmark swing is ProofBench: 21% for GLM-5.3-Flash and 56% for GPT-5.4.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $2.50 / $15 for GPT-5.4.
- GPT-5.4 accepts more context: 1.05M tokens versus 1M.
- GLM-5.3-Flash has downloadable open weights; the other is API-only.
Side by side
| GLM-5.3-Flash | GPT-5.4 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 51.8 | 59.4 |
| Released | 2026-08-20 | 2026-03-05 |
| Weights | Open | Proprietary |
| Context window | 1M | 1.05M |
| Max output | 131K | 128K |
| Input $ / M tokens | $0.15 | $2.50 |
| Output $ / M tokens | $0.50 | $15 |
| Results tracked | 40 | 68 |
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Category by category
Coding Too close to call
GLM-5.3-Flash: 53.1 (#31), GPT-5.4: 52.6 (#33)
| Benchmark | GLM-5.3-Flash | GPT-5.4 |
|---|---|---|
| DeepSWE | 63.4% | 51.8% |
| LMArena WebDev | 1609 | 1465 |
| SciCode | 51.6% | 56.6% |
| LMArena Coding | 1508 | 1497 |
| ALE-Bench | 303.55 | 1,607 |
| SWE-bench Verified | — | 76.9% |
| FrontierCode | 31.8% | — |
| CursorBench | 36.8% | — |
| FrontierSWE | 18.1% | — |
| GSO | — | 31.4% |
| WeirdML | — | 77.7% |
| MirrorCode | — | 15.6% |
| AlgoTune | — | 1.85 |
Agentic & Tool Use GPT-5.4 leads
GLM-5.3-Flash: 34.2 (#47), GPT-5.4: 46.5 (#13)
| Benchmark | GLM-5.3-Flash | GPT-5.4 |
|---|---|---|
| APEX-Agents | 52.8% | 52.4% |
| Terminal-Bench | — | 81.8% |
| τ²-bench Banking | — | 39.4% |
| DeepResearch Bench | — | 35.1% |
| PostTrainBench | — | 19% |
| GBAEval | — | 45.1% |
| GDP.pdf | 14% | — |
| LMArena Search | — | 1197 |
| METR Time Horizons | — | 74.3% |
| Vending-Bench 2 | — | 6,144 |
Reasoning GPT-5.4 leads
GLM-5.3-Flash: 48.0 (#42), GPT-5.4: 61.8 (#19)
| Benchmark | GLM-5.3-Flash | GPT-5.4 |
|---|---|---|
| ARC-AGI-2 | 65.8% | 74% |
| ARC-AGI-1 | 91% | 93.7% |
| CritPt | 15.4% | 23.4% |
| Chess Puzzles | 14% | 44% |
| LMArena Hard Prompts | 1491 | 1485 |
| Mystery Game Puzzles | 8% | 37% |
| Epoch Capabilities Index | 151.88 | 156.81 |
| Kagi LLM Benchmark | — | 63.8% |
| NYT Connections (extended) | — | 91.3% |
| EnigmaEval | — | 16% |
| Thematic Generalization | — | 80% |
| EBR-Bench | — | 25.4% |
| DTBench | — | 94.4% |
| LMCA | — | 52% |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
| ForecastBench | — | 59.5 |
Math GPT-5.4 leads
GLM-5.3-Flash: 53.3 (#47), GPT-5.4: 73.5 (#19)
| Benchmark | GLM-5.3-Flash | GPT-5.4 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | 78.6% |
| FrontierMath Tier 4 | 17.1% | 49% |
| OTIS Mock AIME 2024-2025 | 93.9% | 97.8% |
| ProofBench | 21% | 56% |
| LMArena Math | 1500 | 1488 |
| MathArena Final-Answer Competitions | — | 83.1% |
| FrontierMath (Feb 2025 set) | — | 47.6% |
| FrontierMath Tier 4 (v1) | — | 27.1% |
Knowledge GPT-5.4 leads
GLM-5.3-Flash: 58.4 (#36), GPT-5.4: 65.3 (#14)
| Benchmark | GLM-5.3-Flash | GPT-5.4 |
|---|---|---|
| GPQA Diamond | 90.2% | 93.3% |
| LMArena Expert | 1513 | 1507 |
| Humanity's Last Exam | — | 36.2% |
| SimpleQA Verified | — | 45.1% |
| Vectara Hallucination Rate | — | 7% |
Multimodal Too close to call
GLM-5.3-Flash: 42.8 (#27), GPT-5.4: 43.7 (#20)
| Benchmark | GLM-5.3-Flash | GPT-5.4 |
|---|---|---|
| LMArena Vision | 1296 | 1303 |
| Blueprint-Bench 2 | — | 27.1% |
| Furniture Assembly | — | 37.5% |
| LMArena Document | — | 1471 |
Multilingual Too close to call
GLM-5.3-Flash: 56.0 (#25), GPT-5.4: 56.2 (#23)
| Benchmark | GLM-5.3-Flash | GPT-5.4 |
|---|---|---|
| LMArena Non-English | 1462 | 1465 |
| LMArena Chinese | 1527 | 1519 |
| LMArena French | 1496 | 1493 |
| LMArena German | 1470 | 1472 |
| LMArena Japanese | 1429 | 1485 |
| LMArena Korean | 1446 | 1448 |
| LMArena Russian | 1469 | 1480 |
| LMArena Spanish | 1471 | 1454 |
Instruction Following Too close to call
GLM-5.3-Flash: 77.5 (#20), GPT-5.4: 77.1 (#27)
| Benchmark | GLM-5.3-Flash | GPT-5.4 |
|---|---|---|
| LMArena Instruction Following | 1478 | 1469 |
Long Context GPT-5.4 leads
GLM-5.3-Flash: 45.4 (#39), GPT-5.4: 50.3 (#8)
| Benchmark | GLM-5.3-Flash | GPT-5.4 |
|---|---|---|
| LMArena Longer Query | 1482 | 1473 |
| CL-bench | — | 27.9% |
| CL-bench Life | — | 21.7% |
Writing & Preference GPT-5.4 leads
GLM-5.3-Flash: 65.3 (#50), GPT-5.4: 71.9 (#17)
| Benchmark | GLM-5.3-Flash | GPT-5.4 |
|---|---|---|
| LMArena Text | 1471 | 1469 |
| LMArena Creative Writing | 1442 | 1439 |
| LMArena Multi-Turn | 1467 | 1482 |
| EQ-Bench Creative Writing | — | 1840 |
| EQ-Bench 4 | — | 1272 |
Frequently asked questions
Is GLM-5.3-Flash better than GPT-5.4?
GPT-5.4 is the stronger model overall, scoring 59.4 to 51.8 on the Noometry Index. GLM-5.3-Flash costs 24× less per token, which makes it the better buy when GPT-5.4's lead doesn't matter for your workload.
Which is cheaper, GLM-5.3-Flash or GPT-5.4?
GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; GPT-5.4 lists at $2.50 and $15.
Is GLM-5.3-Flash or GPT-5.4 better for coding?
They score almost the same on coding (53.1 vs 52.6); test both on your own repository before choosing.
Which has the bigger context window?
GPT-5.4 does, with 1.05M tokens against 1M.
How many benchmarks do GLM-5.3-Flash and GPT-5.4 share?
34 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and GPT-5.4 has 68.