Model comparison
GLM-5.3-Flash vs GPT-5 Nano
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 33.5 on the Noometry Index. GPT-5 Nano costs 1.7× less per token, which makes it the better buy when GLM-5.3-Flash's lead doesn't matter for your workload.
Last verified . 28 shared benchmarks.
Summary
- They share 28 benchmarks with published results for both. GLM-5.3-Flash scores higher in 10 categories and GPT-5 Nano in 0 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.3-Flash leads 48.0 to 16.3.
- The biggest single-benchmark swing is ARC-AGI-1: 91% for GLM-5.3-Flash and 20.7% for GPT-5 Nano.
- GPT-5 Nano is cheaper at $0.05 / $0.40 per million input/output tokens, against $0.15 / $0.50 for GLM-5.3-Flash.
- GLM-5.3-Flash accepts more context: 1M tokens versus 400K.
- GLM-5.3-Flash has downloadable open weights; the other is API-only.
Side by side
| GLM-5.3-Flash | GPT-5 Nano | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 51.8 | 33.5 |
| Released | 2026-08-20 | 2025-08-07 |
| Weights | Open | Proprietary |
| Context window | 1M | 400K |
| Max output | 131K | 128K |
| Input $ / M tokens | $0.15 | $0.05 |
| Output $ / M tokens | $0.50 | $0.40 |
| Results tracked | 40 | 49 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-5.3-Flash: 53.1 (#31), GPT-5 Nano: 33.6 (#254)
| Benchmark | GLM-5.3-Flash | GPT-5 Nano |
|---|---|---|
| LMArena Coding | 1508 | 1351 |
| ALE-Bench | 303.55 | 718.67 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| SWE-bench Verified (bash only) | — | 34.8% |
| CursorBench | 36.8% | — |
| LMArena WebDev | 1609 | — |
| FrontierSWE | 18.1% | — |
| SciCode | 51.6% | — |
| WeirdML | — | 38.1% |
Agentic & Tool Use GLM-5.3-Flash leads
GLM-5.3-Flash: 34.2 (#47), GPT-5 Nano: 25.8 (#106)
| Benchmark | GLM-5.3-Flash | GPT-5 Nano |
|---|---|---|
| Terminal-Bench | — | 21.8% |
| APEX-Agents | 52.8% | — |
| Berkeley Function Calling Leaderboard | — | 51.5% |
| GDP.pdf | 14% | — |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 48.0 (#42), GPT-5 Nano: 16.3 (#306)
| Benchmark | GLM-5.3-Flash | GPT-5 Nano |
|---|---|---|
| ARC-AGI-2 | 65.8% | 2.6% |
| ARC-AGI-1 | 91% | 20.7% |
| Chess Puzzles | 14% | 27% |
| LMArena Hard Prompts | 1491 | 1328 |
| Mystery Game Puzzles | 8% | 9% |
| Epoch Capabilities Index | 151.88 | 139.38 |
| Kagi LLM Benchmark | — | 62.2% |
| CritPt | 15.4% | — |
| DTBench | — | 62.7% |
| LMCA | — | 7.9% |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
| ForecastBench | — | 59.1 |
Math GLM-5.3-Flash leads
GLM-5.3-Flash: 53.3 (#47), GPT-5 Nano: 29.4 (#241)
| Benchmark | GLM-5.3-Flash | GPT-5 Nano |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | 20% |
| FrontierMath Tier 4 | 17.1% | 2.4% |
| OTIS Mock AIME 2024-2025 | 93.9% | 81.1% |
| ProofBench | 21% | 12% |
| LMArena Math | 1500 | 1317 |
| Omni-MATH | — | 54.6% |
| MATH Level 5 | — | 95.2% |
| FrontierMath (Feb 2025 set) | — | 8.3% |
| FrontierMath Tier 4 (v1) | — | 2.1% |
Knowledge GLM-5.3-Flash leads
GLM-5.3-Flash: 58.4 (#36), GPT-5 Nano: 35.9 (#178)
| Benchmark | GLM-5.3-Flash | GPT-5 Nano |
|---|---|---|
| GPQA Diamond | 90.2% | 69.4% |
| LMArena Expert | 1513 | 1321 |
| SimpleQA Verified | — | 11.7% |
| MMLU-Pro | — | 77.8% |
| Vectara Hallucination Rate | — | 10.5% |
| GPQA (HELM) | — | 67.9% |
Multimodal GLM-5.3-Flash leads
GLM-5.3-Flash: 42.8 (#27), GPT-5 Nano: 31.3 (#108)
| Benchmark | GLM-5.3-Flash | GPT-5 Nano |
|---|---|---|
| LMArena Vision | 1296 | 1159 |
| VPCT | — | 37.2% |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.0 (#25), GPT-5 Nano: 45.3 (#172)
| Benchmark | GLM-5.3-Flash | GPT-5 Nano |
|---|---|---|
| LMArena Non-English | 1462 | 1313 |
| LMArena Chinese | 1527 | 1356 |
| LMArena German | 1470 | 1327 |
| LMArena Japanese | 1429 | 1226 |
| LMArena Korean | 1446 | 1269 |
| LMArena Russian | 1469 | 1296 |
| LMArena Spanish | 1471 | 1360 |
| LMArena French | 1496 | — |
Instruction Following GLM-5.3-Flash leads
GLM-5.3-Flash: 77.5 (#20), GPT-5 Nano: 75.0 (#79)
| Benchmark | GLM-5.3-Flash | GPT-5 Nano |
|---|---|---|
| LMArena Instruction Following | 1478 | 1306 |
| IFEval | — | 93.2% |
Long Context GLM-5.3-Flash leads
GLM-5.3-Flash: 45.4 (#39), GPT-5 Nano: 31.3 (#281)
| Benchmark | GLM-5.3-Flash | GPT-5 Nano |
|---|---|---|
| LMArena Longer Query | 1482 | 1312 |
| Fiction.LiveBench | — | 44.4% |
Writing & Preference GLM-5.3-Flash leads
GLM-5.3-Flash: 65.3 (#50), GPT-5 Nano: 39.1 (#249)
| Benchmark | GLM-5.3-Flash | GPT-5 Nano |
|---|---|---|
| LMArena Text | 1471 | 1320 |
| LMArena Creative Writing | 1442 | 1249 |
| LMArena Multi-Turn | 1467 | 1311 |
| EQ-Bench Creative Writing | — | 705 |
| WildBench | — | 80.6% |
Frequently asked questions
Is GLM-5.3-Flash better than GPT-5 Nano?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 33.5 on the Noometry Index. GPT-5 Nano costs 1.7× less per token, which makes it the better buy when GLM-5.3-Flash's lead doesn't matter for your workload.
Which is cheaper, GLM-5.3-Flash or GPT-5 Nano?
GPT-5 Nano is cheaper. It lists at $0.05 per million input tokens and $0.40 per million output tokens; GLM-5.3-Flash lists at $0.15 and $0.50.
Is GLM-5.3-Flash or GPT-5 Nano better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 33.6 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3-Flash does, with 1M tokens against 400K.
How many benchmarks do GLM-5.3-Flash and GPT-5 Nano share?
28 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and GPT-5 Nano has 49.