Model comparison
GLM-5.3-Flash vs GPT-4.1
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 35.9 on the Noometry Index.
Last verified . 26 shared benchmarks.
Summary
- They share 26 benchmarks with published results for both. GLM-5.3-Flash scores higher in 9 categories and GPT-4.1 in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.3-Flash leads 48.0 to 11.7.
- The biggest single-benchmark swing is ARC-AGI-1: 91% for GLM-5.3-Flash and 5.5% for GPT-4.1.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $2 / $8 for GPT-4.1.
- GPT-4.1 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-4.1 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 51.8 | 35.9 |
| Released | 2026-08-20 | 2025-04-14 |
| Weights | Open | Proprietary |
| Context window | 1M | 1.05M |
| Max output | 131K | 33K |
| Input $ / M tokens | $0.15 | $2 |
| Output $ / M tokens | $0.50 | $8 |
| Results tracked | 40 | 52 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-5.3-Flash: 53.1 (#31), GPT-4.1: 34.4 (#238)
| Benchmark | GLM-5.3-Flash | GPT-4.1 |
|---|---|---|
| LMArena Coding | 1508 | 1391 |
| ALE-Bench | 303.55 | 558.1 |
| SWE-bench Verified | — | 48.5% |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| SWE-bench Verified (bash only) | — | 39.6% |
| Aider Polyglot | — | 52.4% |
| CursorBench | 36.8% | — |
| LMArena WebDev | 1609 | — |
| FrontierSWE | 18.1% | — |
| SciCode | 51.6% | — |
| WeirdML | — | 39% |
| CadEval | — | 42% |
Agentic & Tool Use Too close to call
GLM-5.3-Flash: 34.2 (#47), GPT-4.1: 34.7 (#43)
| Benchmark | GLM-5.3-Flash | GPT-4.1 |
|---|---|---|
| APEX-Agents | 52.8% | — |
| Berkeley Function Calling Leaderboard | — | 54% |
| GDP.pdf | 14% | — |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 48.0 (#42), GPT-4.1: 11.7 (#339)
| Benchmark | GLM-5.3-Flash | GPT-4.1 |
|---|---|---|
| ARC-AGI-2 | 65.8% | 0.4% |
| ARC-AGI-1 | 91% | 5.5% |
| Chess Puzzles | 14% | 6% |
| LMArena Hard Prompts | 1491 | 1384 |
| Epoch Capabilities Index | 151.88 | 136.78 |
| SimpleBench | — | 27% |
| Kagi LLM Benchmark | — | 52.3% |
| CritPt | 15.4% | — |
| EnigmaEval | — | 2.2% |
| Mystery Game Puzzles | 8% | — |
| DTBench | — | 68.3% |
| LMCA | — | 25.6% |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
| ForecastBench | — | 61.5 |
Math GLM-5.3-Flash leads
GLM-5.3-Flash: 53.3 (#47), GPT-4.1: 22.3 (#280)
| Benchmark | GLM-5.3-Flash | GPT-4.1 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | 6% |
| OTIS Mock AIME 2024-2025 | 93.9% | 38.3% |
| LMArena Math | 1500 | 1370 |
| FrontierMath Tier 4 | 17.1% | — |
| ProofBench | 21% | — |
| Omni-MATH | — | 47.1% |
| MATH Level 5 | — | 83% |
| FrontierMath (Feb 2025 set) | — | 5.5% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge GLM-5.3-Flash leads
GLM-5.3-Flash: 58.4 (#36), GPT-4.1: 37.1 (#160)
| Benchmark | GLM-5.3-Flash | GPT-4.1 |
|---|---|---|
| GPQA Diamond | 90.2% | 66.9% |
| LMArena Expert | 1513 | 1364 |
| Humanity's Last Exam | — | 5.4% |
| SimpleQA Verified | — | 31.1% |
| MMLU-Pro | — | 81.1% |
| Vectara Hallucination Rate | — | 5.6% |
| GPQA (HELM) | — | 65.9% |
Multimodal GLM-5.3-Flash leads
GLM-5.3-Flash: 42.8 (#27), GPT-4.1: 38.2 (#67)
| Benchmark | GLM-5.3-Flash | GPT-4.1 |
|---|---|---|
| LMArena Vision | 1296 | 1211 |
| GeoBench | — | 72% |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.0 (#25), GPT-4.1: 49.4 (#133)
| Benchmark | GLM-5.3-Flash | GPT-4.1 |
|---|---|---|
| LMArena Non-English | 1462 | 1370 |
| LMArena Chinese | 1527 | 1382 |
| LMArena French | 1496 | 1382 |
| LMArena German | 1470 | 1381 |
| LMArena Japanese | 1429 | 1319 |
| LMArena Korean | 1446 | 1339 |
| LMArena Russian | 1469 | 1377 |
| LMArena Spanish | 1471 | 1376 |
Instruction Following GLM-5.3-Flash leads
GLM-5.3-Flash: 77.5 (#20), GPT-4.1: 71.3 (#153)
| Benchmark | GLM-5.3-Flash | GPT-4.1 |
|---|---|---|
| LMArena Instruction Following | 1478 | 1367 |
| IFEval | — | 83.8% |
Long Context GLM-5.3-Flash leads
GLM-5.3-Flash: 45.4 (#39), GPT-4.1: 40.0 (#163)
| Benchmark | GLM-5.3-Flash | GPT-4.1 |
|---|---|---|
| LMArena Longer Query | 1482 | 1385 |
| Fiction.LiveBench | — | 63.9% |
Writing & Preference GLM-5.3-Flash leads
GLM-5.3-Flash: 65.3 (#50), GPT-4.1: 57.6 (#125)
| Benchmark | GLM-5.3-Flash | GPT-4.1 |
|---|---|---|
| LMArena Text | 1471 | 1383 |
| LMArena Creative Writing | 1442 | 1363 |
| LMArena Multi-Turn | 1467 | 1398 |
| EQ-Bench Creative Writing | — | 1420 |
| WildBench | — | 85.4% |
Frequently asked questions
Is GLM-5.3-Flash better than GPT-4.1?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 35.9 on the Noometry Index.
Which is cheaper, GLM-5.3-Flash or GPT-4.1?
GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; GPT-4.1 lists at $2 and $8.
Is GLM-5.3-Flash or GPT-4.1 better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 34.4 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 does, with 1.05M tokens against 1M.
How many benchmarks do GLM-5.3-Flash and GPT-4.1 share?
26 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and GPT-4.1 has 52.