Model comparison
GLM-5.3-Flash vs GPT-4.1 mini
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 33.6 on the Noometry Index.
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-4.1 mini in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.3-Flash leads 48.0 to 10.8.
- The biggest single-benchmark swing is ARC-AGI-1: 91% for GLM-5.3-Flash and 3.5% for GPT-4.1 mini.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.40 / $1.60 for GPT-4.1 mini.
- GPT-4.1 mini 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 mini | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 51.8 | 33.6 |
| Released | 2026-08-20 | 2025-04-14 |
| Weights | Open | Proprietary |
| Context window | 1M | 1.05M |
| Max output | 131K | 33K |
| Input $ / M tokens | $0.15 | $0.40 |
| Output $ / M tokens | $0.50 | $1.60 |
| Results tracked | 40 | 47 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-5.3-Flash: 53.1 (#31), GPT-4.1 mini: 30.6 (#293)
| Benchmark | GLM-5.3-Flash | GPT-4.1 mini |
|---|---|---|
| SciCode | 51.6% | 40.4% |
| LMArena Coding | 1508 | 1367 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| SWE-bench Verified (bash only) | — | 23.9% |
| Aider Polyglot | — | 32.4% |
| CursorBench | 36.8% | — |
| LMArena WebDev | 1609 | — |
| FrontierSWE | 18.1% | — |
| WeirdML | — | 37.6% |
| BigCodeBench Instruct | — | 48.9% |
| CadEval | — | 16% |
| ALE-Bench | 303.55 | — |
Agentic & Tool Use Too close to call
GLM-5.3-Flash: 34.2 (#47), GPT-4.1 mini: 33.3 (#55)
| Benchmark | GLM-5.3-Flash | GPT-4.1 mini |
|---|---|---|
| APEX-Agents | 52.8% | — |
| Berkeley Function Calling Leaderboard | — | 50.5% |
| GDP.pdf | 14% | — |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 48.0 (#42), GPT-4.1 mini: 10.8 (#340)
| Benchmark | GLM-5.3-Flash | GPT-4.1 mini |
|---|---|---|
| ARC-AGI-2 | 65.8% | 0% |
| ARC-AGI-1 | 91% | 3.5% |
| CritPt | 15.4% | 0% |
| Chess Puzzles | 14% | 7% |
| LMArena Hard Prompts | 1491 | 1349 |
| Mystery Game Puzzles | 8% | 7% |
| Epoch Capabilities Index | 151.88 | 135.01 |
| Kagi LLM Benchmark | — | 48.6% |
| DTBench | — | 68.8% |
| LMCA | — | 21.1% |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
Math GLM-5.3-Flash leads
GLM-5.3-Flash: 53.3 (#47), GPT-4.1 mini: 24.1 (#270)
| Benchmark | GLM-5.3-Flash | GPT-4.1 mini |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | 6.7% |
| OTIS Mock AIME 2024-2025 | 93.9% | 44.7% |
| LMArena Math | 1500 | 1343 |
| FrontierMath Tier 4 | 17.1% | — |
| ProofBench | 21% | — |
| Omni-MATH | — | 49.1% |
| MATH Level 5 | — | 87.3% |
| FrontierMath (Feb 2025 set) | — | 4.5% |
Knowledge GLM-5.3-Flash leads
GLM-5.3-Flash: 58.4 (#36), GPT-4.1 mini: 34.7 (#194)
| Benchmark | GLM-5.3-Flash | GPT-4.1 mini |
|---|---|---|
| GPQA Diamond | 90.2% | 65.8% |
| LMArena Expert | 1513 | 1338 |
| SimpleQA Verified | — | 12.7% |
| MMLU-Pro | — | 78.3% |
| GPQA (HELM) | — | 61.4% |
Multimodal GLM-5.3-Flash leads
GLM-5.3-Flash: 42.8 (#27), GPT-4.1 mini: 35.8 (#82)
| Benchmark | GLM-5.3-Flash | GPT-4.1 mini |
|---|---|---|
| LMArena Vision | 1296 | 1181 |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.0 (#25), GPT-4.1 mini: 45.7 (#166)
| Benchmark | GLM-5.3-Flash | GPT-4.1 mini |
|---|---|---|
| LMArena Non-English | 1462 | 1318 |
| LMArena Chinese | 1527 | 1329 |
| LMArena French | 1496 | 1358 |
| LMArena German | 1470 | 1351 |
| LMArena Japanese | 1429 | 1290 |
| LMArena Korean | 1446 | 1298 |
| LMArena Russian | 1469 | 1324 |
| LMArena Spanish | 1471 | 1319 |
Instruction Following GLM-5.3-Flash leads
GLM-5.3-Flash: 77.5 (#20), GPT-4.1 mini: 73.7 (#118)
| Benchmark | GLM-5.3-Flash | GPT-4.1 mini |
|---|---|---|
| LMArena Instruction Following | 1478 | 1333 |
| IFEval | — | 90.4% |
Long Context GLM-5.3-Flash leads
GLM-5.3-Flash: 45.4 (#39), GPT-4.1 mini: 31.8 (#275)
| Benchmark | GLM-5.3-Flash | GPT-4.1 mini |
|---|---|---|
| LMArena Longer Query | 1482 | 1344 |
| Fiction.LiveBench | — | 44.4% |
Writing & Preference GLM-5.3-Flash leads
GLM-5.3-Flash: 65.3 (#50), GPT-4.1 mini: 48.6 (#199)
| Benchmark | GLM-5.3-Flash | GPT-4.1 mini |
|---|---|---|
| LMArena Text | 1471 | 1340 |
| LMArena Creative Writing | 1442 | 1300 |
| LMArena Multi-Turn | 1467 | 1354 |
| EQ-Bench Creative Writing | — | 1147 |
| WildBench | — | 83.8% |
Frequently asked questions
Is GLM-5.3-Flash better than GPT-4.1 mini?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 33.6 on the Noometry Index.
Which is cheaper, GLM-5.3-Flash or GPT-4.1 mini?
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 mini lists at $0.40 and $1.60.
Is GLM-5.3-Flash or GPT-4.1 mini better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 30.6 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 mini does, with 1.05M tokens against 1M.
How many benchmarks do GLM-5.3-Flash and GPT-4.1 mini share?
28 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and GPT-4.1 mini has 47.