Model comparison
GLM-5.3 vs GPT-4.1 nano
GLM-5.3 is the stronger model overall, scoring 54.8 to 27.9 on the Noometry Index. GPT-4.1 nano costs 12× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.
Last verified . 24 shared benchmarks.
Summary
- They share 24 benchmarks with published results for both. GLM-5.3 scores higher in 9 categories and GPT-4.1 nano in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.3 leads 46.1 to 8.5.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 91.1% for GLM-5.3 and 28.9% for GPT-4.1 nano.
- GPT-4.1 nano is cheaper at $0.10 / $0.40 per million input/output tokens, against $1.40 / $4.40 for GLM-5.3.
- GPT-4.1 nano accepts more context: 1.05M tokens versus 1M.
- GLM-5.3 has downloadable open weights; the other is API-only.
Side by side
| GLM-5.3 | GPT-4.1 nano | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 54.8 | 27.9 |
| Released | 2026-08-14 | 2025-04-14 |
| Weights | Open | Proprietary |
| Context window | 1M | 1.05M |
| Max output | 131K | 33K |
| Input $ / M tokens | $1.40 | $0.10 |
| Output $ / M tokens | $4.40 | $0.40 |
| Results tracked | 42 | 38 |
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Category by category
Coding GLM-5.3 leads
GLM-5.3: 59.5 (#14), GPT-4.1 nano: 24.1 (#330)
| Benchmark | GLM-5.3 | GPT-4.1 nano |
|---|---|---|
| SciCode | 59% | 25.9% |
| WeirdML | 75.4% | 19% |
| LMArena Coding | 1496 | 1306 |
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| Aider Polyglot | — | 8.9% |
| CursorBench | 42.6% | — |
| LMArena WebDev | 1622 | — |
| FrontierSWE | 30.2% | — |
| ALE-Bench | 1,317 | — |
Agentic & Tool Use GLM-5.3 leads
GLM-5.3: 36.4 (#38), GPT-4.1 nano: 26.5 (#104)
| Benchmark | GLM-5.3 | GPT-4.1 nano |
|---|---|---|
| APEX-Agents | 56.6% | — |
| Berkeley Function Calling Leaderboard | — | 33% |
| Vending-Bench 2 | 8,164 | — |
Reasoning GLM-5.3 leads
GLM-5.3: 46.1 (#46), GPT-4.1 nano: 8.5 (#349)
| Benchmark | GLM-5.3 | GPT-4.1 nano |
|---|---|---|
| CritPt | 19.1% | 0% |
| LMArena Hard Prompts | 1489 | 1286 |
| DTBench | 87.7% | 52.5% |
| LMCA | 55.5% | 5.5% |
| Epoch Capabilities Index | 155.61 | 129.62 |
| ARC-AGI-2 | — | 0% |
| Kagi LLM Benchmark | — | 33.3% |
| NYT Connections (extended) | 74.2% | — |
| ARC-AGI-1 | — | 0% |
| Chess Puzzles | 21% | — |
| Mystery Game Puzzles | 33% | — |
| Bench to the Future 3 | 0.15 | — |
Math GLM-5.3 leads
GLM-5.3: 62.3 (#33), GPT-4.1 nano: 26.9 (#252)
| Benchmark | GLM-5.3 | GPT-4.1 nano |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 91.1% | 28.9% |
| LMArena Math | 1489 | 1274 |
| FrontierMath (Tiers 1-3) | 68.8% | — |
| FrontierMath Tier 4 | 29.3% | — |
| ProofBench | 49% | — |
| Omni-MATH | — | 36.7% |
| MATH Level 5 | — | 70% |
| FrontierMath (Feb 2025 set) | — | 1% |
Knowledge GLM-5.3 leads
GLM-5.3: 58.3 (#37), GPT-4.1 nano: 21.8 (#273)
| Benchmark | GLM-5.3 | GPT-4.1 nano |
|---|---|---|
| GPQA Diamond | 90.9% | 48.9% |
| SimpleQA Verified | 41% | 6% |
| LMArena Expert | 1516 | 1272 |
| MMLU-Pro | — | 55% |
| GPQA (HELM) | — | 50.7% |
Multimodal Not comparable
GLM-5.3: —, GPT-4.1 nano: 29.2 (#113)
| Benchmark | GLM-5.3 | GPT-4.1 nano |
|---|---|---|
| LMArena Vision | — | 1063 |
Multilingual GLM-5.3 leads
GLM-5.3: 55.7 (#28), GPT-4.1 nano: 41.6 (#205)
| Benchmark | GLM-5.3 | GPT-4.1 nano |
|---|---|---|
| LMArena Non-English | 1457 | 1260 |
| LMArena Chinese | 1528 | 1270 |
| LMArena German | 1499 | 1288 |
| LMArena Japanese | 1453 | 1198 |
| LMArena Russian | 1463 | 1261 |
| LMArena French | 1499 | — |
| LMArena Korean | 1472 | — |
| LMArena Spanish | 1460 | — |
Instruction Following GLM-5.3 leads
GLM-5.3: 77.5 (#23), GPT-4.1 nano: 67.8 (#193)
| Benchmark | GLM-5.3 | GPT-4.1 nano |
|---|---|---|
| LMArena Instruction Following | 1477 | 1267 |
| IFEval | — | 84.3% |
Long Context GLM-5.3 leads
GLM-5.3: 45.4 (#41), GPT-4.1 nano: 23.7 (#296)
| Benchmark | GLM-5.3 | GPT-4.1 nano |
|---|---|---|
| LMArena Longer Query | 1482 | 1283 |
| Fiction.LiveBench | — | 25% |
Writing & Preference GLM-5.3 leads
GLM-5.3: 75.7 (#6), GPT-4.1 nano: 40.5 (#243)
| Benchmark | GLM-5.3 | GPT-4.1 nano |
|---|---|---|
| LMArena Text | 1471 | 1285 |
| LMArena Creative Writing | 1457 | 1260 |
| EQ-Bench Creative Writing | 2075 | 946 |
| LMArena Multi-Turn | 1472 | 1277 |
| WildBench | — | 81.2% |
Frequently asked questions
Is GLM-5.3 better than GPT-4.1 nano?
GLM-5.3 is the stronger model overall, scoring 54.8 to 27.9 on the Noometry Index. GPT-4.1 nano costs 12× 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-4.1 nano?
GPT-4.1 nano is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; GLM-5.3 lists at $1.40 and $4.40.
Is GLM-5.3 or GPT-4.1 nano better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 24.1 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 nano does, with 1.05M tokens against 1M.
How many benchmarks do GLM-5.3 and GPT-4.1 nano share?
24 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and GPT-4.1 nano has 38.