Model comparison
GLM-5.1 vs GPT-4.1 nano
GLM-5.1 is the stronger model overall, scoring 47.6 to 27.7 on the Noometry Index. GPT-4.1 nano costs 12× less per token, which makes it the better buy when GLM-5.1's lead doesn't matter for your workload.
Last verified . 23 shared benchmarks.
Summary
- They share 23 benchmarks with published results for both. GLM-5.1 scores higher in 8 categories and GPT-4.1 nano in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5.1 leads 55.0 to 21.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 93.3% for GLM-5.1 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-5.1.
- GPT-4.1 nano accepts more context: 1.05M tokens versus 200K.
- GLM-5.1 has downloadable open weights; the other is API-only.
Side by side
| GLM-5.1 | GPT-4.1 nano | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 47.6 | 27.7 |
| Released | 2026-04-07 | 2025-04-14 |
| Weights | Open | Proprietary |
| Context window | 200K | 1.05M |
| Max output | 131K | 33K |
| Input $ / M tokens | $1.40 | $0.10 |
| Output $ / M tokens | $4.40 | $0.40 |
| Results tracked | 41 | 38 |
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Category by category
Coding GLM-5.1 leads
GLM-5.1: 48.5 (#52), GPT-4.1 nano: 23.8 (#325)
| Benchmark | GLM-5.1 | GPT-4.1 nano |
|---|---|---|
| SciCode | 43.8% | 25.9% |
| WeirdML | 57.1% | 19% |
| LMArena Coding | 1486 | 1306 |
| SWE-bench Verified | 74.2% | — |
| Aider Polyglot | — | 8.9% |
| LMArena WebDev | 1509 | — |
| ALE-Bench | 887.1 | — |
Agentic & Tool Use GPT-4.1 nano leads
GLM-5.1: 24.8 (#106), GPT-4.1 nano: 26.3 (#98)
| Benchmark | GLM-5.1 | GPT-4.1 nano |
|---|---|---|
| APEX-Agents | 40.9% | — |
| Berkeley Function Calling Leaderboard | — | 33% |
| ExploitBench | 18.1% | — |
| GBAEval | 0% | — |
| Vending-Bench 2 | 5,634 | — |
Reasoning GLM-5.1 leads
GLM-5.1: 38.2 (#57), GPT-4.1 nano: 8.6 (#344)
| Benchmark | GLM-5.1 | GPT-4.1 nano |
|---|---|---|
| CritPt | 4.6% | 0% |
| LMArena Hard Prompts | 1472 | 1286 |
| Epoch Capabilities Index | 149.86 | 129.62 |
| ARC-AGI-2 | — | 0% |
| SimpleBench | 55.1% | — |
| Kagi LLM Benchmark | — | 33.3% |
| NYT Connections (extended) | 77.7% | — |
| ARC-AGI-1 | — | 0% |
| Chess Puzzles | 19% | — |
| Thematic Generalization | 69.8% | — |
| DTBench | — | 52.5% |
| LMCA | — | 5.5% |
Math GLM-5.1 leads
GLM-5.1: 49.0 (#48), GPT-4.1 nano: 25.5 (#246)
| Benchmark | GLM-5.1 | GPT-4.1 nano |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 93.3% | 28.9% |
| LMArena Math | 1472 | 1274 |
| FrontierMath (Feb 2025 set) | 33.4% | 1% |
| FrontierMath (Tiers 1-3) | 36.8% | — |
| MathArena Final-Answer Competitions | 67.1% | — |
| ProofBench | 22.2% | — |
| Omni-MATH | — | 36.7% |
| MATH Level 5 | — | 70% |
| FrontierMath Tier 4 (v1) | 12.5% | — |
Knowledge GLM-5.1 leads
GLM-5.1: 55.0 (#45), GPT-4.1 nano: 21.8 (#268)
| Benchmark | GLM-5.1 | GPT-4.1 nano |
|---|---|---|
| GPQA Diamond | 89.9% | 48.9% |
| SimpleQA Verified | 34% | 6% |
| LMArena Expert | 1477 | 1272 |
| MMLU-Pro | — | 55% |
| GPQA (HELM) | — | 50.7% |
Multimodal Not comparable
GLM-5.1: —, GPT-4.1 nano: 28.6 (#108)
| Benchmark | GLM-5.1 | GPT-4.1 nano |
|---|---|---|
| LMArena Vision | — | 1063 |
Multilingual GLM-5.1 leads
GLM-5.1: 55.2 (#34), GPT-4.1 nano: 41.8 (#200)
| Benchmark | GLM-5.1 | GPT-4.1 nano |
|---|---|---|
| LMArena Non-English | 1448 | 1260 |
| LMArena Chinese | 1516 | 1270 |
| LMArena German | 1466 | 1288 |
| LMArena Japanese | 1436 | 1199 |
| LMArena Russian | 1453 | 1261 |
| LMArena French | 1478 | — |
| LMArena Korean | 1423 | — |
| LMArena Spanish | 1467 | — |
Instruction Following GLM-5.1 leads
GLM-5.1: 76.5 (#41), GPT-4.1 nano: 68.0 (#188)
| Benchmark | GLM-5.1 | GPT-4.1 nano |
|---|---|---|
| LMArena Instruction Following | 1451 | 1267 |
| IFEval | — | 84.3% |
Long Context GLM-5.1 leads
GLM-5.1: 44.9 (#51), GPT-4.1 nano: 23.7 (#291)
| Benchmark | GLM-5.1 | GPT-4.1 nano |
|---|---|---|
| LMArena Longer Query | 1467 | 1283 |
| Fiction.LiveBench | — | 25% |
Writing & Preference GLM-5.1 leads
GLM-5.1: 67.2 (#29), GPT-4.1 nano: 40.7 (#238)
| Benchmark | GLM-5.1 | GPT-4.1 nano |
|---|---|---|
| LMArena Text | 1462 | 1285 |
| LMArena Creative Writing | 1454 | 1260 |
| EQ-Bench Creative Writing | 1592 | 946 |
| LMArena Multi-Turn | 1473 | 1277 |
| WildBench | — | 81.2% |
Frequently asked questions
Is GLM-5.1 better than GPT-4.1 nano?
GLM-5.1 is the stronger model overall, scoring 47.6 to 27.7 on the Noometry Index. GPT-4.1 nano costs 12× less per token, which makes it the better buy when GLM-5.1's lead doesn't matter for your workload.
Which is cheaper, GLM-5.1 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-5.1 lists at $1.40 and $4.40.
Is GLM-5.1 or GPT-4.1 nano better for coding?
GLM-5.1 scores higher on coding benchmarks: 48.5 versus 23.8 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 nano does, with 1.05M tokens against 200K.
How many benchmarks do GLM-5.1 and GPT-4.1 nano share?
23 benchmarks have published results for both models. GLM-5.1 has 41 scored results on Noometry and GPT-4.1 nano has 38.