Model comparison
GLM-5 vs GPT-4.1 nano
GLM-5 is the stronger model overall, scoring 45.9 to 27.7 on the Noometry Index. GPT-4.1 nano costs 8.9× less per token, which makes it the better buy when GLM-5'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 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 knowledge, where GLM-5 leads 52.2 to 21.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 80% for GLM-5 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 / $3.20 for GLM-5.
- GPT-4.1 nano accepts more context: 1.05M tokens versus 205K.
- GLM-5 has downloadable open weights; the other is API-only.
Side by side
| GLM-5 | GPT-4.1 nano | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 45.9 | 27.7 |
| Released | 2026-02-11 | 2025-04-14 |
| Weights | Open | Proprietary |
| Context window | 205K | 1.05M |
| Max output | 131K | 33K |
| Input $ / M tokens | $1 | $0.10 |
| Output $ / M tokens | $3.20 | $0.40 |
| Results tracked | 45 | 38 |
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Category by category
Coding GLM-5 leads
GLM-5: 49.0 (#47), GPT-4.1 nano: 23.8 (#325)
| Benchmark | GLM-5 | GPT-4.1 nano |
|---|---|---|
| WeirdML | 48.2% | 19% |
| LMArena Coding | 1461 | 1306 |
| SWE-bench Verified | 72.1% | — |
| SWE-bench Verified (bash only) | 72.8% | — |
| Aider Polyglot | — | 8.9% |
| LMArena WebDev | 1434 | — |
| SWE-bench Multilingual | 69.7% | — |
| SciCode | — | 25.9% |
| ALE-Bench | 765.62 | — |
Agentic & Tool Use GLM-5 leads
GLM-5: 30.9 (#65), GPT-4.1 nano: 26.3 (#98)
| Benchmark | GLM-5 | GPT-4.1 nano |
|---|---|---|
| Terminal-Bench | 52.4% | — |
| Berkeley Function Calling Leaderboard | — | 33% |
| τ²-bench Airline | 82.5% | — |
| τ²-bench Banking | 9.8% | — |
| τ²-bench Retail | 73.7% | — |
| τ²-bench Telecom | 86.8% | — |
| Vending-Bench 2 | 4,432 | — |
Reasoning GLM-5 leads
GLM-5: 27.6 (#100), GPT-4.1 nano: 8.6 (#344)
| Benchmark | GLM-5 | GPT-4.1 nano |
|---|---|---|
| ARC-AGI-2 | 4.9% | 0% |
| Kagi LLM Benchmark | 75% | 33.3% |
| ARC-AGI-1 | 44.7% | 0% |
| LMArena Hard Prompts | 1451 | 1286 |
| Epoch Capabilities Index | 145.84 | 129.62 |
| SimpleBench | 53.2% | — |
| NYT Connections (extended) | 74.8% | — |
| CritPt | — | 0% |
| Chess Puzzles | 10% | — |
| DTBench | — | 52.5% |
| LMCA | — | 5.5% |
| ForecastBench | 61 | — |
Math GLM-5 leads
GLM-5: 44.8 (#66), GPT-4.1 nano: 25.5 (#246)
| Benchmark | GLM-5 | GPT-4.1 nano |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 80% | 28.9% |
| LMArena Math | 1440 | 1274 |
| FrontierMath (Feb 2025 set) | 16.4% | 1% |
| MathArena Final-Answer Competitions | 65.7% | — |
| Omni-MATH | — | 36.7% |
| MATH Level 5 | — | 70% |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GLM-5 leads
GLM-5: 52.2 (#59), GPT-4.1 nano: 21.8 (#268)
| Benchmark | GLM-5 | GPT-4.1 nano |
|---|---|---|
| GPQA Diamond | 87.8% | 48.9% |
| LMArena Expert | 1454 | 1272 |
| SimpleQA Verified | — | 6% |
| MMLU-Pro | — | 55% |
| Vectara Hallucination Rate | 10.1% | — |
| GPQA (HELM) | — | 50.7% |
Multimodal Not comparable
GLM-5: —, GPT-4.1 nano: 28.6 (#108)
| Benchmark | GLM-5 | GPT-4.1 nano |
|---|---|---|
| LMArena Vision | — | 1063 |
Multilingual GLM-5 leads
GLM-5: 53.9 (#57), GPT-4.1 nano: 41.8 (#200)
| Benchmark | GLM-5 | GPT-4.1 nano |
|---|---|---|
| LMArena Non-English | 1430 | 1260 |
| LMArena Chinese | 1511 | 1270 |
| LMArena German | 1445 | 1288 |
| LMArena Japanese | 1416 | 1199 |
| LMArena Russian | 1436 | 1261 |
| LMArena French | 1455 | — |
| LMArena Korean | 1421 | — |
| LMArena Spanish | 1452 | — |
Instruction Following GLM-5 leads
GLM-5: 75.4 (#64), GPT-4.1 nano: 68.0 (#188)
| Benchmark | GLM-5 | GPT-4.1 nano |
|---|---|---|
| LMArena Instruction Following | 1427 | 1267 |
| IFEval | — | 84.3% |
Long Context GLM-5 leads
GLM-5: 44.6 (#58), GPT-4.1 nano: 23.7 (#291)
| Benchmark | GLM-5 | GPT-4.1 nano |
|---|---|---|
| LMArena Longer Query | 1446 | 1283 |
| Fiction.LiveBench | — | 25% |
| CL-bench | 18.7% | — |
Writing & Preference GLM-5 leads
GLM-5: 66.2 (#38), GPT-4.1 nano: 40.7 (#238)
| Benchmark | GLM-5 | GPT-4.1 nano |
|---|---|---|
| LMArena Text | 1446 | 1285 |
| LMArena Creative Writing | 1438 | 1260 |
| EQ-Bench Creative Writing | 1601 | 946 |
| LMArena Multi-Turn | 1456 | 1277 |
| WildBench | — | 81.2% |
Frequently asked questions
Is GLM-5 better than GPT-4.1 nano?
GLM-5 is the stronger model overall, scoring 45.9 to 27.7 on the Noometry Index. GPT-4.1 nano costs 8.9× less per token, which makes it the better buy when GLM-5's lead doesn't matter for your workload.
Which is cheaper, GLM-5 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 lists at $1 and $3.20.
Is GLM-5 or GPT-4.1 nano better for coding?
GLM-5 scores higher on coding benchmarks: 49.0 versus 23.8 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 nano does, with 1.05M tokens against 205K.
How many benchmarks do GLM-5 and GPT-4.1 nano share?
23 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and GPT-4.1 nano has 38.