Model comparison
GLM-4.6 vs GPT-4.1
GLM-4.6 is the stronger model overall, scoring 41.4 to 35.9 on the Noometry Index.
Last verified . 25 shared benchmarks.
Summary
- They share 25 benchmarks with published results for both. GLM-4.6 scores higher in 8 categories and GPT-4.1 in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-4.6 leads 39.1 to 22.3.
- The biggest single-benchmark swing is Berkeley Function Calling Leaderboard: 72.4% for GLM-4.6 and 54% for GPT-4.1.
- GLM-4.6 is cheaper at $0.60 / $2.20 per million input/output tokens, against $2 / $8 for GPT-4.1.
- GPT-4.1 accepts more context: 1.05M tokens versus 205K.
- GLM-4.6 has downloadable open weights; the other is API-only.
Side by side
| GLM-4.6 | GPT-4.1 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 41.4 | 35.9 |
| Released | 2025-09-30 | 2025-04-14 |
| Weights | Open | Proprietary |
| Context window | 205K | 1.05M |
| Max output | 131K | 33K |
| Input $ / M tokens | $0.60 | $2 |
| Output $ / M tokens | $2.20 | $8 |
| Results tracked | 29 | 52 |
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Category by category
Coding GLM-4.6 leads
GLM-4.6: 40.1 (#148), GPT-4.1: 34.4 (#238)
| Benchmark | GLM-4.6 | GPT-4.1 |
|---|---|---|
| SWE-bench Verified (bash only) | 55.4% | 39.6% |
| LMArena Coding | 1449 | 1391 |
| ALE-Bench | 340.82 | 558.1 |
| SWE-bench Verified | — | 48.5% |
| Aider Polyglot | — | 52.4% |
| LMArena WebDev | 1340 | — |
| SciCode | 38.4% | — |
| WeirdML | — | 39% |
| CadEval | — | 42% |
Agentic & Tool Use GPT-4.1 leads
GLM-4.6: 32.3 (#66), GPT-4.1: 34.7 (#43)
| Benchmark | GLM-4.6 | GPT-4.1 |
|---|---|---|
| Berkeley Function Calling Leaderboard | 72.4% | 54% |
| Terminal-Bench | 24.5% | — |
Reasoning GLM-4.6 leads
GLM-4.6: 23.7 (#172), GPT-4.1: 11.7 (#339)
| Benchmark | GLM-4.6 | GPT-4.1 |
|---|---|---|
| Kagi LLM Benchmark | 47.4% | 52.3% |
| LMArena Hard Prompts | 1440 | 1384 |
| ARC-AGI-2 | — | 0.4% |
| SimpleBench | — | 27% |
| ARC-AGI-1 | — | 5.5% |
| CritPt | 1.1% | — |
| Chess Puzzles | — | 6% |
| EnigmaEval | — | 2.2% |
| DTBench | — | 68.3% |
| LMCA | — | 25.6% |
| Epoch Capabilities Index | — | 136.78 |
| ForecastBench | — | 61.5 |
Math GLM-4.6 leads
GLM-4.6: 39.1 (#111), GPT-4.1: 22.3 (#280)
| Benchmark | GLM-4.6 | GPT-4.1 |
|---|---|---|
| LMArena Math | 1432 | 1370 |
| FrontierMath (Feb 2025 set) | 3.8% | 5.5% |
| FrontierMath Tier 4 (v1) | 2.1% | 0% |
| FrontierMath (Tiers 1-3) | — | 6% |
| OTIS Mock AIME 2024-2025 | — | 38.3% |
| Omni-MATH | — | 47.1% |
| MATH Level 5 | — | 83% |
Knowledge GLM-4.6 leads
GLM-4.6: 40.2 (#124), GPT-4.1: 37.1 (#160)
| Benchmark | GLM-4.6 | GPT-4.1 |
|---|---|---|
| Vectara Hallucination Rate | 9.5% | 5.6% |
| LMArena Expert | 1431 | 1364 |
| GPQA Diamond | — | 66.9% |
| Humanity's Last Exam | — | 5.4% |
| SimpleQA Verified | — | 31.1% |
| MMLU-Pro | — | 81.1% |
| GPQA (HELM) | — | 65.9% |
Multimodal Not comparable
GLM-4.6: —, GPT-4.1: 38.2 (#67)
| Benchmark | GLM-4.6 | GPT-4.1 |
|---|---|---|
| LMArena Vision | — | 1211 |
| GeoBench | — | 72% |
Multilingual GLM-4.6 leads
GLM-4.6: 53.5 (#66), GPT-4.1: 49.4 (#133)
| Benchmark | GLM-4.6 | GPT-4.1 |
|---|---|---|
| LMArena Non-English | 1426 | 1370 |
| LMArena Chinese | 1499 | 1382 |
| LMArena French | 1459 | 1382 |
| LMArena German | 1447 | 1381 |
| LMArena Japanese | 1393 | 1319 |
| LMArena Korean | 1400 | 1339 |
| LMArena Russian | 1419 | 1377 |
| LMArena Spanish | 1436 | 1376 |
Instruction Following GLM-4.6 leads
GLM-4.6: 74.3 (#98), GPT-4.1: 71.3 (#153)
| Benchmark | GLM-4.6 | GPT-4.1 |
|---|---|---|
| LMArena Instruction Following | 1410 | 1367 |
| IFEval | — | 83.8% |
Long Context GLM-4.6 leads
GLM-4.6: 43.4 (#94), GPT-4.1: 40.0 (#163)
| Benchmark | GLM-4.6 | GPT-4.1 |
|---|---|---|
| LMArena Longer Query | 1422 | 1385 |
| Fiction.LiveBench | — | 63.9% |
Writing & Preference GLM-4.6 leads
GLM-4.6: 61.1 (#90), GPT-4.1: 57.6 (#125)
| Benchmark | GLM-4.6 | GPT-4.1 |
|---|---|---|
| LMArena Text | 1440 | 1383 |
| LMArena Creative Writing | 1411 | 1363 |
| EQ-Bench Creative Writing | 1411 | 1420 |
| LMArena Multi-Turn | 1427 | 1398 |
| WildBench | — | 85.4% |
Frequently asked questions
Is GLM-4.6 better than GPT-4.1?
GLM-4.6 is the stronger model overall, scoring 41.4 to 35.9 on the Noometry Index.
Which is cheaper, GLM-4.6 or GPT-4.1?
GLM-4.6 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; GPT-4.1 lists at $2 and $8.
Is GLM-4.6 or GPT-4.1 better for coding?
GLM-4.6 scores higher on coding benchmarks: 40.1 versus 34.4 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 does, with 1.05M tokens against 205K.
How many benchmarks do GLM-4.6 and GPT-4.1 share?
25 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and GPT-4.1 has 52.