Model comparison
GLM-4.6 vs GPT-5.6 Luna
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 41.4 on the Noometry Index.
Last verified . 23 shared benchmarks.
Summary
- They share 23 benchmarks with published results for both. GLM-4.6 scores higher in 1 category and GPT-5.6 Luna in 8 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Luna leads 77.7 to 39.1.
- The biggest single-benchmark swing is CritPt: 1.1% for GLM-4.6 and 20.6% for GPT-5.6 Luna.
- GPT-5.6 Luna is cheaper at $0.20 / $1.20 per million input/output tokens, against $0.60 / $2.20 for GLM-4.6.
- GPT-5.6 Luna 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-5.6 Luna | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 41.4 | 54.6 |
| Released | 2025-09-30 | 2026-07-09 |
| Weights | Open | Proprietary |
| Context window | 205K | 1.05M |
| Max output | 131K | 128K |
| Input $ / M tokens | $0.60 | $0.20 |
| Output $ / M tokens | $2.20 | $1.20 |
| Results tracked | 29 | 52 |
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Category by category
Coding GPT-5.6 Luna leads
GLM-4.6: 40.1 (#148), GPT-5.6 Luna: 54.5 (#28)
| Benchmark | GLM-4.6 | GPT-5.6 Luna |
|---|---|---|
| LMArena WebDev | 1340 | 1519 |
| SciCode | 38.4% | 53.6% |
| LMArena Coding | 1449 | 1466 |
| ALE-Bench | 340.82 | 1,667 |
| DeepSWE | — | 67.2% |
| FrontierCode | — | 39.8% |
| SWE-bench Verified (bash only) | 55.4% | — |
| CursorBench | — | 35.9% |
| WeirdML | — | 60.9% |
Agentic & Tool Use GPT-5.6 Luna leads
GLM-4.6: 32.3 (#66), GPT-5.6 Luna: 34.4 (#45)
| Benchmark | GLM-4.6 | GPT-5.6 Luna |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| APEX-Agents | — | 43% |
| Berkeley Function Calling Leaderboard | 72.4% | — |
| BALROG | — | 45.6% |
| GDP.pdf | — | 22.7% |
| Vending-Bench 2 | — | 4,095 |
Reasoning GPT-5.6 Luna leads
GLM-4.6: 23.7 (#172), GPT-5.6 Luna: 47.6 (#43)
| Benchmark | GLM-4.6 | GPT-5.6 Luna |
|---|---|---|
| Kagi LLM Benchmark | 47.4% | 49.1% |
| CritPt | 1.1% | 20.6% |
| LMArena Hard Prompts | 1440 | 1451 |
| ARC-AGI-2 | — | 59.5% |
| SimpleBench | — | 46.8% |
| NYT Connections (extended) | — | 69.4% |
| ARC-AGI-1 | — | 88% |
| Chess Puzzles | — | 40% |
| Mystery Game Puzzles | — | 21% |
| DTBench | — | 89.1% |
| LMCA | — | 48.5% |
| Surface Evolver Bench | — | 61.9% |
| Epoch Capabilities Index | — | 156.39 |
Math GPT-5.6 Luna leads
GLM-4.6: 39.1 (#111), GPT-5.6 Luna: 77.7 (#14)
| Benchmark | GLM-4.6 | GPT-5.6 Luna |
|---|---|---|
| LMArena Math | 1432 | 1458 |
| FrontierMath (Tiers 1-3) | — | 82.1% |
| FrontierMath Tier 4 | — | 61% |
| OTIS Mock AIME 2024-2025 | — | 98.3% |
| ProofBench | — | 60% |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GPT-5.6 Luna leads
GLM-4.6: 40.2 (#124), GPT-5.6 Luna: 58.5 (#34)
| Benchmark | GLM-4.6 | GPT-5.6 Luna |
|---|---|---|
| LMArena Expert | 1431 | 1478 |
| GPQA Diamond | — | 91.6% |
| SimpleQA Verified | — | 41% |
| Vectara Hallucination Rate | 9.5% | — |
Multimodal Not comparable
GLM-4.6: —, GPT-5.6 Luna: 42.7 (#28)
| Benchmark | GLM-4.6 | GPT-5.6 Luna |
|---|---|---|
| LMArena Vision | — | 1258 |
| Blueprint-Bench 2 | — | 22.6% |
| Furniture Assembly | — | 42.5% |
| LMArena Document | — | 1457 |
Multilingual Too close to call
GLM-4.6: 53.5 (#66), GPT-5.6 Luna: 52.8 (#78)
| Benchmark | GLM-4.6 | GPT-5.6 Luna |
|---|---|---|
| LMArena Non-English | 1426 | 1417 |
| LMArena Chinese | 1499 | 1470 |
| LMArena French | 1459 | 1456 |
| LMArena German | 1447 | 1454 |
| LMArena Japanese | 1393 | 1411 |
| LMArena Korean | 1400 | 1415 |
| LMArena Russian | 1419 | 1428 |
| LMArena Spanish | 1436 | 1448 |
Instruction Following GPT-5.6 Luna leads
GLM-4.6: 74.3 (#98), GPT-5.6 Luna: 75.6 (#57)
| Benchmark | GLM-4.6 | GPT-5.6 Luna |
|---|---|---|
| LMArena Instruction Following | 1410 | 1437 |
Long Context Too close to call
GLM-4.6: 43.4 (#94), GPT-5.6 Luna: 43.9 (#82)
| Benchmark | GLM-4.6 | GPT-5.6 Luna |
|---|---|---|
| LMArena Longer Query | 1422 | 1436 |
Writing & Preference GPT-5.6 Luna leads
GLM-4.6: 61.1 (#90), GPT-5.6 Luna: 68.0 (#29)
| Benchmark | GLM-4.6 | GPT-5.6 Luna |
|---|---|---|
| LMArena Text | 1440 | 1431 |
| LMArena Creative Writing | 1411 | 1396 |
| EQ-Bench Creative Writing | 1411 | 1829 |
| LMArena Multi-Turn | 1427 | 1434 |
| EQ-Bench 4 | — | 1156 |
Frequently asked questions
Is GLM-4.6 better than GPT-5.6 Luna?
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 41.4 on the Noometry Index.
Which is cheaper, GLM-4.6 or GPT-5.6 Luna?
GPT-5.6 Luna is cheaper. It lists at $0.20 per million input tokens and $1.20 per million output tokens; GLM-4.6 lists at $0.60 and $2.20.
Is GLM-4.6 or GPT-5.6 Luna better for coding?
GPT-5.6 Luna scores higher on coding benchmarks: 54.5 versus 40.1 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Luna does, with 1.05M tokens against 205K.
How many benchmarks do GLM-4.6 and GPT-5.6 Luna share?
23 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and GPT-5.6 Luna has 52.