Model comparison
GLM-4.6 vs GPT-6 Luna
GPT-6 Luna is the stronger model overall, scoring 53.3 to 41.4 on the Noometry Index.
Last verified . 21 shared benchmarks.
Summary
- They share 21 benchmarks with published results for both. GLM-4.6 scores higher in 4 categories and GPT-6 Luna in 5 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6 Luna leads 76.1 to 39.1.
- The biggest single-benchmark swing is CritPt: 1.1% for GLM-4.6 and 19.4% for GPT-6 Luna.
- GPT-6 Luna is cheaper at $0.10 / $0.50 per million input/output tokens, against $0.60 / $2.20 for GLM-4.6.
- GPT-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-6 Luna | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 41.4 | 53.3 |
| Released | 2025-09-30 | 2026-09-22 |
| Weights | Open | Proprietary |
| Context window | 205K | 1.05M |
| Max output | 131K | 128K |
| Input $ / M tokens | $0.60 | $0.10 |
| Output $ / M tokens | $2.20 | $0.50 |
| Results tracked | 29 | 42 |
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Category by category
Coding GPT-6 Luna leads
GLM-4.6: 40.1 (#148), GPT-6 Luna: 55.5 (#25)
| Benchmark | GLM-4.6 | GPT-6 Luna |
|---|---|---|
| LMArena WebDev | 1340 | 1581 |
| SciCode | 38.4% | 54.6% |
| LMArena Coding | 1449 | 1439 |
| ALE-Bench | 340.82 | 1,577 |
| DeepSWE | — | 66.6% |
| FrontierCode | — | 42.4% |
| SWE-bench Verified (bash only) | 55.4% | — |
Agentic & Tool Use GPT-6 Luna leads
GLM-4.6: 32.3 (#66), GPT-6 Luna: 33.3 (#54)
| Benchmark | GLM-4.6 | GPT-6 Luna |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| APEX-Agents | — | 44.3% |
| Berkeley Function Calling Leaderboard | 72.4% | — |
| GDP.pdf | — | 23% |
Reasoning GPT-6 Luna leads
GLM-4.6: 23.7 (#172), GPT-6 Luna: 48.2 (#41)
| Benchmark | GLM-4.6 | GPT-6 Luna |
|---|---|---|
| CritPt | 1.1% | 19.4% |
| LMArena Hard Prompts | 1440 | 1411 |
| ARC-AGI-2 | — | 59.3% |
| Kagi LLM Benchmark | 47.4% | — |
| NYT Connections (extended) | — | 68.7% |
| ARC-AGI-1 | — | 86.7% |
| Chess Puzzles | — | 31% |
| Mystery Game Puzzles | — | 7% |
| DTBench | — | 90.1% |
| LMCA | — | 44.5% |
| Epoch Capabilities Index | — | 156.28 |
Math GPT-6 Luna leads
GLM-4.6: 39.1 (#111), GPT-6 Luna: 76.1 (#15)
| Benchmark | GLM-4.6 | GPT-6 Luna |
|---|---|---|
| LMArena Math | 1432 | 1416 |
| FrontierMath (Tiers 1-3) | — | 78.9% |
| FrontierMath Tier 4 | — | 56.1% |
| OTIS Mock AIME 2024-2025 | — | 98.9% |
| ProofBench | — | 64% |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GPT-6 Luna leads
GLM-4.6: 40.2 (#124), GPT-6 Luna: 57.0 (#41)
| Benchmark | GLM-4.6 | GPT-6 Luna |
|---|---|---|
| LMArena Expert | 1431 | 1444 |
| GPQA Diamond | — | 90.5% |
| SimpleQA Verified | — | 41.4% |
| Vectara Hallucination Rate | 9.5% | — |
Multimodal Not comparable
GLM-4.6: —, GPT-6 Luna: 42.4 (#30)
| Benchmark | GLM-4.6 | GPT-6 Luna |
|---|---|---|
| LMArena Vision | — | 1217 |
| Blueprint-Bench 2 | — | 31.2% |
| Furniture Assembly | — | 44.2% |
Multilingual GLM-4.6 leads
GLM-4.6: 53.5 (#66), GPT-6 Luna: 50.5 (#117)
| Benchmark | GLM-4.6 | GPT-6 Luna |
|---|---|---|
| LMArena Non-English | 1426 | 1386 |
| LMArena Chinese | 1499 | 1433 |
| LMArena French | 1459 | 1420 |
| LMArena German | 1447 | 1369 |
| LMArena Japanese | 1393 | 1369 |
| LMArena Korean | 1400 | 1360 |
| LMArena Russian | 1419 | 1394 |
| LMArena Spanish | 1436 | 1393 |
Instruction Following Too close to call
GLM-4.6: 74.3 (#98), GPT-6 Luna: 74.3 (#99)
| Benchmark | GLM-4.6 | GPT-6 Luna |
|---|---|---|
| LMArena Instruction Following | 1410 | 1409 |
Long Context Too close to call
GLM-4.6: 43.4 (#94), GPT-6 Luna: 43.0 (#111)
| Benchmark | GLM-4.6 | GPT-6 Luna |
|---|---|---|
| LMArena Longer Query | 1422 | 1409 |
Writing & Preference GLM-4.6 leads
GLM-4.6: 61.1 (#90), GPT-6 Luna: 58.3 (#119)
| Benchmark | GLM-4.6 | GPT-6 Luna |
|---|---|---|
| LMArena Text | 1440 | 1391 |
| LMArena Creative Writing | 1411 | 1363 |
| LMArena Multi-Turn | 1427 | 1396 |
| EQ-Bench Creative Writing | 1411 | — |
Frequently asked questions
Is GLM-4.6 better than GPT-6 Luna?
GPT-6 Luna is the stronger model overall, scoring 53.3 to 41.4 on the Noometry Index.
Which is cheaper, GLM-4.6 or GPT-6 Luna?
GPT-6 Luna is cheaper. It lists at $0.10 per million input tokens and $0.50 per million output tokens; GLM-4.6 lists at $0.60 and $2.20.
Is GLM-4.6 or GPT-6 Luna better for coding?
GPT-6 Luna scores higher on coding benchmarks: 55.5 versus 40.1 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Luna does, with 1.05M tokens against 205K.
How many benchmarks do GLM-4.6 and GPT-6 Luna share?
21 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and GPT-6 Luna has 42.