Model comparison
GLM-4.7-Flash vs GPT-5.6 Luna
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 3.1× less per token, which makes it the better buy when GPT-5.6 Luna's lead doesn't matter for your workload.
Last verified . 20 shared benchmarks.
Summary
- They share 20 benchmarks with published results for both. GLM-4.7-Flash scores higher in 0 categories and GPT-5.6 Luna in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Luna leads 77.7 to 36.1.
- The biggest single-benchmark swing is Chess Puzzles: 0% for GLM-4.7-Flash and 40% for GPT-5.6 Luna.
- GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $0.20 / $1.20 for GPT-5.6 Luna.
- GPT-5.6 Luna accepts more context: 1.05M tokens versus 200K.
- GLM-4.7-Flash has downloadable open weights; the other is API-only.
Side by side
| GLM-4.7-Flash | GPT-5.6 Luna | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 38.8 | 54.6 |
| Released | 2026-01-19 | 2026-07-09 |
| Weights | Open | Proprietary |
| Context window | 200K | 1.05M |
| Max output | 131K | 128K |
| Input $ / M tokens | $0.06 | $0.20 |
| Output $ / M tokens | $0.40 | $1.20 |
| Results tracked | 21 | 52 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GPT-5.6 Luna leads
GLM-4.7-Flash: 40.6 (#135), GPT-5.6 Luna: 54.5 (#28)
| Benchmark | GLM-4.7-Flash | GPT-5.6 Luna |
|---|---|---|
| LMArena Coding | 1383 | 1466 |
| DeepSWE | — | 67.2% |
| FrontierCode | — | 39.8% |
| CursorBench | — | 35.9% |
| LMArena WebDev | — | 1519 |
| SciCode | — | 53.6% |
| WeirdML | — | 60.9% |
| ALE-Bench | — | 1,667 |
Agentic & Tool Use Not comparable
GLM-4.7-Flash: —, GPT-5.6 Luna: 34.4 (#45)
| Benchmark | GLM-4.7-Flash | GPT-5.6 Luna |
|---|---|---|
| APEX-Agents | — | 43% |
| BALROG | — | 45.6% |
| GDP.pdf | — | 22.7% |
| Vending-Bench 2 | — | 4,095 |
Reasoning GPT-5.6 Luna leads
GLM-4.7-Flash: 20.9 (#229), GPT-5.6 Luna: 47.6 (#43)
| Benchmark | GLM-4.7-Flash | GPT-5.6 Luna |
|---|---|---|
| Chess Puzzles | 0% | 40% |
| LMArena Hard Prompts | 1356 | 1451 |
| ARC-AGI-2 | — | 59.5% |
| SimpleBench | — | 46.8% |
| Kagi LLM Benchmark | — | 49.1% |
| NYT Connections (extended) | — | 69.4% |
| ARC-AGI-1 | — | 88% |
| CritPt | — | 20.6% |
| 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-4.7-Flash: 36.1 (#173), GPT-5.6 Luna: 77.7 (#14)
| Benchmark | GLM-4.7-Flash | GPT-5.6 Luna |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 58.3% | 98.3% |
| LMArena Math | 1355 | 1458 |
| FrontierMath (Tiers 1-3) | — | 82.1% |
| FrontierMath Tier 4 | — | 61% |
| ProofBench | — | 60% |
Knowledge GPT-5.6 Luna leads
GLM-4.7-Flash: 35.5 (#184), GPT-5.6 Luna: 58.5 (#34)
| Benchmark | GLM-4.7-Flash | GPT-5.6 Luna |
|---|---|---|
| GPQA Diamond | 60.5% | 91.6% |
| LMArena Expert | 1357 | 1478 |
| SimpleQA Verified | — | 41% |
| Vectara Hallucination Rate | 9.3% | — |
Multimodal Not comparable
GLM-4.7-Flash: —, GPT-5.6 Luna: 42.7 (#28)
| Benchmark | GLM-4.7-Flash | GPT-5.6 Luna |
|---|---|---|
| LMArena Vision | — | 1258 |
| Blueprint-Bench 2 | — | 22.6% |
| Furniture Assembly | — | 42.5% |
| LMArena Document | — | 1457 |
Multilingual GPT-5.6 Luna leads
GLM-4.7-Flash: 46.5 (#158), GPT-5.6 Luna: 52.8 (#78)
| Benchmark | GLM-4.7-Flash | GPT-5.6 Luna |
|---|---|---|
| LMArena Non-English | 1330 | 1417 |
| LMArena Chinese | 1403 | 1470 |
| LMArena French | 1332 | 1456 |
| LMArena German | 1337 | 1454 |
| LMArena Korean | 1283 | 1415 |
| LMArena Russian | 1332 | 1428 |
| LMArena Spanish | 1350 | 1448 |
| LMArena Japanese | — | 1411 |
Instruction Following GPT-5.6 Luna leads
GLM-4.7-Flash: 70.1 (#167), GPT-5.6 Luna: 75.6 (#57)
| Benchmark | GLM-4.7-Flash | GPT-5.6 Luna |
|---|---|---|
| LMArena Instruction Following | 1327 | 1437 |
Long Context GPT-5.6 Luna leads
GLM-4.7-Flash: 40.9 (#148), GPT-5.6 Luna: 43.9 (#82)
| Benchmark | GLM-4.7-Flash | GPT-5.6 Luna |
|---|---|---|
| LMArena Longer Query | 1345 | 1436 |
Writing & Preference GPT-5.6 Luna leads
GLM-4.7-Flash: 47.4 (#210), GPT-5.6 Luna: 68.0 (#29)
| Benchmark | GLM-4.7-Flash | GPT-5.6 Luna |
|---|---|---|
| LMArena Text | 1351 | 1431 |
| LMArena Creative Writing | 1297 | 1396 |
| EQ-Bench Creative Writing | 1125 | 1829 |
| LMArena Multi-Turn | 1342 | 1434 |
| EQ-Bench 4 | — | 1156 |
Frequently asked questions
Is GLM-4.7-Flash better than GPT-5.6 Luna?
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 3.1× less per token, which makes it the better buy when GPT-5.6 Luna's lead doesn't matter for your workload.
Which is cheaper, GLM-4.7-Flash or GPT-5.6 Luna?
GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; GPT-5.6 Luna lists at $0.20 and $1.20.
Is GLM-4.7-Flash or GPT-5.6 Luna better for coding?
GPT-5.6 Luna scores higher on coding benchmarks: 54.5 versus 40.6 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Luna does, with 1.05M tokens against 200K.
How many benchmarks do GLM-4.7-Flash and GPT-5.6 Luna share?
20 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and GPT-5.6 Luna has 52.