Model comparison
GLM-5 vs GPT-6 Luna
GLM-5 and GPT-6 Luna score almost the same on the Noometry Index (45.9 vs 45.1), so choose on price, context window or the category you care about most.
Last verified . 15 shared benchmarks.
Summary
- They share 15 benchmarks with published results for both. GLM-5 scores higher in 6 categories and GPT-6 Luna in 2 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5 leads 52.2 to 40.1.
- The biggest single-benchmark swing is NYT Connections (extended): 74.8% for GLM-5 and 68.7% for GPT-6 Luna.
- GPT-6 Luna is cheaper at $0.10 / $0.50 per million input/output tokens, against $1 / $3.20 for GLM-5.
- GPT-6 Luna 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-6 Luna | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 45.9 | 45.1 |
| Released | 2026-02-11 | 2026-09-22 |
| Weights | Open | Proprietary |
| Context window | 205K | 1.05M |
| Max output | 131K | 128K |
| Input $ / M tokens | $1 | $0.10 |
| Output $ / M tokens | $3.20 | $0.50 |
| Results tracked | 45 | 18 |
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Category by category
Coding GPT-6 Luna leads
GLM-5: 49.0 (#47), GPT-6 Luna: 54.7 (#24)
| Benchmark | GLM-5 | GPT-6 Luna |
|---|---|---|
| LMArena WebDev | 1434 | 1583 |
| LMArena Coding | 1461 | 1448 |
| SWE-bench Verified | 72.1% | — |
| DeepSWE | — | 66.6% |
| FrontierCode | — | 42.4% |
| SWE-bench Verified (bash only) | 72.8% | — |
| SWE-bench Multilingual | 69.7% | — |
| WeirdML | 48.2% | — |
| ALE-Bench | 765.62 | — |
Agentic & Tool Use Not comparable
GLM-5: 30.9 (#65), GPT-6 Luna: —
| Benchmark | GLM-5 | GPT-6 Luna |
|---|---|---|
| Terminal-Bench | 52.4% | — |
| 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 GPT-6 Luna leads
GLM-5: 27.6 (#100), GPT-6 Luna: 31.7 (#68)
| Benchmark | GLM-5 | GPT-6 Luna |
|---|---|---|
| NYT Connections (extended) | 74.8% | 68.7% |
| LMArena Hard Prompts | 1451 | 1415 |
| ARC-AGI-2 | 4.9% | — |
| SimpleBench | 53.2% | — |
| Kagi LLM Benchmark | 75% | — |
| ARC-AGI-1 | 44.7% | — |
| Chess Puzzles | 10% | — |
| Epoch Capabilities Index | 145.84 | — |
| ForecastBench | 61 | — |
Math GLM-5 leads
GLM-5: 44.8 (#66), GPT-6 Luna: 35.6 (#140)
| Benchmark | GLM-5 | GPT-6 Luna |
|---|---|---|
| LMArena Math | 1440 | 1396 |
| MathArena Final-Answer Competitions | 65.7% | — |
| OTIS Mock AIME 2024-2025 | 80% | — |
| FrontierMath (Feb 2025 set) | 16.4% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GLM-5 leads
GLM-5: 52.2 (#59), GPT-6 Luna: 40.1 (#110)
| Benchmark | GLM-5 | GPT-6 Luna |
|---|---|---|
| LMArena Expert | 1454 | 1458 |
| GPQA Diamond | 87.8% | — |
| Vectara Hallucination Rate | 10.1% | — |
Multimodal Not comparable
GLM-5: —, GPT-6 Luna: 42.0 (#27)
| Benchmark | GLM-5 | GPT-6 Luna |
|---|---|---|
| Furniture Assembly | — | 44.2% |
Multilingual GLM-5 leads
GLM-5: 53.9 (#57), GPT-6 Luna: 51.0 (#111)
| Benchmark | GLM-5 | GPT-6 Luna |
|---|---|---|
| LMArena Non-English | 1430 | 1390 |
| LMArena Chinese | 1511 | 1425 |
| LMArena Russian | 1436 | 1392 |
| LMArena Spanish | 1452 | 1416 |
| LMArena French | 1455 | — |
| LMArena German | 1445 | — |
| LMArena Japanese | 1416 | — |
| LMArena Korean | 1421 | — |
Instruction Following Too close to call
GLM-5: 75.4 (#64), GPT-6 Luna: 74.7 (#85)
| Benchmark | GLM-5 | GPT-6 Luna |
|---|---|---|
| LMArena Instruction Following | 1427 | 1414 |
Long Context GLM-5 leads
GLM-5: 44.6 (#58), GPT-6 Luna: 43.3 (#95)
| Benchmark | GLM-5 | GPT-6 Luna |
|---|---|---|
| LMArena Longer Query | 1446 | 1417 |
| CL-bench | 18.7% | — |
Writing & Preference GLM-5 leads
GLM-5: 66.2 (#38), GPT-6 Luna: 58.4 (#115)
| Benchmark | GLM-5 | GPT-6 Luna |
|---|---|---|
| LMArena Text | 1446 | 1392 |
| LMArena Creative Writing | 1438 | 1357 |
| LMArena Multi-Turn | 1456 | 1400 |
| EQ-Bench Creative Writing | 1601 | — |
Frequently asked questions
Is GLM-5 better than GPT-6 Luna?
GLM-5 and GPT-6 Luna score almost the same on the Noometry Index (45.9 vs 45.1), so choose on price, context window or the category you care about most.
Which is cheaper, GLM-5 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-5 lists at $1 and $3.20.
Is GLM-5 or GPT-6 Luna better for coding?
GPT-6 Luna scores higher on coding benchmarks: 54.7 versus 49.0 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-5 and GPT-6 Luna share?
15 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and GPT-6 Luna has 18.