GLM 5.2 vs GPT-5.6 Luna
GLM 5.2 leads GPT-5.6 Luna on coding, 61.1 to 58.0 on the Vibe Coding Index, and GPT-5.6 Luna costs 3.7x less per output token ($1.20 vs $4.40 per million). Updated October 22, 2026; 84 benchmarks have a result for both.
GLM 5.2 or GPT-5.6 Luna: which to pick
Pick GLM 5.2 for coding: it scores 61.1 on the Vibe Coding Index against 58.0 for GPT-5.6 Luna, though on 4 benchmarks against 6.
Pick GPT-5.6 Luna for output tokens 3.7x cheaper ($1.20 vs $4.40 per million) and a lower cost per solved task ($0.04 against $0.86).
- Shared results
- 84
- GLM 5.2 only
- 14
- GPT-5.6 Luna only
- 35
- Like-for-like categories
- 8 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
GLM 5.2
GLM 5.2 scores 61.1 against 58.0.
4 and 6 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.6 Luna
GPT-5.6 Luna leads, 37.3 against 33.7.
Same board, both measured
Agentic work
AA Agentic Index
GPT-5.6 Luna
GPT-5.6 Luna leads, 42.1 against 38.4.
Same board, both measured
Reasoning
GPQA Diamond
GPT-5.6 Luna
GPT-5.6 Luna leads, 91.7% against 85.6%.
Same board, both measured
Knowledge
MMLU Pro
GLM 5.2
GLM 5.2 leads, 86.7% against 86.0%.
Same board, both measured
Web design
Design Arena: websites
GLM 5.2
GLM 5.2 leads, 1285 against 1255.
Same board, both measured
Long documents
Listed context window
GPT-5.6 Luna
GPT-5.6 Luna takes 1.05M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5.6 Luna
GLM 5.2 $0.0036, GPT-5.6 Luna $0.0008 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5.6 Luna
GLM 5.2 $0.86, GPT-5.6 Luna $0.04.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5.2 61.1 from 4 benchmarks, GPT-5.6 Luna 58.0 from 6 benchmarks.
- Widest category
- Math, 16.5 points apart on average over 6 shared benchmarks.
- Widest single gap
- ARC-AGI-2: GLM 5.2 22.8% against GPT-5.6 Luna 59.5%, 36.8 points apart on a 0 to 100 scale.
- Output price
- GLM 5.2 $4.40 against GPT-5.6 Luna $1.20 per million output tokens, 3.7x apart.
- Context window
- GLM 5.2 takes 1.05M tokens, GPT-5.6 Luna 1.05M.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | GLM 5.2 | Bars | GPT-5.6 Luna | Ties |
|---|---|---|---|---|
| Coding | 7 | 9 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 2 | 5 | ||
| Reasoning | 7 | 6 | ||
| Math | 2 | 4 | ||
| Knowledge | 7 | 3 | ||
| Web design | 8 | 0 | ||
| General | 18 | 4 | ||
| All shared boards | 51 | 33 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index79
| Benchmark | GLM 5.2 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 73.6% | Only GPT-5.6 Luna measured |
| LMArena WebDev | 1604 | 1518 | GLM 5.2 |
| AA Coding Index | 68.8 | 71.4 | GPT-5.6 Luna |
| LiveCodeBench (Vals AI) | 69.5% | Not measured | Only GLM 5.2 measured |
| SWE-bench Verified (Vals AI) | 82.8% | 93.0% | GPT-5.6 Luna |
| Terminal-Bench 4 (Vals AI) | Not measured | 11.6% | Only GPT-5.6 Luna measured |
| Vibe Code Bench (Vals AI) | 64.0% | 77.1% | GPT-5.6 Luna |
| Code migration (Vals AI) | 37.9% | 44.5% | GPT-5.6 Luna |
| ProgramBench (Vals AI) | 0.5% | 0.0% | GLM 5.2 |
| IOI (Vals AI) | Not measured | 61.8% | Only GPT-5.6 Luna measured |
| Terminal-Bench 2.1 (Vals AI) | 67.8% | 79.0% | GPT-5.6 Luna |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 22.6% | Only GPT-5.6 Luna measured |
| SkillsBench (Vals AI) | 45.1% | 60.5% | GPT-5.6 Luna |
| CyberBench (Vals AI) | Not measured | 73.6% | Only GPT-5.6 Luna measured |
| SciCode | 51.2% | 53.6% | GPT-5.6 Luna |
| Terminal-Bench Hard (Artificial Analysis run) | 50.8% | Not measured | Only GLM 5.2 measured |
| FrontierCode | 24.5% | 39.8% | GPT-5.6 Luna |
| WeirdML | 70.1% | 60.9% | GLM 5.2 |
| ALE-Bench | 1047.0 | 1667.4 | GPT-5.6 Luna |
| LMArena Text: coding | 1487 | 1466 | GLM 5.2 |
| LMArena Text: software and IT | 1486 | 1463 | GLM 5.2 |
| LMArena WebDev: React | 1617 | 1508 | GLM 5.2 |
| LMArena WebDev: HTML | 1565 | 1520 | GLM 5.2 |
| LMArena WebDev: from an image | Not measured | 1517 | Only GPT-5.6 Luna measured |
| SciCode | Not measured | 6.2% | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 14 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 5.2 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| AA Intelligence Index | 33.7 | 37.3 | GPT-5.6 Luna |
| Epoch Capabilities Index | 151.9 | 156.5 | GPT-5.6 Luna |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index25
| Benchmark | GLM 5.2 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| AA Agentic Index | 38.4 | 42.1 | GPT-5.6 Luna |
| Design Arena: agents building web apps | 1222 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building full-stack apps | 1241 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building mobile apps | 1159 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building Android apps | 1178 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building Godot games | 1142 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building games | 1206 | Not measured | Only GLM 5.2 measured |
| Design Arena: HTML slides | 1125 | Not measured | Only GLM 5.2 measured |
| Design Arena: PowerPoint slides | 1170 | Not measured | Only GLM 5.2 measured |
| LiveBench research | Not measured | 51.0% | Only GPT-5.6 Luna measured |
| LiveBench browser | Not measured | 68.0% | Only GPT-5.6 Luna measured |
| LiveBench long agentic | Not measured | 30.4% | Only GPT-5.6 Luna measured |
| SREBench (Vals AI) | 0.0% | 1.5% | GPT-5.6 Luna |
| Finance Agent (Vals AI) | 49.7% | 55.0% | GPT-5.6 Luna |
| Tax Agent Bench (Vals AI) | Not measured | 24.8% | Only GPT-5.6 Luna measured |
| Legal Research Bench (Vals AI) | 31.3% | 36.5% | GPT-5.6 Luna |
| Excel Modeling Benchmark (Vals AI) | 61.5% | 67.1% | GPT-5.6 Luna |
| BioMysteryBench (Vals AI) | Not measured | 61.5% | Only GPT-5.6 Luna measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 0.0% | Only GPT-5.6 Luna measured |
| Vals Science Index | Not measured | 25.3% | Only GPT-5.6 Luna measured |
| τ²-Bench Telecom | 99.1% | Not measured | Only GLM 5.2 measured |
| APEX-Agents | 45.2% | 43.0% | GLM 5.2 |
| BALROG | Not measured | 45.6% | Only GPT-5.6 Luna measured |
| PostTrainBench | 31.7% | Not measured | Only GLM 5.2 measured |
| Vending-Bench 2 | $8,314 | $4,095 | GLM 5.2 |
| Neither model has a result on the other 16 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index76
| Benchmark | GLM 5.2 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| GPQA Diamond | 85.6% | 91.7% | GPT-5.6 Luna |
| LiveBench reasoning | Not measured | 85.6% | Only GPT-5.6 Luna measured |
| MysteryMechanism (Vals AI) | Not measured | 14.4% | Only GPT-5.6 Luna measured |
| MMMU Pro (Vals AI) | Not measured | 85.0% | Only GPT-5.6 Luna measured |
| GPQA Diamond (Artificial Analysis run) | 89.5% | 91.1% | GPT-5.6 Luna |
| Humanity's Last Exam (Artificial Analysis run) | 41.1% | 39.5% | GLM 5.2 |
| GPQA Diamond (Epoch AI run) | 91.9% | 91.6% | GLM 5.2 |
| ARC-AGI | 77.0% | 88.0% | GPT-5.6 Luna |
| ARC-AGI-2 | 22.8% | 59.5% | GPT-5.6 Luna |
| SimpleBench | 58.8% | 46.8% | GLM 5.2 |
| Chess puzzles | 22.0% | 43.0% | GPT-5.6 Luna |
| Mystery game puzzles | 19.0% | 21.0% | GPT-5.6 Luna |
| EBR-bench | 9.5% | Not measured | Only GLM 5.2 measured |
| LMArena Text: hard prompts | 1481 | 1451 | GLM 5.2 |
| LMArena Text: expert | 1487 | 1478 | GLM 5.2 |
| LMArena Text: hard prompts in English | 1481 | 1460 | GLM 5.2 |
| LMArena Vision: diagrams | Not measured | 1273 | Only GPT-5.6 Luna measured |
| LMArena Vision: homework | Not measured | 1282 | Only GPT-5.6 Luna measured |
| Kagi LLM Benchmark | 62.6% | 49.1% | GLM 5.2 |
| Neither model has a result on the other 3 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index24
| Benchmark | GLM 5.2 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| OTIS Mock AIME | 86.4% | 98.3% | GPT-5.6 Luna |
| FrontierMath, tiers 1 to 3 | 59.2% | 82.1% | GPT-5.6 Luna |
| FrontierMath, tier 4 | 29.3% | 61.0% | GPT-5.6 Luna |
| ProofBench | 35.0% | 60.0% | GPT-5.6 Luna |
| LMArena Text: math | 1483 | 1459 | GLM 5.2 |
| LMArena Text: mathematical work | 1489 | 1470 | GLM 5.2 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index73
| Benchmark | GLM 5.2 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| MMLU Pro | 86.7% | 86.0% | GLM 5.2 |
| LegalBench (Vals AI) | 84.1% | 84.0% | GLM 5.2 |
| CorpFin (Vals AI) | 66.1% | 64.2% | GLM 5.2 |
| TaxEval (Vals AI) | 73.3% | 76.2% | GPT-5.6 Luna |
| MortgageTax (Vals AI) | Not measured | 67.3% | Only GPT-5.6 Luna measured |
| MedCode (Vals AI) | 40.8% | 42.4% | GPT-5.6 Luna |
| SimpleQA Verified | 34.2% | 41.0% | GPT-5.6 Luna |
| LMArena Text: science | 1489 | 1433 | GLM 5.2 |
| LMArena Text: medicine and healthcare | 1475 | 1399 | GLM 5.2 |
| LMArena Text: legal and government | 1486 | 1446 | GLM 5.2 |
| LMArena Text: business and finance | 1451 | 1439 | GLM 5.2 |
| FACTS Benchmark Suite | Not measured | 35.0% | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | GLM 5.2 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| Design Arena: websites | 1285 | 1255 | GLM 5.2 |
| Design Arena: UI components | 1294 | 1248 | GLM 5.2 |
| Design Arena: game dev | 1268 | 1194 | GLM 5.2 |
| Design Arena: data viz | 1285 | 1211 | GLM 5.2 |
| Design Arena: 3D | 1287 | 1121 | GLM 5.2 |
| Design Arena: all code categories | 1287 | 1231 | GLM 5.2 |
| Design Arena: SVG | 1213 | 1194 | GLM 5.2 |
| Design Arena: ASCII art | 1191 | 1142 | GLM 5.2 |
| LiveBench design | Not measured | 76.0% | Only GPT-5.6 Luna measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index184
| Benchmark | GLM 5.2 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 78.0% | Only GPT-5.6 Luna measured |
| Public Benefits Bench (Vals AI) | Not measured | 61.2% | Only GPT-5.6 Luna measured |
| SAGE (Vals AI) | Not measured | 44.2% | Only GPT-5.6 Luna measured |
| MedScribe (Vals AI) | 83.5% | 84.4% | GPT-5.6 Luna |
| Vals Multimodal Index | Not measured | 69.1% | Only GPT-5.6 Luna measured |
| IFBench | 73.3% | Not measured | Only GLM 5.2 measured |
| Long context reasoning (AA-LCR) | 78.3% | 83.7% | GPT-5.6 Luna |
| DTBench | 93.6% | 89.1% | GLM 5.2 |
| LMCA | 45.8% | 48.5% | GPT-5.6 Luna |
| Surface Evolver Bench | 55.6% | 61.9% | GPT-5.6 Luna |
| Furniture assembly | Not measured | 42.5% | Only GPT-5.6 Luna measured |
| LMArena Text | 1470 | 1431 | GLM 5.2 |
| LMArena Text: instruction following | 1466 | 1437 | GLM 5.2 |
| LMArena Text: creative writing | 1463 | 1396 | GLM 5.2 |
| LMArena Vision | Not measured | 1257 | Only GPT-5.6 Luna measured |
| LMArena Text: English | 1475 | 1441 | GLM 5.2 |
| LMArena Text: non-English | 1459 | 1417 | GLM 5.2 |
| LMArena Text: Chinese | 1522 | 1470 | GLM 5.2 |
| LMArena Text: German | 1470 | 1455 | GLM 5.2 |
| LMArena Text: Japanese | 1450 | 1415 | GLM 5.2 |
| LMArena Text: Korean | 1440 | 1415 | GLM 5.2 |
| LMArena Text: French | 1479 | 1458 | GLM 5.2 |
| LMArena Text: Spanish | 1478 | 1446 | GLM 5.2 |
| LMArena Text: Russian | 1468 | 1427 | GLM 5.2 |
| LMArena Text: Polish | 1456 | 1405 | GLM 5.2 |
| LMArena Text: multi-turn | 1469 | 1434 | GLM 5.2 |
| LMArena Text: longer queries | 1480 | 1436 | GLM 5.2 |
| LMArena Text: writing and language | 1463 | 1419 | GLM 5.2 |
| LMArena Text: entertainment and media | 1450 | 1402 | GLM 5.2 |
| LMArena Vision: English | Not measured | 1263 | Only GPT-5.6 Luna measured |
| LMArena Vision: Chinese | Not measured | 1294 | Only GPT-5.6 Luna measured |
| LMArena Vision: reading text | Not measured | 1264 | Only GPT-5.6 Luna measured |
| LMArena Vision: humor | Not measured | 1190 | Only GPT-5.6 Luna measured |
| LMArena Vision: creative writing | Not measured | 1257 | Only GPT-5.6 Luna measured |
| LMArena Document | Not measured | 1457 | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 11 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.2 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
GPT-5.6 Luna costs less
Repository review
50K input + 3K output tokens
GPT-5.6 Luna costs less
Agent loop
200K input + 10K output tokens
GPT-5.6 Luna costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Z.ai GLM 5.2 | OpenAI GPT-5.6 Luna |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | June 16, 2026 | July 9, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $1.40 | $0.20 |
| Output $/Mtok | $4.40 | $1.20 |
| Coding index | 61.1 | 58.0 |
| Independent measurements behind it | 4 | 6 |
| Cost per solved task | $0.86 | $0.04 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.2 better than GPT-5.6 Luna for coding?
Pick GLM 5.2 for coding: it scores 61.1 on the Vibe Coding Index against 58.0 for GPT-5.6 Luna, though on 4 benchmarks against 6. Pick GPT-5.6 Luna for output tokens 3.7x cheaper ($1.20 vs $4.40 per million) and a lower cost per solved task ($0.04 against $0.86).
Which is cheaper, GLM 5.2 or GPT-5.6 Luna?
GLM 5.2 costs $1.40 per million input tokens and $4.40 per million output tokens; GPT-5.6 Luna costs $0.20 and $1.20. Per solved SWE-bench Verified task, GLM 5.2 costs $0.86 and GPT-5.6 Luna $0.04.
Which has the bigger context window, GLM 5.2 or GPT-5.6 Luna?
GPT-5.6 Luna, with 1.05M tokens against 1.05M.
How many benchmarks is the GLM 5.2 vs GPT-5.6 Luna comparison based on?
84 benchmarks have a result for both models as of October 22, 2026. GLM 5.2's coding index rests on 4 independent benchmarks and GPT-5.6 Luna's on 6. Where only one model has a result, the page says so instead of naming a winner.