Claude Opus 4.8 vs GPT-5.6 Luna
Claude Opus 4.8 leads GPT-5.6 Luna on coding, 65.0 to 58.0 on the Vibe Coding Index, and GPT-5.6 Luna costs 20.8x less per output token ($1.20 vs $25.00 per million). Updated October 22, 2026; 110 benchmarks have a result for both.
Claude Opus 4.8 or GPT-5.6 Luna: which to pick
Pick Claude Opus 4.8 for coding: it scores 65.0 on the Vibe Coding Index against 58.0 for GPT-5.6 Luna.
Pick GPT-5.6 Luna for output tokens 20.8x cheaper ($1.20 vs $25.00 per million) and a lower cost per solved task ($0.04 against $2.17).
- Shared results
- 110
- Claude Opus 4.8 only
- 26
- GPT-5.6 Luna only
- 10
- 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
Claude Opus 4.8
Claude Opus 4.8 scores 65.0 against 58.0.
6 benchmarks behind each score
Intelligence
AA Intelligence Index
Claude Opus 4.8
Claude Opus 4.8 leads, 41.8 against 37.3.
Same board, both measured
Agentic work
AA Agentic Index
GPT-5.6 Luna
GPT-5.6 Luna leads, 42.1 against 41.9.
Same board, both measured
Reasoning
GPQA Diamond
Claude Opus 4.8
Claude Opus 4.8 leads, 92.4% against 91.7%.
Same board, both measured
Knowledge
MMLU Pro
Claude Opus 4.8
Claude Opus 4.8 leads, 89.6% against 86.0%.
Same board, both measured
Web design
Design Arena: websites
Claude Opus 4.8
Claude Opus 4.8 leads, 1258 against 1255.
Same board, both measured
Long documents
Listed context window
GPT-5.6 Luna
GPT-5.6 Luna takes 1.05M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5.6 Luna
Claude Opus 4.8 $0.018, GPT-5.6 Luna $0.0008 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5.6 Luna
Claude Opus 4.8 $2.17, GPT-5.6 Luna $0.04.
Published attempt cost and pass rate
At a glance
- Coding index
- Claude Opus 4.8 65.0 from 6 benchmarks, GPT-5.6 Luna 58.0 from 6 benchmarks.
- Widest category
- Agentic work, 10.3 points apart on average over 11 shared benchmarks.
- Widest single gap
- Kagi LLM Benchmark: Claude Opus 4.8 88.8% against GPT-5.6 Luna 49.1%, 39.7 points apart on a 0 to 100 scale.
- Output price
- Claude Opus 4.8 $25.00 against GPT-5.6 Luna $1.20 per million output tokens, 20.8x apart.
- Context window
- Claude Opus 4.8 takes 1M 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 | Claude Opus 4.8 | Bars | GPT-5.6 Luna | Ties |
|---|---|---|---|---|
| Coding | 15 | 3 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 9 | 2 | ||
| Reasoning | 16 | 1 | ||
| Math | 3 | 2 | 1 tie | |
| Knowledge | 10 | 2 | ||
| Web design | 9 | 0 | ||
| General | 32 | 2 | 1 tie | |
| All shared boards | 96 | 12 | 2 ties |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index153
| Benchmark | Claude Opus 4.8 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| LiveBench coding | 77.3% | 73.6% | Claude Opus 4.8 |
| LMArena WebDev | 1556 | 1518 | Claude Opus 4.8 |
| AA Coding Index | 74.3 | 71.4 | Claude Opus 4.8 |
| LiveCodeBench (Vals AI) | 87.8% | Not measured | Only Claude Opus 4.8 measured |
| SWE-bench Verified (Vals AI) | 88.6% | 93.0% | GPT-5.6 Luna |
| Terminal-Bench 4 (Vals AI) | 23.2% | 11.6% | Claude Opus 4.8 |
| Vibe Code Bench (Vals AI) | 82.7% | 77.1% | Claude Opus 4.8 |
| Code migration (Vals AI) | 47.3% | 44.5% | Claude Opus 4.8 |
| ProgramBench (Vals AI) | 1.0% | 0.0% | Claude Opus 4.8 |
| IOI (Vals AI) | Not measured | 61.8% | Only GPT-5.6 Luna measured |
| Terminal-Bench 2.1 (Vals AI) | 71.9% | 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) | 59.2% | 60.5% | GPT-5.6 Luna |
| CyberBench (Vals AI) | Not measured | 73.6% | Only GPT-5.6 Luna measured |
| SciCode | 54.4% | 53.6% | Claude Opus 4.8 |
| Terminal-Bench Hard (Artificial Analysis run) | 58.3% | Not measured | Only Claude Opus 4.8 measured |
| FrontierCode | 46.5% | 39.8% | Claude Opus 4.8 |
| GSO-Bench | 47.1% | Not measured | Only Claude Opus 4.8 measured |
| WeirdML | 82.9% | 60.9% | Claude Opus 4.8 |
| ALE-Bench | Not measured | 1667.4 | Only GPT-5.6 Luna measured |
| LMArena Text: coding | 1490 | 1466 | Claude Opus 4.8 |
| LMArena Text: software and IT | 1488 | 1463 | Claude Opus 4.8 |
| LMArena WebDev: React | 1557 | 1508 | Claude Opus 4.8 |
| LMArena WebDev: HTML | 1543 | 1520 | Claude Opus 4.8 |
| LMArena WebDev: from an image | Not measured | 1517 | Only GPT-5.6 Luna measured |
| SciCode | 12.3% | 6.2% | Claude Opus 4.8 |
| SWE Atlas: Codebase QnA | 57.3% | Not measured | Only Claude Opus 4.8 measured |
| SWE Atlas: Refactoring | 46.7% | Not measured | Only Claude Opus 4.8 measured |
| SWE Atlas: Test Writing | 49.6% | Not measured | Only Claude Opus 4.8 measured |
| Neither model has a result on the other 14 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Claude Opus 4.8 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| AA Intelligence Index | 41.8 | 37.3 | Claude Opus 4.8 |
| Epoch Capabilities Index | 159.0 | 156.5 | Claude Opus 4.8 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index92
| Benchmark | Claude Opus 4.8 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| AA Agentic Index | 41.9 | 42.1 | GPT-5.6 Luna |
| Design Arena: agents building web apps | 1233 | Not measured | Only Claude Opus 4.8 measured |
| Design Arena: agents building full-stack apps | 1249 | Not measured | Only Claude Opus 4.8 measured |
| Design Arena: agents building mobile apps | 1222 | Not measured | Only Claude Opus 4.8 measured |
| Design Arena: agents building Android apps | 1231 | Not measured | Only Claude Opus 4.8 measured |
| Design Arena: agents building Godot games | 1231 | Not measured | Only Claude Opus 4.8 measured |
| Design Arena: agents building games | 1238 | Not measured | Only Claude Opus 4.8 measured |
| Design Arena: HTML slides | 1160 | Not measured | Only Claude Opus 4.8 measured |
| Design Arena: PowerPoint slides | 1201 | Not measured | Only Claude Opus 4.8 measured |
| Design Arena: spreadsheets | 1314 | Not measured | Only Claude Opus 4.8 measured |
| LiveBench research | 81.8% | 51.0% | Claude Opus 4.8 |
| LiveBench browser | 96.0% | 68.0% | Claude Opus 4.8 |
| LiveBench long agentic | 48.3% | 30.4% | Claude Opus 4.8 |
| SREBench (Vals AI) | Not measured | 1.5% | Only GPT-5.6 Luna measured |
| Finance Agent (Vals AI) | 53.9% | 55.0% | GPT-5.6 Luna |
| Tax Agent Bench (Vals AI) | 30.0% | 24.8% | Claude Opus 4.8 |
| Legal Research Bench (Vals AI) | 43.8% | 36.5% | Claude Opus 4.8 |
| Excel Modeling Benchmark (Vals AI) | 69.4% | 67.1% | Claude Opus 4.8 |
| BioMysteryBench (Vals AI) | Not measured | 61.5% | Only GPT-5.6 Luna measured |
| Terminal-Bench-Science (Vals AI) | 4.3% | 0.0% | Claude Opus 4.8 |
| Vals Science Index | Not measured | 25.3% | Only GPT-5.6 Luna measured |
| Time Horizon Index (Vals AI) | 11.8% | Not measured | Only Claude Opus 4.8 measured |
| τ²-Bench Telecom | 94.4% | Not measured | Only Claude Opus 4.8 measured |
| APEX-Agents | 48.9% | 43.0% | Claude Opus 4.8 |
| Remote Labor Index | 8.3% | Not measured | Only Claude Opus 4.8 measured |
| OSWorld 2.0 | 20.6% | Not measured | Only Claude Opus 4.8 measured |
| BALROG | Not measured | 45.6% | Only GPT-5.6 Luna measured |
| DeepResearch Bench | 50.2% | Not measured | Only Claude Opus 4.8 measured |
| PostTrainBench | 33.8% | Not measured | Only Claude Opus 4.8 measured |
| Vending-Bench 2 | $5,787 | $4,095 | Claude Opus 4.8 |
| LMArena Search | 1204 | Not measured | Only Claude Opus 4.8 measured |
| MCP Atlas | 82.2% | Not measured | Only Claude Opus 4.8 measured |
| Neither model has a result on the other 10 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index161
| Benchmark | Claude Opus 4.8 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| GPQA Diamond | 92.4% | 91.7% | Claude Opus 4.8 |
| LiveBench reasoning | 89.2% | 85.6% | Claude Opus 4.8 |
| MysteryMechanism (Vals AI) | Not measured | 14.4% | Only GPT-5.6 Luna measured |
| MMMU Pro (Vals AI) | 86.6% | 85.0% | Claude Opus 4.8 |
| GPQA Diamond (Artificial Analysis run) | 92.0% | 91.1% | Claude Opus 4.8 |
| Humanity's Last Exam (Artificial Analysis run) | 48.7% | 39.5% | Claude Opus 4.8 |
| GPQA Diamond (Epoch AI run) | 91.0% | 91.6% | GPT-5.6 Luna |
| ARC-AGI | 92.5% | 88.0% | Claude Opus 4.8 |
| ARC-AGI-2 | 72.1% | 59.5% | Claude Opus 4.8 |
| SimpleBench | 64.8% | 46.8% | Claude Opus 4.8 |
| Chess puzzles | 52.0% | 43.0% | Claude Opus 4.8 |
| Mystery game puzzles | 36.0% | 21.0% | Claude Opus 4.8 |
| EBR-bench | 28.6% | Not measured | Only Claude Opus 4.8 measured |
| LMArena Text: hard prompts | 1482 | 1451 | Claude Opus 4.8 |
| LMArena Text: expert | 1503 | 1478 | Claude Opus 4.8 |
| LMArena Text: hard prompts in English | 1487 | 1460 | Claude Opus 4.8 |
| LMArena Vision: diagrams | 1315 | 1273 | Claude Opus 4.8 |
| LMArena Vision: homework | 1337 | 1282 | Claude Opus 4.8 |
| Kagi LLM Benchmark | 88.8% | 49.1% | Claude Opus 4.8 |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index32
| Benchmark | Claude Opus 4.8 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| OTIS Mock AIME | 98.3% | 98.3% | Tie |
| FrontierMath, tiers 1 to 3 | 80.0% | 82.1% | GPT-5.6 Luna |
| FrontierMath, tier 4 | 56.1% | 61.0% | GPT-5.6 Luna |
| ProofBench | 69.0% | 60.0% | Claude Opus 4.8 |
| LMArena Text: math | 1486 | 1459 | Claude Opus 4.8 |
| LMArena Text: mathematical work | 1497 | 1470 | Claude Opus 4.8 |
| MathArena | 39.8% | Not measured | Only Claude Opus 4.8 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index102
| Benchmark | Claude Opus 4.8 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| MMLU Pro | 89.6% | 86.0% | Claude Opus 4.8 |
| LegalBench (Vals AI) | 83.6% | 84.0% | GPT-5.6 Luna |
| CorpFin (Vals AI) | 66.7% | 64.2% | Claude Opus 4.8 |
| TaxEval (Vals AI) | 75.6% | 76.2% | GPT-5.6 Luna |
| MortgageTax (Vals AI) | 69.9% | 67.3% | Claude Opus 4.8 |
| MedCode (Vals AI) | 53.2% | 42.4% | Claude Opus 4.8 |
| SimpleQA Verified | 53.0% | 41.0% | Claude Opus 4.8 |
| LMArena Text: science | 1477 | 1433 | Claude Opus 4.8 |
| LMArena Text: medicine and healthcare | 1464 | 1399 | Claude Opus 4.8 |
| LMArena Text: legal and government | 1477 | 1446 | Claude Opus 4.8 |
| LMArena Text: business and finance | 1458 | 1439 | Claude Opus 4.8 |
| FACTS Benchmark Suite | 53.5% | 35.0% | Claude Opus 4.8 |
| Neither model has a result on the other 8 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index90
| Benchmark | Claude Opus 4.8 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| Design Arena: websites | 1258 | 1255 | Claude Opus 4.8 |
| Design Arena: UI components | 1255 | 1248 | Claude Opus 4.8 |
| Design Arena: game dev | 1264 | 1195 | Claude Opus 4.8 |
| Design Arena: data viz | 1240 | 1212 | Claude Opus 4.8 |
| Design Arena: 3D | 1222 | 1121 | Claude Opus 4.8 |
| Design Arena: all code categories | 1255 | 1230 | Claude Opus 4.8 |
| Design Arena: SVG | 1194 | 1193 | Claude Opus 4.8 |
| Design Arena: ASCII art | 1277 | 1142 | Claude Opus 4.8 |
| LiveBench design | 78.8% | 76.0% | Claude Opus 4.8 |
GeneralLike-for-likePublic boards, shown as published, kept out of the index322
| Benchmark | Claude Opus 4.8 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| LiveBench data analysis | 66.0% | 78.0% | GPT-5.6 Luna |
| Public Benefits Bench (Vals AI) | 68.1% | 61.2% | Claude Opus 4.8 |
| SAGE (Vals AI) | 54.8% | 44.2% | Claude Opus 4.8 |
| MedScribe (Vals AI) | 85.8% | 84.4% | Claude Opus 4.8 |
| Vals Multimodal Index | 70.9% | 69.1% | Claude Opus 4.8 |
| IFBench | 62.2% | Not measured | Only Claude Opus 4.8 measured |
| Long context reasoning (AA-LCR) | 77.7% | 83.7% | GPT-5.6 Luna |
| DTBench | 94.9% | 89.1% | Claude Opus 4.8 |
| LMCA | 57.5% | 48.5% | Claude Opus 4.8 |
| Surface Evolver Bench | 87.5% | 61.9% | Claude Opus 4.8 |
| Furniture assembly | 42.5% | 42.5% | Tie |
| LMArena Text | 1461 | 1431 | Claude Opus 4.8 |
| LMArena Text: instruction following | 1476 | 1437 | Claude Opus 4.8 |
| LMArena Text: creative writing | 1454 | 1396 | Claude Opus 4.8 |
| LMArena Vision | 1294 | 1257 | Claude Opus 4.8 |
| LMArena Text: English | 1468 | 1441 | Claude Opus 4.8 |
| LMArena Text: non-English | 1450 | 1417 | Claude Opus 4.8 |
| LMArena Text: Chinese | 1506 | 1470 | Claude Opus 4.8 |
| LMArena Text: German | 1472 | 1455 | Claude Opus 4.8 |
| LMArena Text: Japanese | 1442 | 1415 | Claude Opus 4.8 |
| LMArena Text: Korean | 1431 | 1415 | Claude Opus 4.8 |
| LMArena Text: French | 1481 | 1458 | Claude Opus 4.8 |
| LMArena Text: Spanish | 1465 | 1446 | Claude Opus 4.8 |
| LMArena Text: Russian | 1474 | 1427 | Claude Opus 4.8 |
| LMArena Text: Polish | 1459 | 1405 | Claude Opus 4.8 |
| LMArena Text: multi-turn | 1476 | 1434 | Claude Opus 4.8 |
| LMArena Text: longer queries | 1483 | 1436 | Claude Opus 4.8 |
| LMArena Text: writing and language | 1461 | 1419 | Claude Opus 4.8 |
| LMArena Text: entertainment and media | 1440 | 1402 | Claude Opus 4.8 |
| LMArena Vision: English | 1293 | 1263 | Claude Opus 4.8 |
| LMArena Vision: Chinese | 1346 | 1294 | Claude Opus 4.8 |
| LMArena Vision: reading text | 1303 | 1264 | Claude Opus 4.8 |
| LMArena Vision: humor | 1282 | 1190 | Claude Opus 4.8 |
| LMArena Vision: creative writing | 1301 | 1257 | Claude Opus 4.8 |
| LMArena Document | 1475 | 1457 | Claude Opus 4.8 |
| EQ-Bench 4 | 1281 | 1156 | Claude Opus 4.8 |
| Neither model has a result on the other 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Claude Opus 4.8 | 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 | Anthropic Claude Opus 4.8 | OpenAI GPT-5.6 Luna |
|---|---|---|
| Maker | Anthropic | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | May 27, 2026 | July 9, 2026 |
| Context window | 1M | 1.05M |
| Input $/Mtok | $5.00 | $0.20 |
| Output $/Mtok | $25.00 | $1.20 |
| Coding index | 65.0 | 58.0 |
| Independent measurements behind it | 6 | 6 |
| Cost per solved task | $2.17 | $0.04 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Claude Opus 4.8 better than GPT-5.6 Luna for coding?
Pick Claude Opus 4.8 for coding: it scores 65.0 on the Vibe Coding Index against 58.0 for GPT-5.6 Luna. Pick GPT-5.6 Luna for output tokens 20.8x cheaper ($1.20 vs $25.00 per million) and a lower cost per solved task ($0.04 against $2.17).
Which is cheaper, Claude Opus 4.8 or GPT-5.6 Luna?
Claude Opus 4.8 costs $5.00 per million input tokens and $25.00 per million output tokens; GPT-5.6 Luna costs $0.20 and $1.20. Per solved SWE-bench Verified task, Claude Opus 4.8 costs $2.17 and GPT-5.6 Luna $0.04.
Which has the bigger context window, Claude Opus 4.8 or GPT-5.6 Luna?
GPT-5.6 Luna, with 1.05M tokens against 1M.
How many benchmarks is the Claude Opus 4.8 vs GPT-5.6 Luna comparison based on?
110 benchmarks have a result for both models as of October 22, 2026. Claude Opus 4.8's coding index rests on 6 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.