GPT-5.6 Luna vs Beam 1.0 501B A23B
GPT-5.6 Luna and Beam 1.0 501B A23B cost about the same per output token ($1.20 vs $1.20 per million). Updated October 22, 2026; 28 benchmarks have a result for both.
GPT-5.6 Luna or Beam 1.0 501B A23B: which to pick
Only GPT-5.6 Luna has a coding score, 58.0 on the Vibe Coding Index; Beam 1.0 501B A23B has none, so the two cannot be ranked on coding.
Pick Beam 1.0 501B A23B for work measured by LMArena Text: coding or LMArena Text: mathematical work, where it leads.
- Shared results
- 28
- GPT-5.6 Luna only
- 92
- Beam 1.0 501B A23B only
- 0
- Like-for-like categories
- 6 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
No call
Only GPT-5.6 Luna has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-5.6 Luna has a AA Intelligence Index result, 37.3.
Directional only
Agentic work
AA Agentic Index
No call
Only GPT-5.6 Luna has a AA Agentic Index result, 42.1.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5.6 Luna has a GPQA Diamond result, 91.7%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.6 Luna has a MMLU Pro result, 86.0%.
Directional only
Web design
Design Arena: websites
GPT-5.6 Luna
GPT-5.6 Luna leads, 1255 against 1245.
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
GPT-5.6 Luna $0.0008, Beam 1.0 501B A23B $0.0010 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.6 Luna has the number, $0.04.
Directional only
At a glance
- Coding index
- GPT-5.6 Luna 58.0 from 6 benchmarks, Beam 1.0 501B A23B not benchmarked.
- Widest category
- Coding, 7.6 points apart on average over 5 shared benchmarks.
- Widest single gap
- LMArena WebDev: HTML: GPT-5.6 Luna 1520 against Beam 1.0 501B A23B 1436, 14.0 points apart on a 0 to 100 scale.
- Output price
- Both cost $1.20 per million output tokens.
- Context window
- GPT-5.6 Luna takes 1.05M tokens, Beam 1.0 501B A23B 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 | GPT-5.6 Luna | Bars | Beam 1.0 501B A23B | Ties |
|---|---|---|---|---|
| Coding | 4 | 1 | ||
| Reasoning | 3 | 0 | ||
| Math | 1 | 1 | ||
| Knowledge | 4 | 0 | ||
| Web design | 1 | 1 | ||
| General | 11 | 1 | ||
| All shared boards | 24 | 4 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index41
| Benchmark | GPT-5.6 Luna | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| LiveBench coding | 73.6% | Not measured | Only GPT-5.6 Luna measured |
| LMArena WebDev | 1518 | 1437 | GPT-5.6 Luna |
| AA Coding Index | 71.4 | Not measured | Only GPT-5.6 Luna measured |
| SWE-bench Verified (Vals AI) | 93.0% | Not measured | Only GPT-5.6 Luna measured |
| Terminal-Bench 4 (Vals AI) | 11.6% | Not measured | Only GPT-5.6 Luna measured |
| Vibe Code Bench (Vals AI) | 77.1% | Not measured | Only GPT-5.6 Luna measured |
| Code migration (Vals AI) | 44.5% | Not measured | Only GPT-5.6 Luna measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only GPT-5.6 Luna measured |
| IOI (Vals AI) | 61.8% | Not measured | Only GPT-5.6 Luna measured |
| Terminal-Bench 2.1 (Vals AI) | 79.0% | Not measured | Only GPT-5.6 Luna measured |
| Vibe Code Bench 1-100 (Vals AI) | 22.6% | Not measured | Only GPT-5.6 Luna measured |
| SkillsBench (Vals AI) | 60.5% | Not measured | Only GPT-5.6 Luna measured |
| CyberBench (Vals AI) | 73.6% | Not measured | Only GPT-5.6 Luna measured |
| SciCode | 53.6% | Not measured | Only GPT-5.6 Luna measured |
| FrontierCode | 39.8% | Not measured | Only GPT-5.6 Luna measured |
| WeirdML | 60.9% | Not measured | Only GPT-5.6 Luna measured |
| ALE-Bench | 1667.4 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: coding | 1466 | 1473 | Beam 1.0 501B A23B |
| LMArena Text: software and IT | 1463 | 1461 | GPT-5.6 Luna |
| LMArena WebDev: React | 1508 | 1443 | GPT-5.6 Luna |
| LMArena WebDev: HTML | 1520 | 1436 | GPT-5.6 Luna |
| LMArena WebDev: from an image | 1517 | Not measured | Only GPT-5.6 Luna measured |
| SciCode | 6.2% | Not measured | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 20 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.6 Luna | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| AA Intelligence Index | 37.3 | Not measured | Only GPT-5.6 Luna measured |
| Epoch Capabilities Index | 156.5 | Not measured | Only GPT-5.6 Luna measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index15 boards, none shared
| Benchmark | GPT-5.6 Luna | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| AA Agentic Index | 42.1 | Not measured | Only GPT-5.6 Luna measured |
| LiveBench research | 51.0% | Not measured | Only GPT-5.6 Luna measured |
| LiveBench browser | 68.0% | Not measured | Only GPT-5.6 Luna measured |
| LiveBench long agentic | 30.4% | Not measured | Only GPT-5.6 Luna measured |
| SREBench (Vals AI) | 1.5% | Not measured | Only GPT-5.6 Luna measured |
| Finance Agent (Vals AI) | 55.0% | Not measured | Only GPT-5.6 Luna measured |
| Tax Agent Bench (Vals AI) | 24.8% | Not measured | Only GPT-5.6 Luna measured |
| Legal Research Bench (Vals AI) | 36.5% | Not measured | Only GPT-5.6 Luna measured |
| Excel Modeling Benchmark (Vals AI) | 67.1% | Not measured | Only GPT-5.6 Luna measured |
| BioMysteryBench (Vals AI) | 61.5% | Not measured | Only GPT-5.6 Luna measured |
| Terminal-Bench-Science (Vals AI) | 0.0% | Not measured | Only GPT-5.6 Luna measured |
| Vals Science Index | 25.3% | Not measured | Only GPT-5.6 Luna measured |
| APEX-Agents | 43.0% | Not measured | Only GPT-5.6 Luna measured |
| BALROG | 45.6% | Not measured | Only GPT-5.6 Luna measured |
| Vending-Bench 2 | $4,095 | Not measured | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 27 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GPT-5.6 Luna | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| GPQA Diamond | 91.7% | Not measured | Only GPT-5.6 Luna measured |
| LiveBench reasoning | 85.6% | Not measured | Only GPT-5.6 Luna measured |
| MysteryMechanism (Vals AI) | 14.4% | Not measured | Only GPT-5.6 Luna measured |
| MMMU Pro (Vals AI) | 85.0% | Not measured | Only GPT-5.6 Luna measured |
| GPQA Diamond (Artificial Analysis run) | 91.1% | Not measured | Only GPT-5.6 Luna measured |
| Humanity's Last Exam (Artificial Analysis run) | 39.5% | Not measured | Only GPT-5.6 Luna measured |
| GPQA Diamond (Epoch AI run) | 91.6% | Not measured | Only GPT-5.6 Luna measured |
| ARC-AGI | 88.0% | Not measured | Only GPT-5.6 Luna measured |
| ARC-AGI-2 | 59.5% | Not measured | Only GPT-5.6 Luna measured |
| SimpleBench | 46.8% | Not measured | Only GPT-5.6 Luna measured |
| Chess puzzles | 43.0% | Not measured | Only GPT-5.6 Luna measured |
| Mystery game puzzles | 21.0% | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: hard prompts | 1451 | 1442 | GPT-5.6 Luna |
| LMArena Text: expert | 1478 | 1447 | GPT-5.6 Luna |
| LMArena Text: hard prompts in English | 1460 | 1451 | GPT-5.6 Luna |
| LMArena Vision: diagrams | 1273 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Vision: homework | 1282 | Not measured | Only GPT-5.6 Luna measured |
| Kagi LLM Benchmark | 49.1% | Not measured | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GPT-5.6 Luna | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| OTIS Mock AIME | 98.3% | Not measured | Only GPT-5.6 Luna measured |
| FrontierMath, tiers 1 to 3 | 82.1% | Not measured | Only GPT-5.6 Luna measured |
| FrontierMath, tier 4 | 61.0% | Not measured | Only GPT-5.6 Luna measured |
| ProofBench | 60.0% | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: math | 1459 | 1459 | GPT-5.6 Luna |
| LMArena Text: mathematical work | 1470 | 1475 | Beam 1.0 501B A23B |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GPT-5.6 Luna | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| MMLU Pro | 86.0% | Not measured | Only GPT-5.6 Luna measured |
| LegalBench (Vals AI) | 84.0% | Not measured | Only GPT-5.6 Luna measured |
| CorpFin (Vals AI) | 64.2% | Not measured | Only GPT-5.6 Luna measured |
| TaxEval (Vals AI) | 76.2% | Not measured | Only GPT-5.6 Luna measured |
| MortgageTax (Vals AI) | 67.3% | Not measured | Only GPT-5.6 Luna measured |
| MedCode (Vals AI) | 42.4% | Not measured | Only GPT-5.6 Luna measured |
| SimpleQA Verified | 41.0% | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: science | 1433 | 1430 | GPT-5.6 Luna |
| LMArena Text: medicine and healthcare | 1399 | 1376 | GPT-5.6 Luna |
| LMArena Text: legal and government | 1446 | 1433 | GPT-5.6 Luna |
| LMArena Text: business and finance | 1439 | 1430 | GPT-5.6 Luna |
| FACTS Benchmark Suite | 35.0% | Not measured | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 8 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GPT-5.6 Luna | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| Design Arena: websites | 1255 | 1245 | GPT-5.6 Luna |
| Design Arena: UI components | 1248 | Not measured | Only GPT-5.6 Luna measured |
| Design Arena: game dev | 1195 | 1220 | Beam 1.0 501B A23B |
| Design Arena: data viz | 1212 | Not measured | Only GPT-5.6 Luna measured |
| Design Arena: 3D | 1121 | Not measured | Only GPT-5.6 Luna measured |
| Design Arena: all code categories | 1230 | Not measured | Only GPT-5.6 Luna measured |
| Design Arena: SVG | 1193 | Not measured | Only GPT-5.6 Luna measured |
| Design Arena: ASCII art | 1142 | Not measured | Only GPT-5.6 Luna measured |
| LiveBench design | 76.0% | Not measured | Only GPT-5.6 Luna measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index111
| Benchmark | GPT-5.6 Luna | Beam 1.0 501B A23B | Leader |
|---|---|---|---|
| LiveBench data analysis | 78.0% | Not measured | Only GPT-5.6 Luna measured |
| Public Benefits Bench (Vals AI) | 61.2% | Not measured | Only GPT-5.6 Luna measured |
| SAGE (Vals AI) | 44.2% | Not measured | Only GPT-5.6 Luna measured |
| MedScribe (Vals AI) | 84.4% | Not measured | Only GPT-5.6 Luna measured |
| Vals Multimodal Index | 69.1% | Not measured | Only GPT-5.6 Luna measured |
| Long context reasoning (AA-LCR) | 83.7% | Not measured | Only GPT-5.6 Luna measured |
| DTBench | 89.1% | Not measured | Only GPT-5.6 Luna measured |
| LMCA | 48.5% | Not measured | Only GPT-5.6 Luna measured |
| Surface Evolver Bench | 61.9% | Not measured | Only GPT-5.6 Luna measured |
| Furniture assembly | 42.5% | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text | 1431 | 1425 | GPT-5.6 Luna |
| LMArena Text: instruction following | 1437 | 1430 | GPT-5.6 Luna |
| LMArena Text: creative writing | 1396 | 1390 | GPT-5.6 Luna |
| LMArena Vision | 1257 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: English | 1441 | 1436 | GPT-5.6 Luna |
| LMArena Text: non-English | 1417 | 1413 | GPT-5.6 Luna |
| LMArena Text: Chinese | 1470 | 1424 | GPT-5.6 Luna |
| LMArena Text: German | 1455 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: Japanese | 1415 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: Korean | 1415 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: French | 1458 | 1461 | Beam 1.0 501B A23B |
| LMArena Text: Spanish | 1446 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: Russian | 1427 | 1412 | GPT-5.6 Luna |
| LMArena Text: Polish | 1405 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: multi-turn | 1434 | 1414 | GPT-5.6 Luna |
| LMArena Text: longer queries | 1436 | 1429 | GPT-5.6 Luna |
| LMArena Text: writing and language | 1419 | 1400 | GPT-5.6 Luna |
| LMArena Text: entertainment and media | 1402 | 1399 | GPT-5.6 Luna |
| LMArena Vision: English | 1263 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Vision: Chinese | 1294 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Vision: reading text | 1264 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Vision: humor | 1190 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Vision: creative writing | 1257 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Document | 1457 | Not measured | Only GPT-5.6 Luna measured |
| EQ-Bench 4 | 1156 | Not measured | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.6 Luna | Beam 1.0 501B A23B | 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 | OpenAI GPT-5.6 Luna | Reflection Beam 1.0 501B A23B |
|---|---|---|
| Maker | OpenAI | Reflection |
| Weights | No public weights listed | Open weights |
| Released | July 9, 2026 | October 16, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.20 | $0.40 |
| Output $/Mtok | $1.20 | $1.20 |
| Coding index | 58.0 | Not benchmarked |
| Independent measurements behind it | 6 | 1 |
| Cost per solved task | $0.04 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.6 Luna better than Beam 1.0 501B A23B for coding?
Only GPT-5.6 Luna has a coding score, 58.0 on the Vibe Coding Index; Beam 1.0 501B A23B has none, so the two cannot be ranked on coding. Pick Beam 1.0 501B A23B for work measured by LMArena Text: coding or LMArena Text: mathematical work, where it leads.
Which is cheaper, GPT-5.6 Luna or Beam 1.0 501B A23B?
GPT-5.6 Luna costs $0.20 per million input tokens and $1.20 per million output tokens; Beam 1.0 501B A23B costs $0.40 and $1.20. Cost per solved task is known for GPT-5.6 Luna only.
Which has the bigger context window, GPT-5.6 Luna or Beam 1.0 501B A23B?
GPT-5.6 Luna, with 1.05M tokens against 1.05M.
How many benchmarks is the GPT-5.6 Luna vs Beam 1.0 501B A23B comparison based on?
28 benchmarks have a result for both models as of October 22, 2026. GPT-5.6 Luna's coding index rests on 6 independent benchmarks and Beam 1.0 501B A23B's on 1. Where only one model has a result, the page says so instead of naming a winner.