GPT-5.3-Codex vs GPT-5.6 Luna
Updated October 21, 2026. 23 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
vs boards won of 23 shared
Decision reading
Pick GPT-5.6 Luna for coding: it scores 61.5 on the Vibe Coding Index against 29.5 for GPT-5.3-Codex.
Pick GPT-5.3-Codex when what matters is WeirdML or Epoch Capabilities Index, where it leads.
Model AGPT-5.3-CodexOpenAI29.5/1001 independent benchmark$0.59 per solved taskModel BGPT-5.6 LunaOpenAI61.5/1002 independent benchmarks$0.04 per solved task
- Shared results
- 23
- GPT-5.3-Codex only
- 11
- GPT-5.6 Luna only
- 93
- Like-for-like categories
- 6 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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, four public coding benchmarks
GPT-5.6 Luna
GPT-5.6 Luna scores 61.5 against 29.5.
1 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.6 Luna
GPT-5.6 Luna leads, 37.3 against 32.5.
Same board, both measured
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, 1256 against 1163.
Same board, both measured
Long documents
Listed context window
GPT-5.6 Luna
GPT-5.6 Luna takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5.6 Luna
GPT-5.3-Codex $0.0088, GPT-5.6 Luna $0.0008 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5.6 Luna
GPT-5.3-Codex $0.59, GPT-5.6 Luna $0.04.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5.3-Codex 29.5 from 1 benchmark, GPT-5.6 Luna 61.5 from 2 benchmarks.
- Widest category
- Coding, 13.4 points apart on average over 9 shared benchmarks.
- Widest single gap
- LMArena WebDev: React: GPT-5.3-Codex 1395 against GPT-5.6 Luna 1508, 18.8 points apart on a 0 to 100 scale.
- Output price
- GPT-5.3-Codex $14.00 against GPT-5.6 Luna $1.20 per million output tokens, 11.7x apart.
- Context window
- GPT-5.3-Codex takes 400K 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 | GPT-5.3-Codex | Bars | GPT-5.6 Luna | Ties |
|---|---|---|---|---|
| Coding | 1 | 8 | ||
| Intelligence | 1 | 1 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| Web design | 1 | 7 | ||
| General | 0 | 1 | ||
| All shared boards | 6 | 17 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index18
| Benchmark | GPT-5.3-Codex | GPT-5.6 Luna | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 73.6% | Only GPT-5.6 Luna measured |
| LMArena WebDev | 1407 | 1518 | GPT-5.6 Luna |
| AA Coding Index | Not measured | 71.4 | Only GPT-5.6 Luna measured |
| LiveCodeBench (Vals AI) | 87.3% | Not measured | Only GPT-5.3-Codex measured |
| SWE-bench Verified (Vals AI) | 78.0% | 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) | 61.8% | 77.1% | GPT-5.6 Luna |
| Code migration (Vals AI) | Not measured | 44.5% | Only GPT-5.6 Luna measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only GPT-5.6 Luna measured |
| IOI (Vals AI) | 53.8% | 61.8% | GPT-5.6 Luna |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 79.0% | Only GPT-5.6 Luna measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 22.6% | Only GPT-5.6 Luna measured |
| SkillsBench (Vals AI) | Not measured | 60.5% | Only GPT-5.6 Luna measured |
| CyberBench (Vals AI) | Not measured | 73.6% | Only GPT-5.6 Luna measured |
| SciCode | Not measured | 53.6% | Only GPT-5.6 Luna measured |
| Terminal-Bench Hard (Artificial Analysis run) | 53.0% | Not measured | Only GPT-5.3-Codex measured |
| Terminal-Bench | 78.4% | Not measured | Only GPT-5.3-Codex measured |
| FrontierCode | Not measured | 39.8% | Only GPT-5.6 Luna measured |
| WeirdML | 79.3% | 60.9% | GPT-5.3-Codex |
| ALE-Bench | 1655.2 | 1667.4 | GPT-5.6 Luna |
| LMArena Text: coding | Not measured | 1466 | Only GPT-5.6 Luna measured |
| LMArena Text: software and IT | Not measured | 1463 | Only GPT-5.6 Luna measured |
| LMArena WebDev: React | 1395 | 1508 | GPT-5.6 Luna |
| LMArena WebDev: HTML | 1422 | 1520 | GPT-5.6 Luna |
| LMArena WebDev: from an image | 1443 | 1517 | GPT-5.6 Luna |
| Neither model has a result on the other 13 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GPT-5.3-Codex | GPT-5.6 Luna | Leader |
|---|---|---|---|
| AA Intelligence Index | 32.5 | 37.3 | GPT-5.6 Luna |
| Epoch Capabilities Index | 157.1 | 156.5 | GPT-5.3-Codex |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.3-Codex | GPT-5.6 Luna | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 42.1 | Only GPT-5.6 Luna measured |
| Design Arena: agents building web apps | 1033 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building full-stack apps | 983 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building mobile apps | 1054 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building Android apps | 1084 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building Godot games | 1124 | Not measured | Only GPT-5.3-Codex 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) | Not measured | 1.5% | Only GPT-5.6 Luna measured |
| Finance Agent (Vals AI) | Not measured | 55.0% | Only GPT-5.6 Luna measured |
| Tax Agent Bench (Vals AI) | Not measured | 24.8% | Only GPT-5.6 Luna measured |
| Legal Research Bench (Vals AI) | Not measured | 36.5% | Only GPT-5.6 Luna measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 67.1% | Only GPT-5.6 Luna measured |
| 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 | 86.0% | Not measured | Only GPT-5.3-Codex measured |
| APEX-Agents | Not measured | 43.0% | Only GPT-5.6 Luna measured |
| METR time horizons | 74.5% | Not measured | Only GPT-5.3-Codex measured |
| BALROG | Not measured | 45.6% | Only GPT-5.6 Luna measured |
| Vending-Bench 2 | $5,940 | $4,095 | GPT-5.3-Codex |
| Neither model has a result on the other 17 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.3-Codex | GPT-5.6 Luna | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 91.7% | Only GPT-5.6 Luna measured |
| 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) | 91.5% | 91.1% | GPT-5.3-Codex |
| Humanity's Last Exam (Artificial Analysis run) | 42.5% | 39.5% | GPT-5.3-Codex |
| GPQA Diamond (Epoch AI run) | Not measured | 91.6% | Only GPT-5.6 Luna measured |
| ARC-AGI | Not measured | 88.0% | Only GPT-5.6 Luna measured |
| ARC-AGI-2 | Not measured | 59.5% | Only GPT-5.6 Luna measured |
| SimpleBench | Not measured | 46.8% | Only GPT-5.6 Luna measured |
| Chess puzzles | Not measured | 43.0% | Only GPT-5.6 Luna measured |
| Mystery game puzzles | Not measured | 21.0% | Only GPT-5.6 Luna measured |
| LMArena Text: hard prompts | Not measured | 1451 | Only GPT-5.6 Luna measured |
| LMArena Text: expert | Not measured | 1478 | Only GPT-5.6 Luna measured |
| LMArena Text: hard prompts in English | Not measured | 1460 | Only GPT-5.6 Luna measured |
| LMArena Vision: diagrams | Not measured | 1273 | Only GPT-5.6 Luna measured |
| LMArena Vision: homework | Not measured | 1282 | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 4 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-5.3-Codex | GPT-5.6 Luna | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 98.3% | Only GPT-5.6 Luna measured |
| FrontierMath, tiers 1 to 3 | Not measured | 82.1% | Only GPT-5.6 Luna measured |
| FrontierMath, tier 4 | Not measured | 61.0% | Only GPT-5.6 Luna measured |
| ProofBench | Not measured | 60.0% | Only GPT-5.6 Luna measured |
| LMArena Text: math | Not measured | 1459 | Only GPT-5.6 Luna measured |
| LMArena Text: mathematical work | Not measured | 1470 | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | GPT-5.3-Codex | GPT-5.6 Luna | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 86.0% | Only GPT-5.6 Luna measured |
| LegalBench (Vals AI) | Not measured | 84.0% | Only GPT-5.6 Luna measured |
| CorpFin (Vals AI) | Not measured | 64.2% | Only GPT-5.6 Luna measured |
| TaxEval (Vals AI) | Not measured | 76.2% | Only GPT-5.6 Luna measured |
| MortgageTax (Vals AI) | Not measured | 67.3% | Only GPT-5.6 Luna measured |
| MedCode (Vals AI) | Not measured | 42.4% | Only GPT-5.6 Luna measured |
| SimpleQA Verified | Not measured | 41.0% | Only GPT-5.6 Luna measured |
| LMArena Text: science | Not measured | 1433 | Only GPT-5.6 Luna measured |
| LMArena Text: medicine and healthcare | Not measured | 1399 | Only GPT-5.6 Luna measured |
| LMArena Text: legal and government | Not measured | 1446 | Only GPT-5.6 Luna measured |
| LMArena Text: business and finance | Not measured | 1439 | 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 index17
| Benchmark | GPT-5.3-Codex | GPT-5.6 Luna | Leader |
|---|---|---|---|
| Design Arena: websites | 1163 | 1256 | GPT-5.6 Luna |
| Design Arena: UI components | 1137 | 1250 | GPT-5.6 Luna |
| Design Arena: game dev | 1162 | 1196 | GPT-5.6 Luna |
| Design Arena: data viz | 1164 | 1212 | GPT-5.6 Luna |
| Design Arena: 3D | 1008 | 1121 | GPT-5.6 Luna |
| Design Arena: all code categories | 1145 | 1231 | GPT-5.6 Luna |
| Design Arena: SVG | 1129 | 1193 | GPT-5.6 Luna |
| Design Arena: ASCII art | 1151 | 1144 | GPT-5.3-Codex |
| LiveBench design | Not measured | 76.0% | Only GPT-5.6 Luna measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5.3-Codex | 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) | Not measured | 84.4% | Only GPT-5.6 Luna measured |
| Vals Multimodal Index | Not measured | 69.1% | Only GPT-5.6 Luna measured |
| IFBench | 75.4% | Not measured | Only GPT-5.3-Codex measured |
| Long context reasoning (AA-LCR) | 83.3% | 83.7% | GPT-5.6 Luna |
| DTBench | Not measured | 89.1% | Only GPT-5.6 Luna measured |
| LMCA | Not measured | 48.5% | Only GPT-5.6 Luna measured |
| Surface Evolver Bench | Not measured | 61.9% | Only GPT-5.6 Luna measured |
| Furniture assembly | Not measured | 42.5% | Only GPT-5.6 Luna measured |
| LMArena Text | Not measured | 1431 | Only GPT-5.6 Luna measured |
| LMArena Text: instruction following | Not measured | 1437 | Only GPT-5.6 Luna measured |
| LMArena Text: creative writing | Not measured | 1396 | Only GPT-5.6 Luna measured |
| LMArena Vision | Not measured | 1257 | Only GPT-5.6 Luna measured |
| LMArena Text: English | Not measured | 1441 | Only GPT-5.6 Luna measured |
| LMArena Text: non-English | Not measured | 1417 | Only GPT-5.6 Luna measured |
| LMArena Text: Chinese | Not measured | 1470 | Only GPT-5.6 Luna measured |
| LMArena Text: German | Not measured | 1455 | Only GPT-5.6 Luna measured |
| LMArena Text: Japanese | Not measured | 1415 | Only GPT-5.6 Luna measured |
| LMArena Text: Korean | Not measured | 1415 | Only GPT-5.6 Luna measured |
| LMArena Text: French | Not measured | 1458 | Only GPT-5.6 Luna measured |
| LMArena Text: Spanish | Not measured | 1446 | Only GPT-5.6 Luna measured |
| LMArena Text: Russian | Not measured | 1427 | Only GPT-5.6 Luna measured |
| LMArena Text: Polish | Not measured | 1405 | Only GPT-5.6 Luna measured |
| LMArena Text: multi-turn | Not measured | 1434 | Only GPT-5.6 Luna measured |
| LMArena Text: longer queries | Not measured | 1436 | Only GPT-5.6 Luna measured |
| LMArena Text: writing and language | Not measured | 1419 | Only GPT-5.6 Luna measured |
| LMArena Text: entertainment and media | Not measured | 1402 | Only GPT-5.6 Luna measured |
| 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 | GPT-5.3-Codex | 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-5.3-Codex$0.0088Fits in one request
GPT-5.6 Luna$0.0008Fits in one request
GPT-5.6 Luna costs less
Repository review
50K input + 3K output tokens
GPT-5.3-Codex$0.130Fits in one request
GPT-5.6 Luna$0.014Fits in one request
GPT-5.6 Luna costs less
Agent loop
200K input + 10K output tokens
GPT-5.3-Codex$0.490Fits in one request
GPT-5.6 Luna$0.052Fits in one request
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-5.3-Codex | OpenAI GPT-5.6 Luna |
|---|---|---|
| Maker | OpenAI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | February 24, 2026 | July 9, 2026 |
| Context window | 400K | 1.05M |
| Input $/Mtok | $1.75 | $0.20 |
| Output $/Mtok | $14.00 | $1.20 |
| Coding index | 29.5 | 61.5 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | $0.59 | $0.04 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.3-Codex better than GPT-5.6 Luna for coding?
Pick GPT-5.6 Luna for coding: it scores 61.5 on the Vibe Coding Index against 29.5 for GPT-5.3-Codex. Pick GPT-5.3-Codex when what matters is WeirdML or Epoch Capabilities Index, where it leads.
Which is cheaper, GPT-5.3-Codex or GPT-5.6 Luna?
GPT-5.3-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; GPT-5.6 Luna costs $0.20 and $1.20. Per solved SWE-bench Verified task, GPT-5.3-Codex costs $0.59 and GPT-5.6 Luna $0.04.
Which has the bigger context window, GPT-5.3-Codex or GPT-5.6 Luna?
GPT-5.6 Luna, with 1.05M tokens against 400K.
How many benchmarks is the GPT-5.3-Codex vs GPT-5.6 Luna comparison based on?
23 benchmarks have a result for both models as of October 21, 2026. GPT-5.3-Codex's coding index rests on 1 independent benchmark and GPT-5.6 Luna's on 2. Where only one model has a result, the page says so instead of naming a winner.