GPT-5.6 Luna vs gpt-oss-120b
GPT-5.6 Luna leads gpt-oss-120b on the Artificial Analysis Intelligence Index, 37.3 to 11.6, and gpt-oss-120b costs 2.4x less per output token ($0.50 vs $1.20 per million). Updated October 22, 2026; 61 benchmarks have a result for both.
GPT-5.6 Luna or gpt-oss-120b: which to pick
Only GPT-5.6 Luna has a coding score, 58.0 on the Vibe Coding Index; gpt-oss-120b has none, so the two cannot be ranked on coding.
Pick gpt-oss-120b for output tokens 2.4x cheaper ($0.50 vs $1.20 per million) and work measured by SciCode or Kagi LLM Benchmark, where it leads.
- Shared results
- 61
- GPT-5.6 Luna only
- 58
- gpt-oss-120b only
- 20
- 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
No call
Only GPT-5.6 Luna has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.6 Luna
GPT-5.6 Luna leads, 37.3 against 11.6.
Same board, both measured
Agentic work
AA Agentic Index
GPT-5.6 Luna
GPT-5.6 Luna leads, 42.1 against 3.7.
Same board, both measured
Reasoning
GPQA Diamond
GPT-5.6 Luna
GPT-5.6 Luna leads, 91.7% against 78.5%.
Same board, both measured
Math
AIME
No call
Only gpt-oss-120b has a AIME result, 92.6%.
Directional only
Knowledge
MMLU Pro
GPT-5.6 Luna
GPT-5.6 Luna leads, 86.0% against 79.2%.
Same board, both measured
Web design
Design Arena: websites
GPT-5.6 Luna
GPT-5.6 Luna leads, 1255 against 968.
Same board, both measured
Long documents
Listed context window
GPT-5.6 Luna
GPT-5.6 Luna takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
gpt-oss-120b
GPT-5.6 Luna $0.0008, gpt-oss-120b $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5.6 Luna
GPT-5.6 Luna $0.04, gpt-oss-120b $0.51.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5.6 Luna 58.0 from 6 benchmarks, gpt-oss-120b not benchmarked.
- Widest category
- Agentic work, 34.2 points apart on average over 3 shared benchmarks.
- Widest single gap
- SWE-bench Verified (Vals AI): GPT-5.6 Luna 93.0% against gpt-oss-120b 33.6%, 59.4 points apart on a 0 to 100 scale.
- Output price
- GPT-5.6 Luna $1.20 against gpt-oss-120b $0.50 per million output tokens, 2.4x apart.
- Context window
- GPT-5.6 Luna takes 1.05M tokens, gpt-oss-120b 131K.
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 | gpt-oss-120b | Ties |
|---|---|---|---|---|
| Coding | 7 | 1 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 3 | 0 | ||
| Reasoning | 9 | 1 | ||
| Math | 3 | 0 | ||
| Knowledge | 8 | 0 | ||
| Web design | 6 | 0 | ||
| General | 21 | 0 | ||
| All shared boards | 59 | 2 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index71
| Benchmark | GPT-5.6 Luna | gpt-oss-120b | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 41.8% | Only gpt-oss-120b measured |
| SWE-bench Verified | Not measured | 26.0% | Only gpt-oss-120b measured |
| LiveBench coding | 73.6% | Not measured | Only GPT-5.6 Luna measured |
| LMArena WebDev | 1518 | Not measured | Only GPT-5.6 Luna measured |
| AA Coding Index | 71.4 | 30.4 | GPT-5.6 Luna |
| LiveCodeBench (Vals AI) | Not measured | 83.2% | Only gpt-oss-120b measured |
| SWE-bench Verified (Vals AI) | 93.0% | 33.6% | GPT-5.6 Luna |
| 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 |
| LiveCodeBench (Artificial Analysis run) | Not measured | 87.8% | Only gpt-oss-120b measured |
| SciCode | 53.6% | 34.0% | GPT-5.6 Luna |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 23.5% | Only gpt-oss-120b measured |
| Terminal-Bench | Not measured | 18.7% | Only gpt-oss-120b measured |
| FrontierCode | 39.8% | Not measured | Only GPT-5.6 Luna measured |
| WeirdML | 60.9% | 48.2% | GPT-5.6 Luna |
| ALE-Bench | 1667.4 | 575.6 | GPT-5.6 Luna |
| LMArena Text: coding | 1466 | 1379 | GPT-5.6 Luna |
| LMArena Text: software and IT | 1463 | 1387 | GPT-5.6 Luna |
| LMArena WebDev: React | 1508 | Not measured | Only GPT-5.6 Luna measured |
| LMArena WebDev: HTML | 1520 | Not measured | Only GPT-5.6 Luna measured |
| LMArena WebDev: from an image | 1517 | Not measured | Only GPT-5.6 Luna measured |
| SciCode | 6.2% | 7.7% | gpt-oss-120b |
| Neither model has a result on the other 10 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.6 Luna | gpt-oss-120b | Leader |
|---|---|---|---|
| AA Intelligence Index | 37.3 | 11.6 | GPT-5.6 Luna |
| Epoch Capabilities Index | 156.5 | 139.8 | GPT-5.6 Luna |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GPT-5.6 Luna | gpt-oss-120b | Leader |
|---|---|---|---|
| AA Agentic Index | 42.1 | 3.7 | GPT-5.6 Luna |
| 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 |
| τ²-Bench Telecom | Not measured | 65.8% | Only gpt-oss-120b measured |
| APEX-Agents | 43.0% | 4.4% | GPT-5.6 Luna |
| METR time horizons | Not measured | 56.6% | Only gpt-oss-120b measured |
| BALROG | 45.6% | Not measured | Only GPT-5.6 Luna measured |
| Vending-Bench 2 | $4,095 | -$22 | GPT-5.6 Luna |
| BrowseComp-Plus | Not measured | 67.0% | Only gpt-oss-120b measured |
| Neither model has a result on the other 23 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index91
| Benchmark | GPT-5.6 Luna | gpt-oss-120b | Leader |
|---|---|---|---|
| GPQA Diamond | 91.7% | 78.5% | GPT-5.6 Luna |
| 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% | 78.2% | GPT-5.6 Luna |
| Humanity's Last Exam (Artificial Analysis run) | 39.5% | 19.6% | GPT-5.6 Luna |
| GPQA Diamond (Epoch AI run) | 91.6% | 75.8% | GPT-5.6 Luna |
| 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% | 22.1% | GPT-5.6 Luna |
| Chess puzzles | 43.0% | Not measured | Only GPT-5.6 Luna measured |
| Mystery game puzzles | 21.0% | 2.0% | GPT-5.6 Luna |
| LMArena Text: hard prompts | 1451 | 1364 | GPT-5.6 Luna |
| LMArena Text: expert | 1478 | 1356 | GPT-5.6 Luna |
| LMArena Text: hard prompts in English | 1460 | 1368 | 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% | 58.6% | gpt-oss-120b |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GPT-5.6 Luna | gpt-oss-120b | Leader |
|---|---|---|---|
| AIME | Not measured | 92.6% | Only gpt-oss-120b measured |
| MATH 500 (Vals AI) | Not measured | 94.8% | Only gpt-oss-120b measured |
| MGSM (Vals AI) | Not measured | 92.0% | Only gpt-oss-120b measured |
| AA Math Index | Not measured | 93.4 | Only gpt-oss-120b measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 93.4% | Only gpt-oss-120b measured |
| OTIS Mock AIME | 98.3% | 88.9% | GPT-5.6 Luna |
| 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 | 1389 | GPT-5.6 Luna |
| LMArena Text: mathematical work | 1470 | 1386 | GPT-5.6 Luna |
| Neither model has a result on the other 2 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | GPT-5.6 Luna | gpt-oss-120b | Leader |
|---|---|---|---|
| MMLU Pro | 86.0% | 79.2% | GPT-5.6 Luna |
| LegalBench (Vals AI) | 84.0% | 75.9% | GPT-5.6 Luna |
| CaseLaw (Vals AI) | Not measured | 48.8% | Only gpt-oss-120b measured |
| CorpFin (Vals AI) | 64.2% | 58.2% | GPT-5.6 Luna |
| TaxEval (Vals AI) | 76.2% | 71.6% | GPT-5.6 Luna |
| MortgageTax (Vals AI) | 67.3% | Not measured | Only GPT-5.6 Luna measured |
| MedQA (Vals AI) | Not measured | 91.4% | Only gpt-oss-120b measured |
| MedCode (Vals AI) | 42.4% | Not measured | Only GPT-5.6 Luna measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 80.8% | Only gpt-oss-120b measured |
| SimpleQA Verified | 41.0% | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: science | 1433 | 1382 | GPT-5.6 Luna |
| LMArena Text: medicine and healthcare | 1399 | 1388 | GPT-5.6 Luna |
| LMArena Text: legal and government | 1446 | 1355 | GPT-5.6 Luna |
| LMArena Text: business and finance | 1439 | 1359 | 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 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GPT-5.6 Luna | gpt-oss-120b | Leader |
|---|---|---|---|
| Design Arena: websites | 1255 | 968 | GPT-5.6 Luna |
| Design Arena: UI components | 1248 | 924 | GPT-5.6 Luna |
| Design Arena: game dev | 1195 | 992 | GPT-5.6 Luna |
| Design Arena: data viz | 1212 | 987 | GPT-5.6 Luna |
| Design Arena: 3D | 1121 | 900 | GPT-5.6 Luna |
| Design Arena: all code categories | 1230 | 962 | GPT-5.6 Luna |
| 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 index210
| Benchmark | GPT-5.6 Luna | gpt-oss-120b | 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 |
| IFBench | Not measured | 69.0% | Only gpt-oss-120b measured |
| Long context reasoning (AA-LCR) | 83.7% | 52.0% | GPT-5.6 Luna |
| DTBench | 89.1% | 76.3% | GPT-5.6 Luna |
| LMCA | 48.5% | 22.1% | GPT-5.6 Luna |
| Fiction.LiveBench | Not measured | 44.4% | Only gpt-oss-120b measured |
| Creative writing (Lech Mazur) | Not measured | 77.1% | Only gpt-oss-120b measured |
| Surface Evolver Bench | 61.9% | 25.0% | GPT-5.6 Luna |
| Furniture assembly | 42.5% | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text | 1431 | 1366 | GPT-5.6 Luna |
| LMArena Text: instruction following | 1437 | 1318 | GPT-5.6 Luna |
| LMArena Text: creative writing | 1396 | 1274 | GPT-5.6 Luna |
| LMArena Vision | 1257 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: English | 1441 | 1375 | GPT-5.6 Luna |
| LMArena Text: non-English | 1417 | 1351 | GPT-5.6 Luna |
| LMArena Text: Chinese | 1470 | 1385 | GPT-5.6 Luna |
| LMArena Text: German | 1455 | 1353 | GPT-5.6 Luna |
| LMArena Text: Japanese | 1415 | 1331 | GPT-5.6 Luna |
| LMArena Text: Korean | 1415 | 1282 | GPT-5.6 Luna |
| LMArena Text: French | 1458 | 1370 | GPT-5.6 Luna |
| LMArena Text: Spanish | 1446 | 1389 | GPT-5.6 Luna |
| LMArena Text: Russian | 1427 | 1343 | GPT-5.6 Luna |
| LMArena Text: Polish | 1405 | 1353 | GPT-5.6 Luna |
| LMArena Text: multi-turn | 1434 | 1340 | GPT-5.6 Luna |
| LMArena Text: longer queries | 1436 | 1319 | GPT-5.6 Luna |
| LMArena Text: writing and language | 1419 | 1306 | GPT-5.6 Luna |
| LMArena Text: entertainment and media | 1402 | 1292 | 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 |
| Neither model has a result on the other 9 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.6 Luna | gpt-oss-120b | 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-oss-120b costs less
Repository review
50K input + 3K output tokens
gpt-oss-120b costs less
Agent loop
200K input + 10K output tokens
gpt-oss-120b 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 | OpenAI gpt-oss-120b |
|---|---|---|
| Maker | OpenAI | OpenAI |
| Weights | No public weights listed | Open weights |
| Released | July 9, 2026 | August 5, 2025 |
| Context window | 1.05M | 131K |
| Input $/Mtok | $0.20 | $0.10 |
| Output $/Mtok | $1.20 | $0.50 |
| Coding index | 58.0 | Not benchmarked |
| Independent measurements behind it | 6 | 1 |
| Cost per solved task | $0.04 | $0.51 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.6 Luna better than gpt-oss-120b for coding?
Only GPT-5.6 Luna has a coding score, 58.0 on the Vibe Coding Index; gpt-oss-120b has none, so the two cannot be ranked on coding. Pick gpt-oss-120b for output tokens 2.4x cheaper ($0.50 vs $1.20 per million) and work measured by SciCode or Kagi LLM Benchmark, where it leads.
Which is cheaper, GPT-5.6 Luna or gpt-oss-120b?
GPT-5.6 Luna costs $0.20 per million input tokens and $1.20 per million output tokens; gpt-oss-120b costs $0.10 and $0.50. Per solved SWE-bench Verified task, GPT-5.6 Luna costs $0.04 and gpt-oss-120b $0.51.
Which has the bigger context window, GPT-5.6 Luna or gpt-oss-120b?
GPT-5.6 Luna, with 1.05M tokens against 131K.
How many benchmarks is the GPT-5.6 Luna vs gpt-oss-120b comparison based on?
61 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 gpt-oss-120b's on 1. Where only one model has a result, the page says so instead of naming a winner.