DeepSeek V3.2 vs GPT-5.6 Luna
Updated October 21, 2026. 58 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GPT-5.6 Luna for coding: it scores 61.5 on the Vibe Coding Index against 54.4 for DeepSeek V3.2.
Pick DeepSeek V3.2 when output tokens cost 2.9x less, or when what matters is LMArena Text: science or LMArena Text: medicine and healthcare, where it leads.
- Shared results
- 58
- DeepSeek V3.2 only
- 17
- GPT-5.6 Luna only
- 58
- Like-for-like categories
- 8 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 54.4.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
GPT-5.6 Luna
GPT-5.6 Luna leads, 37.3 against 21.5.
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
GPT-5.6 Luna
GPT-5.6 Luna leads, 91.7% against 80.3%.
Same board, both measured
Math
AIME
No call
Only DeepSeek V3.2 has a AIME result, 84.6%.
Directional only
Knowledge
MMLU Pro
GPT-5.6 Luna
GPT-5.6 Luna leads, 86.0% against 84.9%.
Same board, both measured
Web design
Design Arena: websites
GPT-5.6 Luna
GPT-5.6 Luna leads, 1256 against 1175.
Same board, both measured
Long documents
Listed context window
GPT-5.6 Luna
GPT-5.6 Luna takes 1.05M tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.2
DeepSeek V3.2 $0.0005, GPT-5.6 Luna $0.0008 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5.6 Luna
DeepSeek V3.2 $0.99, GPT-5.6 Luna $0.04.
Published attempt cost and pass rate
At a glance
- Coding index
- DeepSeek V3.2 54.4 from 2 benchmarks, GPT-5.6 Luna 61.5 from 2 benchmarks.
- Widest category
- Coding, 25.3 points apart on average over 8 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): DeepSeek V3.2 5.1% against GPT-5.6 Luna 77.1%, 72.0 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 $0.42 against GPT-5.6 Luna $1.20 per million output tokens, 2.9x apart.
- Context window
- DeepSeek V3.2 takes 164K 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 | DeepSeek V3.2 | Bars | GPT-5.6 Luna | Ties |
|---|---|---|---|---|
| Coding | 0 | 8 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 2 | ||
| Reasoning | 0 | 8 | ||
| Math | 0 | 2 | ||
| Knowledge | 2 | 6 | ||
| Web design | 1 | 7 | ||
| General | 2 | 18 | ||
| All shared boards | 5 | 53 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index08
| Benchmark | DeepSeek V3.2 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| SWE-bench Verified | 70.0% | Not measured | Only DeepSeek V3.2 measured |
| SWE-bench Multilingual | 59.0% | Not measured | Only DeepSeek V3.2 measured |
| LiveBench coding | Not measured | 73.6% | Only GPT-5.6 Luna measured |
| LMArena WebDev | 1362 | 1518 | GPT-5.6 Luna |
| AA Coding Index | 44.2 | 71.4 | GPT-5.6 Luna |
| LiveCodeBench (Vals AI) | 80.7% | Not measured | Only DeepSeek V3.2 measured |
| SWE-bench Verified (Vals AI) | 67.6% | 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) | 5.1% | 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) | Not measured | 61.8% | Only GPT-5.6 Luna measured |
| 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 |
| LiveCodeBench (Artificial Analysis run) | 86.2% | Not measured | Only DeepSeek V3.2 measured |
| SciCode | Not measured | 53.6% | Only GPT-5.6 Luna measured |
| Terminal-Bench Hard (Artificial Analysis run) | 35.6% | Not measured | Only DeepSeek V3.2 measured |
| Terminal-Bench | 39.6% | Not measured | Only DeepSeek V3.2 measured |
| FrontierCode | Not measured | 39.8% | Only GPT-5.6 Luna measured |
| WeirdML | Not measured | 60.9% | Only GPT-5.6 Luna measured |
| ALE-Bench | Not measured | 1667.4 | Only GPT-5.6 Luna measured |
| LMArena Text: coding | 1453 | 1466 | GPT-5.6 Luna |
| LMArena Text: software and IT | 1449 | 1463 | GPT-5.6 Luna |
| LMArena WebDev: React | 1350 | 1508 | GPT-5.6 Luna |
| LMArena WebDev: HTML | 1371 | 1520 | GPT-5.6 Luna |
| LMArena WebDev: from an image | Not measured | 1517 | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 10 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3.2 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| AA Intelligence Index | 21.5 | 37.3 | GPT-5.6 Luna |
| Epoch Capabilities Index | 146.1 | 156.5 | GPT-5.6 Luna |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3.2 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 42.1 | Only GPT-5.6 Luna 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 | 90.6% | Not measured | Only DeepSeek V3.2 measured |
| APEX-Agents | 21.3% | 43.0% | GPT-5.6 Luna |
| BALROG | Not measured | 45.6% | Only GPT-5.6 Luna measured |
| Vending-Bench 2 | $1,034 | $4,095 | GPT-5.6 Luna |
| Neither model has a result on the other 23 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index08
| Benchmark | DeepSeek V3.2 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| GPQA Diamond | 80.3% | 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) | 84.0% | 91.1% | GPT-5.6 Luna |
| Humanity's Last Exam (Artificial Analysis run) | 24.6% | 39.5% | GPT-5.6 Luna |
| GPQA Diamond (Epoch AI run) | Not measured | 91.6% | Only GPT-5.6 Luna measured |
| ARC-AGI | 57.0% | 88.0% | GPT-5.6 Luna |
| ARC-AGI-2 | 4.0% | 59.5% | GPT-5.6 Luna |
| 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 | 1434 | 1451 | GPT-5.6 Luna |
| LMArena Text: expert | 1436 | 1478 | GPT-5.6 Luna |
| LMArena Text: hard prompts in English | 1446 | 1460 | GPT-5.6 Luna |
| 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 | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3.2 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| AIME | 84.6% | Not measured | Only DeepSeek V3.2 measured |
| MGSM (Vals AI) | 86.0% | Not measured | Only DeepSeek V3.2 measured |
| AA Math Index | 92.0 | Not measured | Only DeepSeek V3.2 measured |
| AIME 2025 (Artificial Analysis run) | 92.0% | Not measured | Only DeepSeek V3.2 measured |
| 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 | 1435 | 1459 | GPT-5.6 Luna |
| LMArena Text: mathematical work | 1440 | 1470 | GPT-5.6 Luna |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index26
| Benchmark | DeepSeek V3.2 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| MMLU Pro | 84.9% | 86.0% | GPT-5.6 Luna |
| LegalBench (Vals AI) | 76.1% | 84.0% | GPT-5.6 Luna |
| CaseLaw (Vals AI) | 55.4% | Not measured | Only DeepSeek V3.2 measured |
| CorpFin (Vals AI) | 51.0% | 64.2% | GPT-5.6 Luna |
| TaxEval (Vals AI) | 68.2% | 76.2% | GPT-5.6 Luna |
| MortgageTax (Vals AI) | Not measured | 67.3% | Only GPT-5.6 Luna measured |
| MedQA (Vals AI) | 93.9% | Not measured | Only DeepSeek V3.2 measured |
| MedCode (Vals AI) | Not measured | 42.4% | Only GPT-5.6 Luna measured |
| MMLU Pro (Artificial Analysis run) | 86.2% | Not measured | Only DeepSeek V3.2 measured |
| SimpleQA Verified | Not measured | 41.0% | Only GPT-5.6 Luna measured |
| LMArena Text: science | 1446 | 1433 | DeepSeek V3.2 |
| LMArena Text: medicine and healthcare | 1440 | 1399 | DeepSeek V3.2 |
| LMArena Text: legal and government | 1425 | 1446 | GPT-5.6 Luna |
| LMArena Text: business and finance | 1410 | 1439 | GPT-5.6 Luna |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index17
| Benchmark | DeepSeek V3.2 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| Design Arena: websites | 1175 | 1256 | GPT-5.6 Luna |
| Design Arena: UI components | 1149 | 1250 | GPT-5.6 Luna |
| Design Arena: game dev | 1132 | 1196 | GPT-5.6 Luna |
| Design Arena: data viz | 1158 | 1212 | GPT-5.6 Luna |
| Design Arena: 3D | 1133 | 1121 | DeepSeek V3.2 |
| Design Arena: all code categories | 1164 | 1231 | GPT-5.6 Luna |
| Design Arena: SVG | 1032 | 1193 | GPT-5.6 Luna |
| Design Arena: ASCII art | 1082 | 1144 | GPT-5.6 Luna |
| LiveBench design | Not measured | 76.0% | Only GPT-5.6 Luna measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index218
| Benchmark | DeepSeek V3.2 | 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) | Not measured | 84.4% | Only GPT-5.6 Luna measured |
| Vals Multimodal Index | Not measured | 69.1% | Only GPT-5.6 Luna measured |
| IFBench | 60.7% | Not measured | Only DeepSeek V3.2 measured |
| Long context reasoning (AA-LCR) | 73.3% | 83.7% | GPT-5.6 Luna |
| DTBench | 85.6% | 89.1% | GPT-5.6 Luna |
| LMCA | 28.8% | 48.5% | GPT-5.6 Luna |
| CL-bench | 12.4% | Not measured | Only DeepSeek V3.2 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 |
| CL-bench Life | 7.4% | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text | 1425 | 1431 | GPT-5.6 Luna |
| LMArena Text: instruction following | 1413 | 1437 | GPT-5.6 Luna |
| LMArena Text: creative writing | 1400 | 1396 | DeepSeek V3.2 |
| LMArena Vision | Not measured | 1257 | Only GPT-5.6 Luna measured |
| LMArena Text: English | 1439 | 1441 | GPT-5.6 Luna |
| LMArena Text: non-English | 1409 | 1417 | GPT-5.6 Luna |
| LMArena Text: Chinese | 1455 | 1470 | GPT-5.6 Luna |
| LMArena Text: German | 1413 | 1455 | GPT-5.6 Luna |
| LMArena Text: Japanese | 1374 | 1415 | GPT-5.6 Luna |
| LMArena Text: Korean | 1370 | 1415 | GPT-5.6 Luna |
| LMArena Text: French | 1429 | 1458 | GPT-5.6 Luna |
| LMArena Text: Spanish | 1424 | 1446 | GPT-5.6 Luna |
| LMArena Text: Russian | 1424 | 1427 | GPT-5.6 Luna |
| LMArena Text: Polish | 1419 | 1405 | DeepSeek V3.2 |
| LMArena Text: multi-turn | 1427 | 1434 | GPT-5.6 Luna |
| LMArena Text: longer queries | 1428 | 1436 | GPT-5.6 Luna |
| LMArena Text: writing and language | 1407 | 1419 | GPT-5.6 Luna |
| LMArena Text: entertainment and media | 1395 | 1402 | GPT-5.6 Luna |
| 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 9 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.2 | 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
DeepSeek V3.2 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 | DeepSeek V3.2 | OpenAI GPT-5.6 Luna |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | December 1, 2025 | July 9, 2026 |
| Context window | 164K | 1.05M |
| Input $/Mtok | $0.28 | $0.20 |
| Output $/Mtok | $0.42 | $1.20 |
| Coding index | 54.4 | 61.5 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | $0.99 | $0.04 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 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 54.4 for DeepSeek V3.2. Pick DeepSeek V3.2 when output tokens cost 2.9x less, or when what matters is LMArena Text: science or LMArena Text: medicine and healthcare, where it leads.
Which is cheaper, DeepSeek V3.2 or GPT-5.6 Luna?
DeepSeek V3.2 costs $0.28 per million input tokens and $0.42 per million output tokens; GPT-5.6 Luna costs $0.20 and $1.20. Per solved SWE-bench Verified task, DeepSeek V3.2 costs $0.99 and GPT-5.6 Luna $0.04.
Which has the bigger context window, DeepSeek V3.2 or GPT-5.6 Luna?
GPT-5.6 Luna, with 1.05M tokens against 164K.
How many benchmarks is the DeepSeek V3.2 vs GPT-5.6 Luna comparison based on?
58 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2's coding index rests on 2 independent benchmarks and GPT-5.6 Luna's on 2. Where only one model has a result, the page says so instead of naming a winner.