DeepSeek V3.2 Exp vs GPT-5.6 Luna
GPT-5.6 Luna leads DeepSeek V3.2 Exp on the Artificial Analysis Intelligence Index, 37.3 to 16.6, and DeepSeek V3.2 Exp costs 2.9x less per output token ($0.41 vs $1.20 per million). Updated October 22, 2026; 46 benchmarks have a result for both.
DeepSeek V3.2 Exp or GPT-5.6 Luna: which to pick
Only GPT-5.6 Luna has a coding score, 58.0 on the Vibe Coding Index; DeepSeek V3.2 Exp has none, so the two cannot be ranked on coding.
Pick DeepSeek V3.2 Exp for output tokens 2.9x cheaper ($0.41 vs $1.20 per million) and work measured by Kagi LLM Benchmark or LMArena Text: science, where it leads.
- Shared results
- 46
- DeepSeek V3.2 Exp only
- 13
- GPT-5.6 Luna only
- 73
- Like-for-like categories
- 7 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 16.6.
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, 1255 against 1178.
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 Exp
DeepSeek V3.2 Exp $0.0005, GPT-5.6 Luna $0.0008 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
- DeepSeek V3.2 Exp not benchmarked, GPT-5.6 Luna 58.0 from 6 benchmarks.
- Widest category
- Coding, 21.1 points apart on average over 5 shared benchmarks.
- Widest single gap
- LMArena WebDev: HTML: DeepSeek V3.2 Exp 1281 against GPT-5.6 Luna 1520, 39.8 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 Exp $0.41 against GPT-5.6 Luna $1.20 per million output tokens, 2.9x apart.
- Context window
- DeepSeek V3.2 Exp 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 Exp | Bars | GPT-5.6 Luna | Ties |
|---|---|---|---|---|
| Coding | 0 | 5 | ||
| Intelligence | 0 | 2 | ||
| Reasoning | 1 | 5 | ||
| Math | 0 | 2 | ||
| Knowledge | 2 | 2 | ||
| Web design | 1 | 6 | ||
| General | 4 | 16 | ||
| All shared boards | 8 | 38 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index05
| Benchmark | DeepSeek V3.2 Exp | GPT-5.6 Luna | Leader |
|---|---|---|---|
| Aider polyglot | 74.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| LiveBench coding | Not measured | 73.6% | Only GPT-5.6 Luna measured |
| LMArena WebDev | 1272 | 1518 | GPT-5.6 Luna |
| AA Coding Index | Not measured | 71.4 | Only GPT-5.6 Luna measured |
| SWE-bench Verified (Vals AI) | Not measured | 93.0% | Only GPT-5.6 Luna measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 11.6% | Only GPT-5.6 Luna measured |
| Vibe Code Bench (Vals AI) | Not measured | 77.1% | Only GPT-5.6 Luna measured |
| 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) | 78.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| SciCode | Not measured | 53.6% | Only GPT-5.6 Luna measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.1% | Not measured | Only DeepSeek V3.2 Exp measured |
| FrontierCode | Not measured | 39.8% | Only GPT-5.6 Luna measured |
| WeirdML | 39.5% | 60.9% | GPT-5.6 Luna |
| ALE-Bench | Not measured | 1667.4 | Only GPT-5.6 Luna measured |
| LMArena Text: coding | 1438 | 1466 | GPT-5.6 Luna |
| LMArena Text: software and IT | 1438 | 1463 | GPT-5.6 Luna |
| LMArena WebDev: React | Not measured | 1508 | Only GPT-5.6 Luna measured |
| LMArena WebDev: HTML | 1281 | 1520 | GPT-5.6 Luna |
| LMArena WebDev: from an image | Not measured | 1517 | Only GPT-5.6 Luna measured |
| SciCode | Not measured | 6.2% | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 13 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3.2 Exp | GPT-5.6 Luna | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.6 | 37.3 | GPT-5.6 Luna |
| Epoch Capabilities Index | 144.9 | 156.5 | GPT-5.6 Luna |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index18 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | 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 | 33.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| APEX-Agents | Not measured | 43.0% | Only GPT-5.6 Luna measured |
| BALROG | Not measured | 45.6% | Only GPT-5.6 Luna measured |
| TheAgentCompany | 42.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| Vending-Bench 2 | Not measured | $4,095 | Only GPT-5.6 Luna measured |
| Berkeley Function Calling Leaderboard (BFCL) | 56.7% | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 23 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index15
| Benchmark | DeepSeek V3.2 Exp | 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) | 79.7% | 91.1% | GPT-5.6 Luna |
| Humanity's Last Exam (Artificial Analysis run) | 14.9% | 39.5% | GPT-5.6 Luna |
| 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 | 1429 | 1451 | GPT-5.6 Luna |
| LMArena Text: expert | 1422 | 1478 | GPT-5.6 Luna |
| LMArena Text: hard prompts in English | 1444 | 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 |
| Kagi LLM Benchmark | 52.2% | 49.1% | DeepSeek V3.2 Exp |
| 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 Exp | GPT-5.6 Luna | Leader |
|---|---|---|---|
| AA Math Index | 87.7 | Not measured | Only DeepSeek V3.2 Exp measured |
| AIME 2025 (Artificial Analysis run) | 87.7% | Not measured | Only DeepSeek V3.2 Exp 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 | 1422 | 1459 | GPT-5.6 Luna |
| LMArena Text: mathematical work | 1433 | 1470 | GPT-5.6 Luna |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index22
| Benchmark | DeepSeek V3.2 Exp | 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 |
| MMLU Pro (Artificial Analysis run) | 85.0% | Not measured | Only DeepSeek V3.2 Exp measured |
| SimpleQA Verified | Not measured | 41.0% | Only GPT-5.6 Luna measured |
| LMArena Text: science | 1443 | 1433 | DeepSeek V3.2 Exp |
| LMArena Text: medicine and healthcare | 1468 | 1399 | DeepSeek V3.2 Exp |
| LMArena Text: legal and government | 1419 | 1446 | GPT-5.6 Luna |
| LMArena Text: business and finance | 1412 | 1439 | GPT-5.6 Luna |
| FACTS Benchmark Suite | Not measured | 35.0% | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 5 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index16
| Benchmark | DeepSeek V3.2 Exp | GPT-5.6 Luna | Leader |
|---|---|---|---|
| Design Arena: websites | 1178 | 1255 | GPT-5.6 Luna |
| Design Arena: UI components | 1164 | 1248 | GPT-5.6 Luna |
| Design Arena: game dev | 1144 | 1194 | GPT-5.6 Luna |
| Design Arena: data viz | 1152 | 1211 | GPT-5.6 Luna |
| Design Arena: 3D | 1148 | 1121 | DeepSeek V3.2 Exp |
| Design Arena: all code categories | 1170 | 1231 | GPT-5.6 Luna |
| Design Arena: SVG | 1031 | 1194 | GPT-5.6 Luna |
| Design Arena: ASCII art | Not measured | 1142 | Only GPT-5.6 Luna measured |
| LiveBench design | Not measured | 76.0% | Only GPT-5.6 Luna measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index416
| Benchmark | DeepSeek V3.2 Exp | 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 | 54.1% | Not measured | Only DeepSeek V3.2 Exp measured |
| Long context reasoning (AA-LCR) | 72.3% | 83.7% | GPT-5.6 Luna |
| DTBench | 87.7% | 89.1% | GPT-5.6 Luna |
| LMCA | 29.1% | 48.5% | GPT-5.6 Luna |
| Fiction.LiveBench | 83.3% | Not measured | Only DeepSeek V3.2 Exp measured |
| CL-bench | 13.2% | Not measured | Only DeepSeek V3.2 Exp 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 | 9.5% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text | 1425 | 1431 | GPT-5.6 Luna |
| LMArena Text: instruction following | 1401 | 1437 | GPT-5.6 Luna |
| LMArena Text: creative writing | 1404 | 1396 | DeepSeek V3.2 Exp |
| LMArena Vision | Not measured | 1257 | Only GPT-5.6 Luna measured |
| LMArena Text: English | 1441 | 1441 | DeepSeek V3.2 Exp |
| LMArena Text: non-English | 1404 | 1417 | GPT-5.6 Luna |
| LMArena Text: Chinese | 1461 | 1470 | GPT-5.6 Luna |
| LMArena Text: German | 1440 | 1455 | GPT-5.6 Luna |
| LMArena Text: Japanese | 1331 | 1415 | GPT-5.6 Luna |
| LMArena Text: Korean | 1372 | 1415 | GPT-5.6 Luna |
| LMArena Text: French | 1434 | 1458 | GPT-5.6 Luna |
| LMArena Text: Spanish | 1441 | 1446 | GPT-5.6 Luna |
| LMArena Text: Russian | 1412 | 1427 | GPT-5.6 Luna |
| LMArena Text: Polish | 1435 | 1405 | DeepSeek V3.2 Exp |
| LMArena Text: multi-turn | 1422 | 1434 | GPT-5.6 Luna |
| LMArena Text: longer queries | 1418 | 1436 | GPT-5.6 Luna |
| LMArena Text: writing and language | 1407 | 1419 | GPT-5.6 Luna |
| LMArena Text: entertainment and media | 1407 | 1402 | DeepSeek V3.2 Exp |
| 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 8 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | 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 Exp 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 Exp | OpenAI GPT-5.6 Luna |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | September 29, 2025 | July 9, 2026 |
| Context window | 164K | 1.05M |
| Input $/Mtok | $0.27 | $0.20 |
| Output $/Mtok | $0.41 | $1.20 |
| Coding index | Not benchmarked | 58.0 |
| Independent measurements behind it | 1 | 6 |
| Cost per solved task | Not computable | $0.04 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 Exp better than GPT-5.6 Luna for coding?
Only GPT-5.6 Luna has a coding score, 58.0 on the Vibe Coding Index; DeepSeek V3.2 Exp has none, so the two cannot be ranked on coding. Pick DeepSeek V3.2 Exp for output tokens 2.9x cheaper ($0.41 vs $1.20 per million) and work measured by Kagi LLM Benchmark or LMArena Text: science, where it leads.
Which is cheaper, DeepSeek V3.2 Exp or GPT-5.6 Luna?
DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; GPT-5.6 Luna costs $0.20 and $1.20. Cost per solved task is known for GPT-5.6 Luna only.
Which has the bigger context window, DeepSeek V3.2 Exp or GPT-5.6 Luna?
GPT-5.6 Luna, with 1.05M tokens against 164K.
How many benchmarks is the DeepSeek V3.2 Exp vs GPT-5.6 Luna comparison based on?
46 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.2 Exp's coding index rests on 1 independent benchmark and GPT-5.6 Luna's on 6. Where only one model has a result, the page says so instead of naming a winner.