DeepSeek V3.1 vs GPT-5.6 Luna
GPT-5.6 Luna leads DeepSeek V3.1 on the Artificial Analysis Intelligence Index, 37.3 to 13.7, and GPT-5.6 Luna costs 1.3x less per output token ($1.20 vs $1.50 per million). Updated October 22, 2026; 11 benchmarks have a result for both.
DeepSeek V3.1 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 V3.1 has none, so the two cannot be ranked on coding.
Pick DeepSeek V3.1 for work measured by Kagi LLM Benchmark, where it leads.
- Shared results
- 11
- DeepSeek V3.1 only
- 0
- GPT-5.6 Luna only
- 109
- Like-for-like categories
- 4 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 13.7.
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 1122.
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
GPT-5.6 Luna
DeepSeek V3.1 $0.0013, 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 V3.1 not benchmarked, GPT-5.6 Luna 58.0 from 6 benchmarks.
- Widest category
- Intelligence, 23.6 points apart on average over 1 shared benchmark.
- Widest single gap
- Design Arena: SVG: DeepSeek V3.1 967 against GPT-5.6 Luna 1193, 28.3 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.1 $1.50 against GPT-5.6 Luna $1.20 per million output tokens, 1.3x apart.
- Context window
- DeepSeek V3.1 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 V3.1 | Bars | GPT-5.6 Luna | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Reasoning | 1 | 0 | ||
| Web design | 0 | 7 | ||
| General | 0 | 2 | ||
| All shared boards | 1 | 10 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index23 boards, none shared
| Benchmark | DeepSeek V3.1 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 73.6% | Only GPT-5.6 Luna measured |
| LMArena WebDev | Not measured | 1518 | Only GPT-5.6 Luna measured |
| 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 |
| SciCode | Not measured | 53.6% | Only GPT-5.6 Luna 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 | 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 | Not measured | 1508 | Only GPT-5.6 Luna measured |
| LMArena WebDev: HTML | Not measured | 1520 | Only GPT-5.6 Luna measured |
| 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 20 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.1 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.7 | 37.3 | GPT-5.6 Luna |
| Epoch Capabilities Index | Not measured | 156.5 | Only GPT-5.6 Luna measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index15 boards, none shared
| Benchmark | DeepSeek V3.1 | 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 |
| APEX-Agents | Not measured | 43.0% | Only GPT-5.6 Luna measured |
| BALROG | Not measured | 45.6% | Only GPT-5.6 Luna measured |
| Vending-Bench 2 | Not measured | $4,095 | 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 index10
| Benchmark | DeepSeek V3.1 | 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) | Not measured | 91.1% | Only GPT-5.6 Luna measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 39.5% | Only GPT-5.6 Luna measured |
| 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 |
| Kagi LLM Benchmark | 53.2% | 49.1% | DeepSeek V3.1 |
| Neither model has a result on the other 5 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | DeepSeek V3.1 | 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 8 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | DeepSeek V3.1 | 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 |
| FACTS Benchmark Suite | Not measured | 35.0% | 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 index07
| Benchmark | DeepSeek V3.1 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| Design Arena: websites | 1122 | 1255 | GPT-5.6 Luna |
| Design Arena: UI components | 1083 | 1248 | GPT-5.6 Luna |
| Design Arena: game dev | 1084 | 1195 | GPT-5.6 Luna |
| Design Arena: data viz | 1096 | 1212 | GPT-5.6 Luna |
| Design Arena: 3D | 1077 | 1121 | GPT-5.6 Luna |
| Design Arena: all code categories | 1112 | 1230 | GPT-5.6 Luna |
| Design Arena: SVG | 967 | 1193 | 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 index02
| Benchmark | DeepSeek V3.1 | 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 |
| Long context reasoning (AA-LCR) | Not measured | 83.7% | Only GPT-5.6 Luna measured |
| DTBench | 82.7% | 89.1% | GPT-5.6 Luna |
| LMCA | 24.3% | 48.5% | GPT-5.6 Luna |
| 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 |
| EQ-Bench 4 | Not measured | 1156 | 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 | DeepSeek V3.1 | 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-5.6 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 | DeepSeek V3.1 | OpenAI GPT-5.6 Luna |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | August 21, 2025 | July 9, 2026 |
| Context window | 164K | 1.05M |
| Input $/Mtok | $0.55 | $0.20 |
| Output $/Mtok | $1.50 | $1.20 |
| Coding index | Not benchmarked | 58.0 |
| Independent measurements behind it | 0 | 6 |
| Cost per solved task | Not computable | $0.04 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.1 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 V3.1 has none, so the two cannot be ranked on coding. Pick DeepSeek V3.1 for work measured by Kagi LLM Benchmark, where it leads.
Which is cheaper, DeepSeek V3.1 or GPT-5.6 Luna?
DeepSeek V3.1 costs $0.55 per million input tokens and $1.50 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 V3.1 or GPT-5.6 Luna?
GPT-5.6 Luna, with 1.05M tokens against 164K.
How many benchmarks is the DeepSeek V3.1 vs GPT-5.6 Luna comparison based on?
11 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.1's coding index rests on 0 independent benchmarks and GPT-5.6 Luna's on 6. Where only one model has a result, the page says so instead of naming a winner.