DeepSeek V3.2 vs GPT-6 Luna
GPT-6 Luna leads DeepSeek V3.2 on coding, 57.3 to 30.0 on the Vibe Coding Index, and GPT-6 Luna costs 1.8x less per output token ($0.50 vs $0.88 per million). Updated October 22, 2026; 49 benchmarks have a result for both.
DeepSeek V3.2 or GPT-6 Luna: which to pick
Pick GPT-6 Luna for coding: it scores 57.3 on the Vibe Coding Index against 30.0 for DeepSeek V3.2.
Pick DeepSeek V3.2 for work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
- Shared results
- 49
- DeepSeek V3.2 only
- 26
- GPT-6 Luna only
- 54
- 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
GPT-6 Luna
GPT-6 Luna scores 57.3 against 30.0.
3 and 5 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-6 Luna
GPT-6 Luna leads, 38.1 against 21.5.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only DeepSeek V3.2 has a GPQA Diamond result, 80.3%.
Directional only
Math
AIME
No call
Only DeepSeek V3.2 has a AIME result, 84.6%.
Directional only
Knowledge
MMLU Pro
No call
Only DeepSeek V3.2 has a MMLU Pro result, 84.9%.
Directional only
Web design
Design Arena: websites
GPT-6 Luna
GPT-6 Luna leads, 1271 against 1175.
Same board, both measured
General
Vals Index
No call
Only GPT-6 Luna has a Vals Index result, 52.3%.
Directional only
Long documents
Listed context window
GPT-6 Luna
GPT-6 Luna takes 1.05M tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-6 Luna
DeepSeek V3.2 $0.0007, GPT-6 Luna $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only DeepSeek V3.2 has the number, $0.99.
Directional only
At a glance
- Coding index
- DeepSeek V3.2 30.0 from 3 benchmarks, GPT-6 Luna 57.3 from 5 benchmarks.
- Widest category
- Coding, 31.5 points apart on average over 6 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): DeepSeek V3.2 5.1% against GPT-6 Luna 81.7%, 76.5 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 $0.88 against GPT-6 Luna $0.50 per million output tokens, 1.8x apart.
- Context window
- DeepSeek V3.2 takes 164K tokens, GPT-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-6 Luna | Ties |
|---|---|---|---|---|
| Coding | 2 | 4 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 2 | 4 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| Web design | 0 | 8 | ||
| General | 17 | 3 | ||
| All shared boards | 27 | 22 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index24
| Benchmark | DeepSeek V3.2 | GPT-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 | 84.5% | Only GPT-6 Luna measured |
| LMArena WebDev | 1362 | 1578 | GPT-6 Luna |
| AA Coding Index | 44.2 | Not measured | Only DeepSeek V3.2 measured |
| LiveCodeBench (Vals AI) | 80.7% | Not measured | Only DeepSeek V3.2 measured |
| SWE-bench Verified (Vals AI) | 67.6% | Not measured | Only DeepSeek V3.2 measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 13.6% | Only GPT-6 Luna measured |
| Vibe Code Bench (Vals AI) | 5.1% | 81.7% | GPT-6 Luna |
| Code migration (Vals AI) | Not measured | 42.5% | Only GPT-6 Luna measured |
| ProgramBench (Vals AI) | Not measured | 0.5% | Only GPT-6 Luna measured |
| IOI (Vals AI) | Not measured | 55.6% | Only GPT-6 Luna measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 73.0% | Only GPT-6 Luna measured |
| SWE-bench Multimodal (Vals AI) | Not measured | 37.5% | Only GPT-6 Luna measured |
| CyberBench (Vals AI) | Not measured | 76.3% | Only GPT-6 Luna measured |
| LiveCodeBench (Artificial Analysis run) | 86.2% | Not measured | Only DeepSeek V3.2 measured |
| SciCode | Not measured | 54.6% | Only GPT-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 | 42.4% | Only GPT-6 Luna measured |
| ALE-Bench | Not measured | 1576.9 | Only GPT-6 Luna measured |
| LMArena Text: coding | 1453 | 1437 | DeepSeek V3.2 |
| LMArena Text: software and IT | 1449 | 1423 | DeepSeek V3.2 |
| LMArena WebDev: React | 1350 | 1573 | GPT-6 Luna |
| LMArena WebDev: HTML | 1371 | 1595 | GPT-6 Luna |
| LMArena WebDev: from an image | Not measured | 1571 | Only GPT-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 | GPT-6 Luna | Leader |
|---|---|---|---|
| AA Intelligence Index | 21.5 | 38.1 | GPT-6 Luna |
| Epoch Capabilities Index | 146.1 | 156.8 | GPT-6 Luna |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.2 | GPT-6 Luna | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | Not measured | 1191 | Only GPT-6 Luna measured |
| Design Arena: agents building full-stack apps | Not measured | 1158 | Only GPT-6 Luna measured |
| Design Arena: agents building mobile apps | Not measured | 1134 | Only GPT-6 Luna measured |
| LiveBench research | Not measured | 43.5% | Only GPT-6 Luna measured |
| LiveBench browser | Not measured | 72.0% | Only GPT-6 Luna measured |
| LiveBench long agentic | Not measured | 48.1% | Only GPT-6 Luna measured |
| SREBench (Vals AI) | Not measured | 2.7% | Only GPT-6 Luna measured |
| Finance Agent (Vals AI) | Not measured | 49.9% | Only GPT-6 Luna measured |
| Tax Agent Bench (Vals AI) | Not measured | 19.9% | Only GPT-6 Luna measured |
| Legal Research Bench (Vals AI) | Not measured | 30.3% | Only GPT-6 Luna measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 4.2% | Only GPT-6 Luna measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 68.5% | Only GPT-6 Luna measured |
| BioMysteryBench (Vals AI) | Not measured | 61.5% | Only GPT-6 Luna measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 4.3% | Only GPT-6 Luna measured |
| Vals Science Index | Not measured | 28.4% | Only GPT-6 Luna measured |
| τ²-Bench Telecom | 90.6% | Not measured | Only DeepSeek V3.2 measured |
| APEX-Agents | 21.3% | 44.3% | GPT-6 Luna |
| SpireClimb | Not measured | 0.0% | Only GPT-6 Luna measured |
| Vending-Bench 2 | $1,034 | Not measured | Only DeepSeek V3.2 measured |
| Neither model has a result on the other 22 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index24
| Benchmark | DeepSeek V3.2 | GPT-6 Luna | Leader |
|---|---|---|---|
| GPQA Diamond | 80.3% | Not measured | Only DeepSeek V3.2 measured |
| LiveBench reasoning | Not measured | 81.8% | Only GPT-6 Luna measured |
| MysteryMechanism (Vals AI) | Not measured | 19.4% | Only GPT-6 Luna measured |
| GPQA Diamond (Artificial Analysis run) | 84.0% | Not measured | Only DeepSeek V3.2 measured |
| Humanity's Last Exam (Artificial Analysis run) | 24.6% | 38.5% | GPT-6 Luna |
| GPQA Diamond (Epoch AI run) | Not measured | 90.5% | Only GPT-6 Luna measured |
| ARC-AGI | 57.0% | 86.7% | GPT-6 Luna |
| ARC-AGI-2 | 4.0% | 59.3% | GPT-6 Luna |
| Chess puzzles | Not measured | 36.0% | Only GPT-6 Luna measured |
| Mystery game puzzles | Not measured | 7.0% | Only GPT-6 Luna measured |
| LMArena Text: hard prompts | 1434 | 1410 | DeepSeek V3.2 |
| LMArena Text: expert | 1436 | 1442 | GPT-6 Luna |
| LMArena Text: hard prompts in English | 1446 | 1410 | DeepSeek V3.2 |
| LMArena Vision: diagrams | Not measured | 1245 | Only GPT-6 Luna measured |
| LMArena Vision: homework | Not measured | 1261 | Only GPT-6 Luna measured |
| Neither model has a result on the other 7 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V3.2 | GPT-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 | 98.9% | Only GPT-6 Luna measured |
| FrontierMath, tiers 1 to 3 | Not measured | 78.9% | Only GPT-6 Luna measured |
| FrontierMath, tier 4 | Not measured | 56.1% | Only GPT-6 Luna measured |
| ProofBench | Not measured | 64.0% | Only GPT-6 Luna measured |
| LMArena Text: math | 1435 | 1409 | DeepSeek V3.2 |
| LMArena Text: mathematical work | 1440 | 1427 | DeepSeek V3.2 |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | DeepSeek V3.2 | GPT-6 Luna | Leader |
|---|---|---|---|
| MMLU Pro | 84.9% | Not measured | Only DeepSeek V3.2 measured |
| LegalBench (Vals AI) | 76.1% | Not measured | Only DeepSeek V3.2 measured |
| CaseLaw (Vals AI) | 55.4% | Not measured | Only DeepSeek V3.2 measured |
| CorpFin (Vals AI) | 51.0% | Not measured | Only DeepSeek V3.2 measured |
| TaxEval (Vals AI) | 68.2% | Not measured | Only DeepSeek V3.2 measured |
| MedQA (Vals AI) | 93.9% | Not measured | Only DeepSeek V3.2 measured |
| MedCode (Vals AI) | Not measured | 44.7% | Only GPT-6 Luna measured |
| MMLU Pro (Artificial Analysis run) | 86.2% | Not measured | Only DeepSeek V3.2 measured |
| SimpleQA Verified | Not measured | 41.4% | Only GPT-6 Luna measured |
| LMArena Text: science | 1446 | 1386 | DeepSeek V3.2 |
| LMArena Text: medicine and healthcare | 1440 | 1352 | DeepSeek V3.2 |
| LMArena Text: legal and government | 1425 | 1400 | DeepSeek V3.2 |
| LMArena Text: business and finance | 1410 | 1388 | DeepSeek V3.2 |
| FACTS Benchmark Suite | Not measured | 63.3% | Only GPT-6 Luna measured |
| Neither model has a result on the other 4 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index08
| Benchmark | DeepSeek V3.2 | GPT-6 Luna | Leader |
|---|---|---|---|
| Design Arena: websites | 1175 | 1271 | GPT-6 Luna |
| Design Arena: UI components | 1146 | 1295 | GPT-6 Luna |
| Design Arena: game dev | 1131 | 1257 | GPT-6 Luna |
| Design Arena: data viz | 1158 | 1216 | GPT-6 Luna |
| Design Arena: 3D | 1132 | 1218 | GPT-6 Luna |
| Design Arena: all code categories | 1163 | 1270 | GPT-6 Luna |
| Design Arena: SVG | 1032 | 1252 | GPT-6 Luna |
| Design Arena: ASCII art | 1083 | 1153 | GPT-6 Luna |
| LiveBench design | Not measured | 82.5% | Only GPT-6 Luna measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index173
| Benchmark | DeepSeek V3.2 | GPT-6 Luna | Leader |
|---|---|---|---|
| Vals Index | Not measured | 52.3% | Only GPT-6 Luna measured |
| LiveBench data analysis | Not measured | 73.4% | Only GPT-6 Luna measured |
| Public Benefits Bench (Vals AI) | Not measured | 57.6% | Only GPT-6 Luna measured |
| SAGE (Vals AI) | Not measured | 48.1% | Only GPT-6 Luna measured |
| MedScribe (Vals AI) | Not measured | 83.7% | Only GPT-6 Luna measured |
| IFBench | 60.7% | Not measured | Only DeepSeek V3.2 measured |
| Long context reasoning (AA-LCR) | 73.3% | 83.3% | GPT-6 Luna |
| DTBench | 85.6% | 90.1% | GPT-6 Luna |
| LMCA | 28.8% | 44.5% | GPT-6 Luna |
| CL-bench | 12.4% | Not measured | Only DeepSeek V3.2 measured |
| Furniture assembly | Not measured | 44.2% | Only GPT-6 Luna measured |
| CL-bench Life | 7.4% | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text | 1425 | 1389 | DeepSeek V3.2 |
| LMArena Text: instruction following | 1413 | 1408 | DeepSeek V3.2 |
| LMArena Text: creative writing | 1400 | 1363 | DeepSeek V3.2 |
| LMArena Vision | Not measured | 1216 | Only GPT-6 Luna measured |
| LMArena Text: English | 1439 | 1393 | DeepSeek V3.2 |
| LMArena Text: non-English | 1409 | 1381 | DeepSeek V3.2 |
| LMArena Text: Chinese | 1455 | 1427 | DeepSeek V3.2 |
| LMArena Text: German | 1413 | 1376 | DeepSeek V3.2 |
| LMArena Text: Japanese | 1374 | 1369 | DeepSeek V3.2 |
| LMArena Text: Korean | 1370 | 1359 | DeepSeek V3.2 |
| LMArena Text: French | 1429 | 1408 | DeepSeek V3.2 |
| LMArena Text: Spanish | 1424 | 1391 | DeepSeek V3.2 |
| LMArena Text: Russian | 1424 | 1392 | DeepSeek V3.2 |
| LMArena Text: Polish | 1419 | 1383 | DeepSeek V3.2 |
| LMArena Text: multi-turn | 1427 | 1392 | DeepSeek V3.2 |
| LMArena Text: longer queries | 1428 | 1408 | DeepSeek V3.2 |
| LMArena Text: writing and language | 1407 | 1384 | DeepSeek V3.2 |
| LMArena Text: entertainment and media | 1395 | 1364 | DeepSeek V3.2 |
| LMArena Vision: English | Not measured | 1204 | Only GPT-6 Luna measured |
| LMArena Vision: Chinese | Not measured | 1266 | Only GPT-6 Luna measured |
| LMArena Vision: reading text | Not measured | 1225 | Only GPT-6 Luna measured |
| LMArena Vision: creative writing | Not measured | 1205 | Only GPT-6 Luna measured |
| Neither model has a result on the other 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.2 | GPT-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-6 Luna costs less
Repository review
50K input + 3K output tokens
GPT-6 Luna costs less
Agent loop
200K input + 10K output tokens
GPT-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-6 Luna |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | December 1, 2025 | September 22, 2026 |
| Context window | 164K | 1.05M |
| Input $/Mtok | $0.29 | $0.10 |
| Output $/Mtok | $0.88 | $0.50 |
| Coding index | 30.0 | 57.3 |
| Independent measurements behind it | 3 | 5 |
| Cost per solved task | $0.99 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 better than GPT-6 Luna for coding?
Pick GPT-6 Luna for coding: it scores 57.3 on the Vibe Coding Index against 30.0 for DeepSeek V3.2. Pick DeepSeek V3.2 for work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
Which is cheaper, DeepSeek V3.2 or GPT-6 Luna?
DeepSeek V3.2 costs $0.29 per million input tokens and $0.88 per million output tokens; GPT-6 Luna costs $0.10 and $0.50. Cost per solved task is known for DeepSeek V3.2 only.
Which has the bigger context window, DeepSeek V3.2 or GPT-6 Luna?
GPT-6 Luna, with 1.05M tokens against 164K.
How many benchmarks is the DeepSeek V3.2 vs GPT-6 Luna comparison based on?
49 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.2's coding index rests on 3 independent benchmarks and GPT-6 Luna's on 5. Where only one model has a result, the page says so instead of naming a winner.