DeepSeek V4.1 Flash 1017 vs GPT-5.6 Luna
DeepSeek V4.1 Flash 1017 leads GPT-5.6 Luna on coding, 70.3 to 58.0 on the Vibe Coding Index, and DeepSeek V4.1 Flash 1017 costs 2.0x less per output token ($0.60 vs $1.20 per million). Updated October 22, 2026; 36 benchmarks have a result for both.
DeepSeek V4.1 Flash 1017 or GPT-5.6 Luna: which to pick
Pick DeepSeek V4.1 Flash 1017 for coding: it scores 70.3 on the Vibe Coding Index against 58.0 for GPT-5.6 Luna, though on 4 benchmarks against 6.
Pick GPT-5.6 Luna for a larger context window (1.05M against 1.05M tokens) and work measured by Code migration (Vals AI) or ALE-Bench, where it leads.
- Shared results
- 36
- DeepSeek V4.1 Flash 1017 only
- 3
- GPT-5.6 Luna only
- 83
- 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
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 scores 70.3 against 58.0.
4 and 6 benchmarks behind the scores
Intelligence
AA Intelligence Index
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 leads, 39.5 against 37.3.
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
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 leads, 1311 against 1255.
Same board, both measured
General
Vals Index
No call
Only DeepSeek V4.1 Flash 1017 has a Vals Index result, 61.3%.
Directional only
Long documents
Listed context window
GPT-5.6 Luna
GPT-5.6 Luna takes 1.05M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 $0.0004, 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 V4.1 Flash 1017 70.3 from 4 benchmarks, GPT-5.6 Luna 58.0 from 6 benchmarks.
- Widest category
- Web design, 13.9 points apart on average over 8 shared benchmarks.
- Widest single gap
- Design Arena: 3D: DeepSeek V4.1 Flash 1017 1358 against GPT-5.6 Luna 1121, 29.6 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4.1 Flash 1017 $0.60 against GPT-5.6 Luna $1.20 per million output tokens, 2.0x apart.
- Context window
- DeepSeek V4.1 Flash 1017 takes 1.05M 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 V4.1 Flash 1017 | Bars | GPT-5.6 Luna | Ties |
|---|---|---|---|---|
| Coding | 7 | 2 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 6 | 1 | ||
| Reasoning | 5 | 0 | ||
| Math | 1 | 2 | ||
| Knowledge | 1 | 0 | ||
| Web design | 8 | 0 | ||
| General | 1 | 1 | ||
| All shared boards | 30 | 6 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index72
| Benchmark | DeepSeek V4.1 Flash 1017 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| LiveBench coding | 96.0% | 73.6% | DeepSeek V4.1 Flash 1017 |
| LMArena WebDev | 1664 | 1518 | DeepSeek V4.1 Flash 1017 |
| 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) | 31.8% | 11.6% | DeepSeek V4.1 Flash 1017 |
| Vibe Code Bench (Vals AI) | 87.7% | 77.1% | DeepSeek V4.1 Flash 1017 |
| Code migration (Vals AI) | 44.2% | 44.5% | GPT-5.6 Luna |
| 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 | 55.9% | 53.6% | DeepSeek V4.1 Flash 1017 |
| FrontierCode | Not measured | 39.8% | Only GPT-5.6 Luna measured |
| WeirdML | Not measured | 60.9% | Only GPT-5.6 Luna measured |
| ALE-Bench | 1622.0 | 1667.4 | GPT-5.6 Luna |
| 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 | 1667 | 1508 | DeepSeek V4.1 Flash 1017 |
| LMArena WebDev: HTML | 1687 | 1520 | DeepSeek V4.1 Flash 1017 |
| 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 16 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4.1 Flash 1017 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| AA Intelligence Index | 39.5 | 37.3 | DeepSeek V4.1 Flash 1017 |
| Epoch Capabilities Index | Not measured | 156.5 | Only GPT-5.6 Luna measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index61
| Benchmark | DeepSeek V4.1 Flash 1017 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 42.1 | Only GPT-5.6 Luna measured |
| Design Arena: spreadsheets | 1245 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench research | 79.5% | 51.0% | DeepSeek V4.1 Flash 1017 |
| LiveBench browser | 76.0% | 68.0% | DeepSeek V4.1 Flash 1017 |
| LiveBench long agentic | 50.9% | 30.4% | DeepSeek V4.1 Flash 1017 |
| SREBench (Vals AI) | Not measured | 1.5% | Only GPT-5.6 Luna measured |
| Finance Agent (Vals AI) | 57.0% | 55.0% | DeepSeek V4.1 Flash 1017 |
| Tax Agent Bench (Vals AI) | 44.4% | 24.8% | DeepSeek V4.1 Flash 1017 |
| Legal Research Bench (Vals AI) | 45.2% | 36.5% | DeepSeek V4.1 Flash 1017 |
| Harvey's Legal Agent Benchmark (Vals AI) | 14.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Excel Modeling Benchmark (Vals AI) | 61.8% | 67.1% | GPT-5.6 Luna |
| 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 24 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | DeepSeek V4.1 Flash 1017 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 91.7% | Only GPT-5.6 Luna measured |
| LiveBench reasoning | 89.1% | 85.6% | DeepSeek V4.1 Flash 1017 |
| 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) | 43.3% | 39.5% | DeepSeek V4.1 Flash 1017 |
| GPQA Diamond (Epoch AI run) | 93.2% | 91.6% | DeepSeek V4.1 Flash 1017 |
| 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 | 46.0% | 43.0% | DeepSeek V4.1 Flash 1017 |
| Mystery game puzzles | 49.0% | 21.0% | DeepSeek V4.1 Flash 1017 |
| 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 | Not measured | 49.1% | 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 index12
| Benchmark | DeepSeek V4.1 Flash 1017 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| OTIS Mock AIME | 99.4% | 98.3% | DeepSeek V4.1 Flash 1017 |
| FrontierMath, tiers 1 to 3 | 71.2% | 82.1% | GPT-5.6 Luna |
| FrontierMath, tier 4 | 46.3% | 61.0% | GPT-5.6 Luna |
| 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 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4.1 Flash 1017 | 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 | 42.5% | 41.0% | DeepSeek V4.1 Flash 1017 |
| 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 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | DeepSeek V4.1 Flash 1017 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| Design Arena: websites | 1311 | 1255 | DeepSeek V4.1 Flash 1017 |
| Design Arena: UI components | 1303 | 1248 | DeepSeek V4.1 Flash 1017 |
| Design Arena: game dev | 1357 | 1194 | DeepSeek V4.1 Flash 1017 |
| Design Arena: data viz | 1330 | 1211 | DeepSeek V4.1 Flash 1017 |
| Design Arena: 3D | 1358 | 1121 | DeepSeek V4.1 Flash 1017 |
| Design Arena: all code categories | 1332 | 1231 | DeepSeek V4.1 Flash 1017 |
| Design Arena: SVG | Not measured | 1194 | Only GPT-5.6 Luna measured |
| Design Arena: ASCII art | 1242 | 1142 | DeepSeek V4.1 Flash 1017 |
| LiveBench design | 83.4% | 76.0% | DeepSeek V4.1 Flash 1017 |
GeneralLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | DeepSeek V4.1 Flash 1017 | GPT-5.6 Luna | Leader |
|---|---|---|---|
| Vals Index | 61.3% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench data analysis | 79.5% | 78.0% | DeepSeek V4.1 Flash 1017 |
| 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) | 82.7% | 83.7% | GPT-5.6 Luna |
| DTBench | Not measured | 89.1% | Only GPT-5.6 Luna measured |
| LMCA | Not measured | 48.5% | Only GPT-5.6 Luna 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 |
| 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 |
| Neither model has a result on the other 11 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | 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 V4.1 Flash 1017 costs less
Repository review
50K input + 3K output tokens
DeepSeek V4.1 Flash 1017 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4.1 Flash 1017 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 V4.1 Flash 1017 | OpenAI GPT-5.6 Luna |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | October 17, 2026 | July 9, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.15 | $0.20 |
| Output $/Mtok | $0.60 | $1.20 |
| Coding index | 70.3 | 58.0 |
| Independent measurements behind it | 4 | 6 |
| Cost per solved task | Not computable | $0.04 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4.1 Flash 1017 better than GPT-5.6 Luna for coding?
Pick DeepSeek V4.1 Flash 1017 for coding: it scores 70.3 on the Vibe Coding Index against 58.0 for GPT-5.6 Luna, though on 4 benchmarks against 6. Pick GPT-5.6 Luna for a larger context window (1.05M against 1.05M tokens) and work measured by Code migration (Vals AI) or ALE-Bench, where it leads.
Which is cheaper, DeepSeek V4.1 Flash 1017 or GPT-5.6 Luna?
DeepSeek V4.1 Flash 1017 costs $0.15 per million input tokens and $0.60 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 V4.1 Flash 1017 or GPT-5.6 Luna?
GPT-5.6 Luna, with 1.05M tokens against 1.05M.
How many benchmarks is the DeepSeek V4.1 Flash 1017 vs GPT-5.6 Luna comparison based on?
36 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4.1 Flash 1017's coding index rests on 4 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.