DeepSeek V4.1 Flash 1017 vs GPT-5.6 Sol
GPT-5.6 Sol leads DeepSeek V4.1 Flash 1017 on coding, 73.5 to 70.3 on the Vibe Coding Index, and DeepSeek V4.1 Flash 1017 costs 33.3x less per output token ($0.60 vs $20.00 per million). Updated October 22, 2026; 36 benchmarks have a result for both.
vs boards won of 36 shared
DeepSeek V4.1 Flash 1017 or GPT-5.6 Sol: which to pick
Pick GPT-5.6 Sol for coding: it scores 73.5 on the Vibe Coding Index against 70.3 for DeepSeek V4.1 Flash 1017.
Pick DeepSeek V4.1 Flash 1017 for output tokens 33.3x cheaper ($0.60 vs $20.00 per million) and work measured by LiveBench coding or LMArena WebDev, where it leads.
Model ADeepSeek V4.1 Flash 1017DeepSeek70.3/1004 independent benchmarksNo cost per solved taskModel BGPT-5.6 SolOpenAI73.5/1006 independent benchmarks$1.20 per solved task
- Shared results
- 36
- DeepSeek V4.1 Flash 1017 only
- 3
- GPT-5.6 Sol only
- 112
- 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-5.6 Sol
GPT-5.6 Sol scores 73.5 against 70.3.
4 and 6 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.6 Sol
GPT-5.6 Sol leads, 47.0 against 39.5.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GPT-5.6 Sol has a AA Agentic Index result, 50.2.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5.6 Sol has a GPQA Diamond result, 95.2%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.6 Sol has a MMLU Pro result, 89.1%.
Directional only
Web design
Design Arena: websites
GPT-5.6 Sol
GPT-5.6 Sol leads, 1321 against 1310.
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 Sol
GPT-5.6 Sol 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 Sol $0.014 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.6 Sol has the number, $1.20.
Directional only
At a glance
- Coding index
- DeepSeek V4.1 Flash 1017 70.3 from 4 benchmarks, GPT-5.6 Sol 73.5 from 6 benchmarks.
- Widest category
- Knowledge, 27.2 points apart on average over 1 shared benchmark.
- Widest single gap
- FrontierMath, tier 4: DeepSeek V4.1 Flash 1017 46.3% against GPT-5.6 Sol 82.9%, 36.6 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4.1 Flash 1017 $0.60 against GPT-5.6 Sol $20.00 per million output tokens, 33.3x apart.
- Context window
- DeepSeek V4.1 Flash 1017 takes 1.05M tokens, GPT-5.6 Sol 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 Sol | Ties |
|---|---|---|---|---|
| Coding | 5 | 4 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 4 | 3 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 3 | ||
| Knowledge | 0 | 1 | ||
| Web design | 1 | 7 | ||
| General | 0 | 2 | ||
| All shared boards | 10 | 26 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index54
| Benchmark | DeepSeek V4.1 Flash 1017 | GPT-5.6 Sol | Leader |
|---|---|---|---|
| LiveBench coding | 96.0% | 89.7% | DeepSeek V4.1 Flash 1017 |
| LMArena WebDev | 1664 | 1616 | DeepSeek V4.1 Flash 1017 |
| AA Coding Index | Not measured | 77.4 | Only GPT-5.6 Sol measured |
| LiveCodeBench (Vals AI) | Not measured | 82.6% | Only GPT-5.6 Sol measured |
| SWE-bench Verified (Vals AI) | Not measured | 96.2% | Only GPT-5.6 Sol measured |
| Terminal-Bench 4 (Vals AI) | 31.8% | 37.9% | GPT-5.6 Sol |
| Vibe Code Bench (Vals AI) | 87.7% | 80.5% | DeepSeek V4.1 Flash 1017 |
| Code migration (Vals AI) | 44.2% | 52.9% | GPT-5.6 Sol |
| ProgramBench (Vals AI) | Not measured | 1.5% | Only GPT-5.6 Sol measured |
| IOI (Vals AI) | Not measured | 91.2% | Only GPT-5.6 Sol measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 85.8% | Only GPT-5.6 Sol measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 20.0% | Only GPT-5.6 Sol measured |
| SkillsBench (Vals AI) | Not measured | 54.1% | Only GPT-5.6 Sol measured |
| CyberBench (Vals AI) | Not measured | 76.3% | Only GPT-5.6 Sol measured |
| SciCode | 55.9% | 57.8% | GPT-5.6 Sol |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 65.9% | Only GPT-5.6 Sol measured |
| FrontierCode | Not measured | 47.5% | Only GPT-5.6 Sol measured |
| FrontierSWE | Not measured | 32.2% | Only GPT-5.6 Sol measured |
| GSO-Bench | Not measured | 76.5% | Only GPT-5.6 Sol measured |
| WeirdML | Not measured | 88.8% | Only GPT-5.6 Sol measured |
| MirrorCode | Not measured | 20.0% | Only GPT-5.6 Sol measured |
| ALE-Bench | 1622.0 | 2176.9 | GPT-5.6 Sol |
| LMArena Text: coding | Not measured | 1496 | Only GPT-5.6 Sol measured |
| LMArena Text: software and IT | Not measured | 1489 | Only GPT-5.6 Sol measured |
| LMArena WebDev: React | 1667 | 1620 | DeepSeek V4.1 Flash 1017 |
| LMArena WebDev: HTML | 1687 | 1618 | DeepSeek V4.1 Flash 1017 |
| LMArena WebDev: from an image | Not measured | 1609 | Only GPT-5.6 Sol measured |
| SciCode | Not measured | 10.8% | Only GPT-5.6 Sol measured |
| SWE-Bench Pro V2 | Not measured | 95.5% | Only GPT-5.6 Sol measured |
| SWE Atlas: Codebase QnA | Not measured | 46.0% | Only GPT-5.6 Sol measured |
| SWE Atlas: Test Writing | Not measured | 45.9% | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 12 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V4.1 Flash 1017 | GPT-5.6 Sol | Leader |
|---|---|---|---|
| AA Intelligence Index | 39.5 | 47.0 | GPT-5.6 Sol |
| Epoch Capabilities Index | Not measured | 162.8 | Only GPT-5.6 Sol measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index43
| Benchmark | DeepSeek V4.1 Flash 1017 | GPT-5.6 Sol | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 50.2 | Only GPT-5.6 Sol measured |
| Design Arena: agents building web apps | Not measured | 1245 | Only GPT-5.6 Sol measured |
| Design Arena: agents building full-stack apps | Not measured | 1261 | Only GPT-5.6 Sol measured |
| Design Arena: agents building mobile apps | Not measured | 1248 | Only GPT-5.6 Sol measured |
| Design Arena: agents building Android apps | Not measured | 1239 | Only GPT-5.6 Sol measured |
| Design Arena: agents building Godot games | Not measured | 1260 | Only GPT-5.6 Sol measured |
| Design Arena: agents building games | Not measured | 1266 | Only GPT-5.6 Sol measured |
| Design Arena: HTML slides | Not measured | 1225 | Only GPT-5.6 Sol measured |
| Design Arena: PowerPoint slides | Not measured | 1214 | Only GPT-5.6 Sol measured |
| Design Arena: spreadsheets | 1259 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench research | 79.5% | 54.5% | DeepSeek V4.1 Flash 1017 |
| LiveBench browser | 76.0% | 88.0% | GPT-5.6 Sol |
| LiveBench long agentic | 50.9% | 48.5% | DeepSeek V4.1 Flash 1017 |
| SREBench (Vals AI) | Not measured | 30.5% | Only GPT-5.6 Sol measured |
| Finance Agent (Vals AI) | 57.0% | 53.8% | DeepSeek V4.1 Flash 1017 |
| Tax Agent Bench (Vals AI) | 44.4% | 31.1% | DeepSeek V4.1 Flash 1017 |
| Legal Research Bench (Vals AI) | 45.2% | 48.1% | GPT-5.6 Sol |
| 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% | 72.3% | GPT-5.6 Sol |
| BioMysteryBench (Vals AI) | Not measured | 71.1% | Only GPT-5.6 Sol measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 20.0% | Only GPT-5.6 Sol measured |
| Vals Science Index | Not measured | 41.5% | Only GPT-5.6 Sol measured |
| Time Horizon Index (Vals AI) | Not measured | 23.8% | Only GPT-5.6 Sol measured |
| CUA-bench (Vals AI) | Not measured | 8.3% | Only GPT-5.6 Sol measured |
| τ²-Bench Telecom | Not measured | 85.1% | Only GPT-5.6 Sol measured |
| OSWorld 2.0 | Not measured | 27.3% | Only GPT-5.6 Sol measured |
| BALROG | Not measured | 60.0% | Only GPT-5.6 Sol measured |
| PostTrainBench | Not measured | 36.2% | Only GPT-5.6 Sol measured |
| Vending-Bench 2 | Not measured | $9,619 | Only GPT-5.6 Sol measured |
| LMArena Search | Not measured | 1261 | Only GPT-5.6 Sol measured |
| MCP Atlas | Not measured | 81.8% | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 11 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | DeepSeek V4.1 Flash 1017 | GPT-5.6 Sol | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 95.2% | Only GPT-5.6 Sol measured |
| LiveBench reasoning | 89.1% | 91.7% | GPT-5.6 Sol |
| MysteryMechanism (Vals AI) | Not measured | 33.3% | Only GPT-5.6 Sol measured |
| MMMU Pro (Vals AI) | Not measured | 88.8% | Only GPT-5.6 Sol measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 94.1% | Only GPT-5.6 Sol measured |
| Humanity's Last Exam (Artificial Analysis run) | 43.3% | 49.5% | GPT-5.6 Sol |
| GPQA Diamond (Epoch AI run) | 93.2% | 93.5% | GPT-5.6 Sol |
| ARC-AGI | Not measured | 97.5% | Only GPT-5.6 Sol measured |
| ARC-AGI-2 | Not measured | 92.5% | Only GPT-5.6 Sol measured |
| SimpleBench | Not measured | 64.8% | Only GPT-5.6 Sol measured |
| Chess puzzles | 46.0% | 59.0% | GPT-5.6 Sol |
| Mystery game puzzles | 49.0% | 58.0% | GPT-5.6 Sol |
| EBR-bench | Not measured | 44.8% | Only GPT-5.6 Sol measured |
| LMArena Text: hard prompts | Not measured | 1483 | Only GPT-5.6 Sol measured |
| LMArena Text: expert | Not measured | 1517 | Only GPT-5.6 Sol measured |
| LMArena Text: hard prompts in English | Not measured | 1477 | Only GPT-5.6 Sol measured |
| LMArena Vision: diagrams | Not measured | 1305 | Only GPT-5.6 Sol measured |
| LMArena Vision: homework | Not measured | 1305 | Only GPT-5.6 Sol measured |
| Kagi LLM Benchmark | Not measured | 67.0% | Only GPT-5.6 Sol measured |
| Humanity's Last Exam (Scale run) | Not measured | 31.2% | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 3 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | DeepSeek V4.1 Flash 1017 | GPT-5.6 Sol | Leader |
|---|---|---|---|
| OTIS Mock AIME | 99.4% | 100.0% | GPT-5.6 Sol |
| FrontierMath, tiers 1 to 3 | 71.2% | 89.1% | GPT-5.6 Sol |
| FrontierMath, tier 4 | 46.3% | 82.9% | GPT-5.6 Sol |
| ProofBench | Not measured | 83.0% | Only GPT-5.6 Sol measured |
| LMArena Text: math | Not measured | 1473 | Only GPT-5.6 Sol measured |
| LMArena Text: mathematical work | Not measured | 1496 | Only GPT-5.6 Sol measured |
| MathArena | Not measured | 62.9% | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V4.1 Flash 1017 | GPT-5.6 Sol | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 89.1% | Only GPT-5.6 Sol measured |
| LegalBench (Vals AI) | Not measured | 87.0% | Only GPT-5.6 Sol measured |
| CorpFin (Vals AI) | Not measured | 64.4% | Only GPT-5.6 Sol measured |
| TaxEval (Vals AI) | Not measured | 74.8% | Only GPT-5.6 Sol measured |
| MortgageTax (Vals AI) | Not measured | 67.3% | Only GPT-5.6 Sol measured |
| MedCode (Vals AI) | Not measured | 44.0% | Only GPT-5.6 Sol measured |
| SimpleQA Verified | 42.5% | 69.7% | GPT-5.6 Sol |
| LMArena Text: science | Not measured | 1455 | Only GPT-5.6 Sol measured |
| LMArena Text: medicine and healthcare | Not measured | 1431 | Only GPT-5.6 Sol measured |
| LMArena Text: legal and government | Not measured | 1476 | Only GPT-5.6 Sol measured |
| LMArena Text: business and finance | Not measured | 1457 | Only GPT-5.6 Sol measured |
| FACTS Benchmark Suite | Not measured | 52.4% | Only GPT-5.6 Sol measured |
| PRBench Finance | Not measured | 50.4% | Only GPT-5.6 Sol measured |
| PRBench Legal | Not measured | 50.5% | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index17
| Benchmark | DeepSeek V4.1 Flash 1017 | GPT-5.6 Sol | Leader |
|---|---|---|---|
| Design Arena: websites | 1310 | 1321 | GPT-5.6 Sol |
| Design Arena: UI components | 1306 | 1336 | GPT-5.6 Sol |
| Design Arena: game dev | 1352 | 1367 | GPT-5.6 Sol |
| Design Arena: data viz | 1306 | 1323 | GPT-5.6 Sol |
| Design Arena: 3D | 1363 | 1384 | GPT-5.6 Sol |
| Design Arena: all code categories | 1330 | 1341 | GPT-5.6 Sol |
| Design Arena: SVG | Not measured | 1315 | Only GPT-5.6 Sol measured |
| Design Arena: ASCII art | 1233 | 1304 | GPT-5.6 Sol |
| LiveBench design | 83.4% | 81.6% | DeepSeek V4.1 Flash 1017 |
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V4.1 Flash 1017 | GPT-5.6 Sol | Leader |
|---|---|---|---|
| Vals Index | 61.3% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench data analysis | 79.5% | 79.8% | GPT-5.6 Sol |
| Public Benefits Bench (Vals AI) | Not measured | 66.5% | Only GPT-5.6 Sol measured |
| SAGE (Vals AI) | Not measured | 52.6% | Only GPT-5.6 Sol measured |
| MedScribe (Vals AI) | Not measured | 85.2% | Only GPT-5.6 Sol measured |
| Vals Multimodal Index | Not measured | 72.6% | Only GPT-5.6 Sol measured |
| IFBench | Not measured | 72.7% | Only GPT-5.6 Sol measured |
| Long context reasoning (AA-LCR) | 82.7% | 84.0% | GPT-5.6 Sol |
| DTBench | Not measured | 95.7% | Only GPT-5.6 Sol measured |
| LMCA | Not measured | 58.5% | Only GPT-5.6 Sol measured |
| Surface Evolver Bench | Not measured | 93.1% | Only GPT-5.6 Sol measured |
| Furniture assembly | Not measured | 56.7% | Only GPT-5.6 Sol measured |
| LMArena Text | Not measured | 1457 | Only GPT-5.6 Sol measured |
| LMArena Text: instruction following | Not measured | 1483 | Only GPT-5.6 Sol measured |
| LMArena Text: creative writing | Not measured | 1449 | Only GPT-5.6 Sol measured |
| LMArena Vision | Not measured | 1281 | Only GPT-5.6 Sol measured |
| LMArena Text: English | Not measured | 1457 | Only GPT-5.6 Sol measured |
| LMArena Text: non-English | Not measured | 1451 | Only GPT-5.6 Sol measured |
| LMArena Text: Chinese | Not measured | 1525 | Only GPT-5.6 Sol measured |
| LMArena Text: German | Not measured | 1476 | Only GPT-5.6 Sol measured |
| LMArena Text: Japanese | Not measured | 1472 | Only GPT-5.6 Sol measured |
| LMArena Text: Korean | Not measured | 1442 | Only GPT-5.6 Sol measured |
| LMArena Text: French | Not measured | 1472 | Only GPT-5.6 Sol measured |
| LMArena Text: Spanish | Not measured | 1443 | Only GPT-5.6 Sol measured |
| LMArena Text: Russian | Not measured | 1471 | Only GPT-5.6 Sol measured |
| LMArena Text: Polish | Not measured | 1459 | Only GPT-5.6 Sol measured |
| LMArena Text: multi-turn | Not measured | 1461 | Only GPT-5.6 Sol measured |
| LMArena Text: longer queries | Not measured | 1481 | Only GPT-5.6 Sol measured |
| LMArena Text: writing and language | Not measured | 1462 | Only GPT-5.6 Sol measured |
| LMArena Text: entertainment and media | Not measured | 1450 | Only GPT-5.6 Sol measured |
| LMArena Vision: English | Not measured | 1280 | Only GPT-5.6 Sol measured |
| LMArena Vision: Chinese | Not measured | 1313 | Only GPT-5.6 Sol measured |
| LMArena Vision: reading text | Not measured | 1290 | Only GPT-5.6 Sol measured |
| LMArena Vision: humor | Not measured | 1246 | Only GPT-5.6 Sol measured |
| LMArena Vision: creative writing | Not measured | 1283 | Only GPT-5.6 Sol measured |
| LMArena Document | Not measured | 1483 | Only GPT-5.6 Sol measured |
| EQ-Bench 4 | Not measured | 1250 | Only GPT-5.6 Sol 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 Sol | 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$0.0004Fits in one request
GPT-5.6 Sol$0.014Fits in one request
DeepSeek V4.1 Flash 1017 costs less
Repository review
50K input + 3K output tokens
DeepSeek V4.1 Flash 1017$0.0093Fits in one request
GPT-5.6 Sol$0.260Fits in one request
DeepSeek V4.1 Flash 1017 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4.1 Flash 1017$0.036Fits in one request
GPT-5.6 Sol$1.00Fits in one request
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 Sol |
|---|---|---|
| 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 | $4.00 |
| Output $/Mtok | $0.60 | $20.00 |
| Coding index | 70.3 | 73.5 |
| Independent measurements behind it | 4 | 6 |
| Cost per solved task | Not computable | $1.20 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4.1 Flash 1017 better than GPT-5.6 Sol for coding?
Pick GPT-5.6 Sol for coding: it scores 73.5 on the Vibe Coding Index against 70.3 for DeepSeek V4.1 Flash 1017. Pick DeepSeek V4.1 Flash 1017 for output tokens 33.3x cheaper ($0.60 vs $20.00 per million) and work measured by LiveBench coding or LMArena WebDev, where it leads.
Which is cheaper, DeepSeek V4.1 Flash 1017 or GPT-5.6 Sol?
DeepSeek V4.1 Flash 1017 costs $0.15 per million input tokens and $0.60 per million output tokens; GPT-5.6 Sol costs $4.00 and $20.00. Cost per solved task is known for GPT-5.6 Sol only.
Which has the bigger context window, DeepSeek V4.1 Flash 1017 or GPT-5.6 Sol?
GPT-5.6 Sol, with 1.05M tokens against 1.05M.
How many benchmarks is the DeepSeek V4.1 Flash 1017 vs GPT-5.6 Sol 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 Sol's on 6. Where only one model has a result, the page says so instead of naming a winner.