DeepSeek V4.1 Flash vs GPT-6.1 Sol
Updated October 21, 2026. 88 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GPT-6.1 Sol for coding: it scores 88.7 on the Vibe Coding Index against 77.2 for DeepSeek V4.1 Flash.
Pick DeepSeek V4.1 Flash when output tokens cost 19.0x less, or when what matters is SWE-bench Multimodal (Vals AI) or CyberBench (Vals AI), where it leads.
- Shared results
- 88
- DeepSeek V4.1 Flash only
- 13
- GPT-6.1 Sol only
- 7
- Like-for-like categories
- 8 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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, four public coding benchmarks
GPT-6.1 Sol
GPT-6.1 Sol scores 88.7 against 77.2.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
GPT-6.1 Sol
GPT-6.1 Sol leads, 51.8 against 24.7.
Same board, both measured
Web design
Design Arena: websites
GPT-6.1 Sol
GPT-6.1 Sol leads, 1321 against 1309.
Same board, both measured
General
Vals Index
No call
Only GPT-6.1 Sol has a Vals Index result, 63.9%.
Directional only
Long documents
Listed context window
GPT-6.1 Sol
GPT-6.1 Sol takes 1.05M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash $0.0003, GPT-6.1 Sol $0.0070 per turn.
List prices
At a glance
- Coding index
- DeepSeek V4.1 Flash 77.2 from 2 benchmarks, GPT-6.1 Sol 88.7 from 2 benchmarks.
- Widest category
- Math, 25.5 points apart on average over 6 shared benchmarks.
- Widest single gap
- FrontierMath, tier 4: DeepSeek V4.1 Flash 26.8% against GPT-6.1 Sol 100.0%, 73.2 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4.1 Flash $0.525 against GPT-6.1 Sol $10.00 per million output tokens, 19.0x apart.
- Context window
- DeepSeek V4.1 Flash takes 1.05M tokens, GPT-6.1 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 | Bars | GPT-6.1 Sol | Ties |
|---|---|---|---|---|
| Coding | 4 | 13 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 4 | 8 | ||
| Reasoning | 3 | 10 | ||
| Math | 0 | 6 | ||
| Knowledge | 3 | 3 | ||
| Web design | 1 | 6 | ||
| General | 16 | 8 | 1 tie | |
| All shared boards | 31 | 56 | 1 tie |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index413
| Benchmark | DeepSeek V4.1 Flash | GPT-6.1 Sol | Leader |
|---|---|---|---|
| LiveBench coding | 90.2% | 96.0% | GPT-6.1 Sol |
| LMArena WebDev | 1618 | 1753 | GPT-6.1 Sol |
| Terminal-Bench 4 (Vals AI) | 19.7% | 55.0% | GPT-6.1 Sol |
| Vibe Code Bench (Vals AI) | 84.7% | 88.9% | GPT-6.1 Sol |
| Code migration (Vals AI) | 45.6% | 65.1% | GPT-6.1 Sol |
| ProgramBench (Vals AI) | 0.5% | 3.0% | GPT-6.1 Sol |
| IOI (Vals AI) | 40.3% | 96.9% | GPT-6.1 Sol |
| Terminal-Bench 2.1 (Vals AI) | 74.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| SWE-bench Multimodal (Vals AI) | 46.5% | 43.5% | DeepSeek V4.1 Flash |
| Vibe Code Bench 1-100 (Vals AI) | 16.4% | Not measured | Only DeepSeek V4.1 Flash measured |
| SkillsBench (Vals AI) | 69.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| CyberBench (Vals AI) | 73.7% | 39.3% | DeepSeek V4.1 Flash |
| SciCode | 51.9% | 55.8% | GPT-6.1 Sol |
| FrontierCode | 43.9% | 50.2% | GPT-6.1 Sol |
| ALE-Bench | 1092.3 | 2743.8 | GPT-6.1 Sol |
| LMArena Text: coding | 1502 | 1478 | DeepSeek V4.1 Flash |
| LMArena Text: software and IT | 1496 | 1476 | DeepSeek V4.1 Flash |
| LMArena WebDev: React | 1628 | 1754 | GPT-6.1 Sol |
| LMArena WebDev: HTML | 1612 | 1800 | GPT-6.1 Sol |
| LMArena WebDev: from an image | 1606 | 1702 | GPT-6.1 Sol |
| Neither model has a result on the other 18 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V4.1 Flash | GPT-6.1 Sol | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.7 | 51.8 | GPT-6.1 Sol |
| Epoch Capabilities Index | 155.9 | 168.4 | GPT-6.1 Sol |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index48
| Benchmark | DeepSeek V4.1 Flash | GPT-6.1 Sol | Leader |
|---|---|---|---|
| Design Arena: agents building mobile apps | Not measured | 1190 | Only GPT-6.1 Sol measured |
| Design Arena: spreadsheets | Not measured | 1250 | Only GPT-6.1 Sol measured |
| LiveBench research | 86.8% | 83.5% | DeepSeek V4.1 Flash |
| LiveBench browser | 84.0% | 88.0% | GPT-6.1 Sol |
| LiveBench long agentic | 40.1% | 73.0% | GPT-6.1 Sol |
| SREBench (Vals AI) | 0.8% | 50.8% | GPT-6.1 Sol |
| Finance Agent (Vals AI) | 53.5% | 52.0% | DeepSeek V4.1 Flash |
| Tax Agent Bench (Vals AI) | 26.6% | 21.8% | DeepSeek V4.1 Flash |
| Legal Research Bench (Vals AI) | 41.4% | 38.5% | DeepSeek V4.1 Flash |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 6.3% | Only GPT-6.1 Sol measured |
| Excel Modeling Benchmark (Vals AI) | 57.2% | 70.8% | GPT-6.1 Sol |
| BioMysteryBench (Vals AI) | 67.8% | 79.6% | GPT-6.1 Sol |
| Terminal-Bench-Science (Vals AI) | 0.0% | 52.9% | GPT-6.1 Sol |
| Vals Science Index | 29.6% | 59.6% | GPT-6.1 Sol |
| Robo-bench (Vals AI) | Not measured | 28.3% | Only GPT-6.1 Sol measured |
| APEX-Agents | 39.5% | 60.0% | GPT-6.1 Sol |
| SpireClimb | Not measured | 24.7% | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 22 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index310
| Benchmark | DeepSeek V4.1 Flash | GPT-6.1 Sol | Leader |
|---|---|---|---|
| LiveBench reasoning | 86.7% | 92.6% | GPT-6.1 Sol |
| MysteryMechanism (Vals AI) | 21.2% | 46.4% | GPT-6.1 Sol |
| Humanity's Last Exam (Artificial Analysis run) | 39.2% | 52.9% | GPT-6.1 Sol |
| GPQA Diamond (Epoch AI run) | 89.8% | 95.4% | GPT-6.1 Sol |
| ARC-AGI | 94.5% | 98.5% | GPT-6.1 Sol |
| ARC-AGI-2 | 72.9% | 94.2% | GPT-6.1 Sol |
| Chess puzzles | 48.0% | 68.0% | GPT-6.1 Sol |
| Mystery game puzzles | 43.0% | 80.0% | GPT-6.1 Sol |
| EBR-bench | Not measured | 54.3% | Only GPT-6.1 Sol measured |
| LMArena Text: hard prompts | 1483 | 1463 | DeepSeek V4.1 Flash |
| LMArena Text: expert | 1501 | 1489 | DeepSeek V4.1 Flash |
| LMArena Text: hard prompts in English | 1488 | 1465 | DeepSeek V4.1 Flash |
| LMArena Vision: diagrams | 1297 | 1306 | GPT-6.1 Sol |
| LMArena Vision: homework | 1307 | 1356 | GPT-6.1 Sol |
| Neither model has a result on the other 7 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | DeepSeek V4.1 Flash | GPT-6.1 Sol | Leader |
|---|---|---|---|
| OTIS Mock AIME | 98.3% | 100.0% | GPT-6.1 Sol |
| FrontierMath, tiers 1 to 3 | 67.4% | 93.7% | GPT-6.1 Sol |
| FrontierMath, tier 4 | 26.8% | 100.0% | GPT-6.1 Sol |
| ProofBench | 54.0% | 99.0% | GPT-6.1 Sol |
| LMArena Text: math | 1478 | 1489 | GPT-6.1 Sol |
| LMArena Text: mathematical work | 1485 | 1513 | GPT-6.1 Sol |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index33
| Benchmark | DeepSeek V4.1 Flash | GPT-6.1 Sol | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | 83.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| MedCode (Vals AI) | 41.2% | 48.8% | GPT-6.1 Sol |
| SimpleQA Verified | 42.9% | 73.9% | GPT-6.1 Sol |
| LMArena Text: science | 1469 | 1444 | DeepSeek V4.1 Flash |
| LMArena Text: medicine and healthcare | 1484 | 1433 | DeepSeek V4.1 Flash |
| LMArena Text: legal and government | 1443 | 1471 | GPT-6.1 Sol |
| LMArena Text: business and finance | 1461 | 1445 | DeepSeek V4.1 Flash |
| Neither model has a result on the other 10 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index16
| Benchmark | DeepSeek V4.1 Flash | GPT-6.1 Sol | Leader |
|---|---|---|---|
| Design Arena: websites | 1309 | 1321 | GPT-6.1 Sol |
| Design Arena: UI components | 1314 | 1321 | GPT-6.1 Sol |
| Design Arena: game dev | 1331 | 1389 | GPT-6.1 Sol |
| Design Arena: data viz | 1290 | 1278 | DeepSeek V4.1 Flash |
| Design Arena: 3D | 1356 | 1461 | GPT-6.1 Sol |
| Design Arena: all code categories | 1320 | 1345 | GPT-6.1 Sol |
| Design Arena: SVG | 1243 | Not measured | Only DeepSeek V4.1 Flash measured |
| Design Arena: ASCII art | 1265 | Not measured | Only DeepSeek V4.1 Flash measured |
| LiveBench design | 79.2% | 82.1% | GPT-6.1 Sol |
GeneralLike-for-likePublic boards, shown as published, kept out of the index168
| Benchmark | DeepSeek V4.1 Flash | GPT-6.1 Sol | Leader |
|---|---|---|---|
| Vals Index | Not measured | 63.9% | Only GPT-6.1 Sol measured |
| LiveBench data analysis | 79.3% | 82.7% | GPT-6.1 Sol |
| Public Benefits Bench (Vals AI) | 64.3% | 59.3% | DeepSeek V4.1 Flash |
| SAGE (Vals AI) | 47.9% | 46.5% | DeepSeek V4.1 Flash |
| MedScribe (Vals AI) | 85.5% | 86.5% | GPT-6.1 Sol |
| Long context reasoning (AA-LCR) | 84.0% | 84.0% | Tie |
| DTBench | 89.9% | 96.5% | GPT-6.1 Sol |
| LMCA | 47.0% | 62.8% | GPT-6.1 Sol |
| Surface Evolver Bench | 46.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| Furniture assembly | 34.2% | 80.0% | GPT-6.1 Sol |
| LMArena Text | 1464 | 1443 | DeepSeek V4.1 Flash |
| LMArena Text: instruction following | 1473 | 1466 | DeepSeek V4.1 Flash |
| LMArena Text: creative writing | 1437 | 1425 | DeepSeek V4.1 Flash |
| LMArena Vision | 1278 | 1288 | GPT-6.1 Sol |
| LMArena Text: English | 1473 | 1450 | DeepSeek V4.1 Flash |
| LMArena Text: non-English | 1449 | 1433 | DeepSeek V4.1 Flash |
| LMArena Text: Chinese | 1501 | 1487 | DeepSeek V4.1 Flash |
| LMArena Text: German | 1470 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: Japanese | 1432 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: Korean | 1436 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: French | 1465 | 1460 | DeepSeek V4.1 Flash |
| LMArena Text: Spanish | 1459 | 1439 | DeepSeek V4.1 Flash |
| LMArena Text: Russian | 1467 | 1457 | DeepSeek V4.1 Flash |
| LMArena Text: Polish | 1457 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: multi-turn | 1461 | 1442 | DeepSeek V4.1 Flash |
| LMArena Text: longer queries | 1473 | 1458 | DeepSeek V4.1 Flash |
| LMArena Text: writing and language | 1450 | 1444 | DeepSeek V4.1 Flash |
| LMArena Text: entertainment and media | 1445 | 1426 | DeepSeek V4.1 Flash |
| LMArena Vision: English | 1288 | 1274 | DeepSeek V4.1 Flash |
| LMArena Vision: Chinese | 1327 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision: reading text | 1282 | 1289 | GPT-6.1 Sol |
| LMArena Vision: humor | 1260 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision: creative writing | 1275 | 1301 | GPT-6.1 Sol |
| 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 V4.1 Flash | GPT-6.1 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 costs less
Repository review
50K input + 3K output tokens
DeepSeek V4.1 Flash costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4.1 Flash 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 | OpenAI GPT-6.1 Sol |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | September 10, 2026 | September 29, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.049 | $2.00 |
| Output $/Mtok | $0.525 | $10.00 |
| Coding index | 77.2 | 88.7 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4.1 Flash better than GPT-6.1 Sol for coding?
Pick GPT-6.1 Sol for coding: it scores 88.7 on the Vibe Coding Index against 77.2 for DeepSeek V4.1 Flash. Pick DeepSeek V4.1 Flash when output tokens cost 19.0x less, or when what matters is SWE-bench Multimodal (Vals AI) or CyberBench (Vals AI), where it leads.
Which is cheaper, DeepSeek V4.1 Flash or GPT-6.1 Sol?
DeepSeek V4.1 Flash costs $0.049 per million input tokens and $0.525 per million output tokens; GPT-6.1 Sol costs $2.00 and $10.00. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V4.1 Flash or GPT-6.1 Sol?
GPT-6.1 Sol, with 1.05M tokens against 1.05M.
How many benchmarks is the DeepSeek V4.1 Flash vs GPT-6.1 Sol comparison based on?
88 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4.1 Flash's coding index rests on 2 independent benchmarks and GPT-6.1 Sol's on 2. Where only one model has a result, the page says so instead of naming a winner.