DeepSeek V4.1 Flash vs GPT-5 Nano
Updated October 21, 2026. 60 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
vs boards won of 60 shared
Decision reading
Pick DeepSeek V4.1 Flash for coding: it scores 77.2 on the Vibe Coding Index against 34.8 for GPT-5 Nano.
Pick GPT-5 Nano when output tokens cost 1.3x less.
Model ADeepSeek V4.1 FlashDeepSeek77.2/1002 independent benchmarksNo cost per solved taskModel BGPT-5 NanoOpenAI34.8/1001 independent benchmark$0.109 per solved task
- Shared results
- 60
- DeepSeek V4.1 Flash only
- 41
- GPT-5 Nano only
- 25
- Like-for-like categories
- 7 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
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash scores 77.2 against 34.8.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash leads, 24.7 against 13.0.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GPT-5 Nano has a GPQA Diamond result, 63.4%.
Directional only
Math
AIME
No call
Only GPT-5 Nano has a AIME result, 81.2%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5 Nano has a MMLU Pro result, 76.1%.
Directional only
Web design
Design Arena: websites
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash leads, 1309 against 1101.
Same board, both measured
Long documents
Listed context window
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5 Nano
DeepSeek V4.1 Flash $0.0003, GPT-5 Nano $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5 Nano has the number, $0.109.
Directional only
At a glance
- Coding index
- DeepSeek V4.1 Flash 77.2 from 2 benchmarks, GPT-5 Nano 34.8 from 1 benchmark.
- Widest category
- Reasoning, 35.1 points apart on average over 11 shared benchmarks.
- Widest single gap
- ARC-AGI: DeepSeek V4.1 Flash 94.5% against GPT-5 Nano 20.7%, 73.8 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4.1 Flash $0.525 against GPT-5 Nano $0.40 per million output tokens, 1.3x apart.
- Context window
- DeepSeek V4.1 Flash takes 1.05M tokens, GPT-5 Nano 400K.
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-5 Nano | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 2 | 0 | ||
| Reasoning | 11 | 0 | ||
| Math | 6 | 0 | ||
| Knowledge | 7 | 0 | ||
| Web design | 6 | 0 | ||
| General | 26 | 0 | ||
| All shared boards | 60 | 0 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index20
| Benchmark | DeepSeek V4.1 Flash | GPT-5 Nano | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 34.8% | Only GPT-5 Nano measured |
| LiveBench coding | 90.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena WebDev | 1618 | Not measured | Only DeepSeek V4.1 Flash measured |
| LiveCodeBench (Vals AI) | Not measured | 70.2% | Only GPT-5 Nano measured |
| Terminal-Bench 4 (Vals AI) | 19.7% | Not measured | Only DeepSeek V4.1 Flash measured |
| Vibe Code Bench (Vals AI) | 84.7% | Not measured | Only DeepSeek V4.1 Flash measured |
| Code migration (Vals AI) | 45.6% | Not measured | Only DeepSeek V4.1 Flash measured |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| IOI (Vals AI) | 40.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | 74.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| SWE-bench Multimodal (Vals AI) | 46.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| 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% | Not measured | Only DeepSeek V4.1 Flash measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 78.9% | Only GPT-5 Nano measured |
| SciCode | 51.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 17.4% | Only GPT-5 Nano measured |
| Terminal-Bench | Not measured | 21.8% | Only GPT-5 Nano measured |
| FrontierCode | 43.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| WeirdML | Not measured | 38.1% | Only GPT-5 Nano measured |
| ALE-Bench | 1092.3 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: coding | 1502 | 1351 | DeepSeek V4.1 Flash |
| LMArena Text: software and IT | 1496 | 1345 | DeepSeek V4.1 Flash |
| LMArena WebDev: React | 1628 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena WebDev: HTML | 1612 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena WebDev: from an image | 1606 | Not measured | Only DeepSeek V4.1 Flash measured |
| Neither model has a result on the other 12 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4.1 Flash | GPT-5 Nano | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.7 | 13.0 | DeepSeek V4.1 Flash |
| Epoch Capabilities Index | 155.9 | 139.2 | DeepSeek V4.1 Flash |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index13 boards, none shared
| Benchmark | DeepSeek V4.1 Flash | GPT-5 Nano | Leader |
|---|---|---|---|
| LiveBench research | 86.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| LiveBench browser | 84.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| LiveBench long agentic | 40.1% | Not measured | Only DeepSeek V4.1 Flash measured |
| SREBench (Vals AI) | 0.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| Finance Agent (Vals AI) | 53.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| Tax Agent Bench (Vals AI) | 26.6% | Not measured | Only DeepSeek V4.1 Flash measured |
| Legal Research Bench (Vals AI) | 41.4% | Not measured | Only DeepSeek V4.1 Flash measured |
| Excel Modeling Benchmark (Vals AI) | 57.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| BioMysteryBench (Vals AI) | 67.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| Terminal-Bench-Science (Vals AI) | 0.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| Vals Science Index | 29.6% | Not measured | Only DeepSeek V4.1 Flash measured |
| τ²-Bench Telecom | Not measured | 36.5% | Only GPT-5 Nano measured |
| APEX-Agents | 39.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| Neither model has a result on the other 26 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index110
| Benchmark | DeepSeek V4.1 Flash | GPT-5 Nano | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 63.4% | Only GPT-5 Nano measured |
| LiveBench reasoning | 86.7% | Not measured | Only DeepSeek V4.1 Flash measured |
| MysteryMechanism (Vals AI) | 21.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| MMMU Pro (Vals AI) | Not measured | 70.9% | Only GPT-5 Nano measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 67.6% | Only GPT-5 Nano measured |
| Humanity's Last Exam (Artificial Analysis run) | 39.2% | 9.5% | DeepSeek V4.1 Flash |
| GPQA Diamond (Epoch AI run) | 89.8% | 69.4% | DeepSeek V4.1 Flash |
| ARC-AGI | 94.5% | 20.7% | DeepSeek V4.1 Flash |
| ARC-AGI-2 | 72.9% | 2.6% | DeepSeek V4.1 Flash |
| Chess puzzles | 48.0% | 22.0% | DeepSeek V4.1 Flash |
| Mystery game puzzles | 43.0% | 9.0% | DeepSeek V4.1 Flash |
| VPCT | Not measured | 37.2% | Only GPT-5 Nano measured |
| LMArena Text: hard prompts | 1483 | 1328 | DeepSeek V4.1 Flash |
| LMArena Text: expert | 1501 | 1321 | DeepSeek V4.1 Flash |
| LMArena Text: hard prompts in English | 1488 | 1338 | DeepSeek V4.1 Flash |
| LMArena Vision: diagrams | 1297 | 1141 | DeepSeek V4.1 Flash |
| LMArena Vision: homework | 1307 | 1157 | DeepSeek V4.1 Flash |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | DeepSeek V4.1 Flash | GPT-5 Nano | Leader |
|---|---|---|---|
| AIME | Not measured | 81.2% | Only GPT-5 Nano measured |
| MATH 500 (Vals AI) | Not measured | 93.8% | Only GPT-5 Nano measured |
| MGSM (Vals AI) | Not measured | 89.3% | Only GPT-5 Nano measured |
| AA Math Index | Not measured | 83.7 | Only GPT-5 Nano measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 83.7% | Only GPT-5 Nano measured |
| OTIS Mock AIME | 98.3% | 81.1% | DeepSeek V4.1 Flash |
| FrontierMath, tiers 1 to 3 | 67.4% | 20.0% | DeepSeek V4.1 Flash |
| FrontierMath, tier 4 | 26.8% | 2.4% | DeepSeek V4.1 Flash |
| ProofBench | 54.0% | 12.0% | DeepSeek V4.1 Flash |
| MATH level 5 | Not measured | 95.2% | Only GPT-5 Nano measured |
| LMArena Text: math | 1478 | 1317 | DeepSeek V4.1 Flash |
| LMArena Text: mathematical work | 1485 | 1326 | DeepSeek V4.1 Flash |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | DeepSeek V4.1 Flash | GPT-5 Nano | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 76.1% | Only GPT-5 Nano measured |
| LegalBench (Vals AI) | 83.3% | 50.1% | DeepSeek V4.1 Flash |
| CaseLaw (Vals AI) | Not measured | 52.6% | Only GPT-5 Nano measured |
| TaxEval (Vals AI) | Not measured | 67.4% | Only GPT-5 Nano measured |
| MortgageTax (Vals AI) | Not measured | 53.6% | Only GPT-5 Nano measured |
| MedQA (Vals AI) | Not measured | 93.3% | Only GPT-5 Nano measured |
| MedCode (Vals AI) | 41.2% | 30.4% | DeepSeek V4.1 Flash |
| MMLU Pro (Artificial Analysis run) | Not measured | 78.0% | Only GPT-5 Nano measured |
| SimpleQA Verified | 42.9% | 11.7% | DeepSeek V4.1 Flash |
| LMArena Text: science | 1469 | 1318 | DeepSeek V4.1 Flash |
| LMArena Text: medicine and healthcare | 1484 | 1335 | DeepSeek V4.1 Flash |
| LMArena Text: legal and government | 1443 | 1298 | DeepSeek V4.1 Flash |
| LMArena Text: business and finance | 1461 | 1311 | DeepSeek V4.1 Flash |
| Neither model has a result on the other 4 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | DeepSeek V4.1 Flash | GPT-5 Nano | Leader |
|---|---|---|---|
| Design Arena: websites | 1309 | 1101 | DeepSeek V4.1 Flash |
| Design Arena: UI components | 1314 | 1066 | DeepSeek V4.1 Flash |
| Design Arena: game dev | 1331 | 1045 | DeepSeek V4.1 Flash |
| Design Arena: data viz | 1290 | 1056 | DeepSeek V4.1 Flash |
| Design Arena: 3D | 1356 | 966 | DeepSeek V4.1 Flash |
| Design Arena: all code categories | 1320 | 1084 | DeepSeek V4.1 Flash |
| 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% | Not measured | Only DeepSeek V4.1 Flash measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index260
| Benchmark | DeepSeek V4.1 Flash | GPT-5 Nano | Leader |
|---|---|---|---|
| LiveBench data analysis | 79.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| Public Benefits Bench (Vals AI) | 64.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| SAGE (Vals AI) | 47.9% | 30.4% | DeepSeek V4.1 Flash |
| MedScribe (Vals AI) | 85.5% | 72.9% | DeepSeek V4.1 Flash |
| IFBench | Not measured | 67.6% | Only GPT-5 Nano measured |
| Long context reasoning (AA-LCR) | 84.0% | 45.0% | DeepSeek V4.1 Flash |
| DTBench | 89.9% | 62.7% | DeepSeek V4.1 Flash |
| LMCA | 47.0% | 7.9% | DeepSeek V4.1 Flash |
| Fiction.LiveBench | Not measured | 44.4% | Only GPT-5 Nano measured |
| Surface Evolver Bench | 46.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| Furniture assembly | 34.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text | 1464 | 1320 | DeepSeek V4.1 Flash |
| LMArena Text: instruction following | 1473 | 1306 | DeepSeek V4.1 Flash |
| LMArena Text: creative writing | 1437 | 1249 | DeepSeek V4.1 Flash |
| LMArena Vision | 1278 | 1159 | DeepSeek V4.1 Flash |
| LMArena Text: English | 1473 | 1321 | DeepSeek V4.1 Flash |
| LMArena Text: non-English | 1449 | 1313 | DeepSeek V4.1 Flash |
| LMArena Text: Chinese | 1501 | 1356 | DeepSeek V4.1 Flash |
| LMArena Text: German | 1470 | 1327 | DeepSeek V4.1 Flash |
| LMArena Text: Japanese | 1432 | 1227 | DeepSeek V4.1 Flash |
| LMArena Text: Korean | 1436 | 1269 | DeepSeek V4.1 Flash |
| LMArena Text: French | 1465 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: Spanish | 1459 | 1360 | DeepSeek V4.1 Flash |
| LMArena Text: Russian | 1467 | 1296 | DeepSeek V4.1 Flash |
| LMArena Text: Polish | 1457 | 1314 | DeepSeek V4.1 Flash |
| LMArena Text: multi-turn | 1461 | 1311 | DeepSeek V4.1 Flash |
| LMArena Text: longer queries | 1473 | 1312 | DeepSeek V4.1 Flash |
| LMArena Text: writing and language | 1450 | 1283 | DeepSeek V4.1 Flash |
| LMArena Text: entertainment and media | 1445 | 1268 | DeepSeek V4.1 Flash |
| LMArena Vision: English | 1288 | 1170 | DeepSeek V4.1 Flash |
| LMArena Vision: Chinese | 1327 | 1176 | DeepSeek V4.1 Flash |
| LMArena Vision: reading text | 1282 | 1152 | DeepSeek V4.1 Flash |
| LMArena Vision: humor | 1260 | 1110 | DeepSeek V4.1 Flash |
| LMArena Vision: creative writing | 1275 | Not measured | Only DeepSeek V4.1 Flash 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 V4.1 Flash | GPT-5 Nano | 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$0.0003Fits in one request
GPT-5 Nano$0.0003Fits in one request
GPT-5 Nano costs less
Repository review
50K input + 3K output tokens
DeepSeek V4.1 Flash$0.0040Fits in one request
GPT-5 Nano$0.0037Fits in one request
GPT-5 Nano costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4.1 Flash$0.015Fits in one request
GPT-5 Nano$0.014Fits in one request
GPT-5 Nano 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-5 Nano |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | September 10, 2026 | August 7, 2025 |
| Context window | 1.05M | 400K |
| Input $/Mtok | $0.049 | $0.05 |
| Output $/Mtok | $0.525 | $0.40 |
| Coding index | 77.2 | 34.8 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | Not computable | $0.109 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4.1 Flash better than GPT-5 Nano for coding?
Pick DeepSeek V4.1 Flash for coding: it scores 77.2 on the Vibe Coding Index against 34.8 for GPT-5 Nano. Pick GPT-5 Nano when output tokens cost 1.3x less.
Which is cheaper, DeepSeek V4.1 Flash or GPT-5 Nano?
DeepSeek V4.1 Flash costs $0.049 per million input tokens and $0.525 per million output tokens; GPT-5 Nano costs $0.05 and $0.40. Cost per solved task is known for GPT-5 Nano only.
Which has the bigger context window, DeepSeek V4.1 Flash or GPT-5 Nano?
DeepSeek V4.1 Flash, with 1.05M tokens against 400K.
How many benchmarks is the DeepSeek V4.1 Flash vs GPT-5 Nano comparison based on?
60 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-5 Nano's on 1. Where only one model has a result, the page says so instead of naming a winner.