DeepSeek V4 Pro 0813 vs GPT-5 Nano
DeepSeek V4 Pro 0813 leads GPT-5 Nano on the Artificial Analysis Intelligence Index, 36.0 to 13.0, and GPT-5 Nano costs 4.9x less per output token ($0.40 vs $1.98 per million). Updated October 22, 2026; 49 benchmarks have a result for both.
DeepSeek V4 Pro 0813 or GPT-5 Nano: which to pick
Only DeepSeek V4 Pro 0813 has a coding score, 56.1 on the Vibe Coding Index; GPT-5 Nano has none, so the two cannot be ranked on coding.
Pick GPT-5 Nano for output tokens 4.9x cheaper ($0.40 vs $1.98 per million).
- Shared results
- 49
- DeepSeek V4 Pro 0813 only
- 29
- GPT-5 Nano only
- 39
- Like-for-like categories
- 6 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
No call
Only DeepSeek V4 Pro 0813 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 leads, 36.0 against 13.0.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only DeepSeek V4 Pro 0813 has a AA Agentic Index result, 41.3.
Directional only
Reasoning
GPQA Diamond
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 leads, 92.4% against 63.4%.
Same board, both measured
Math
AIME
No call
Only GPT-5 Nano has a AIME result, 81.2%.
Directional only
Knowledge
MMLU Pro
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 leads, 87.0% against 76.1%.
Same board, both measured
Web design
Design Arena: websites
No call
Only GPT-5 Nano has a Design Arena: websites result, 1101.
Directional only
General
Vals Index
No call
Only DeepSeek V4 Pro 0813 has a Vals Index result, 53.0%.
Directional only
Long documents
Listed context window
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5 Nano
DeepSeek V4 Pro 0813 $0.0016, GPT-5 Nano $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 $0.10, GPT-5 Nano $0.109.
Published attempt cost and pass rate
At a glance
- Coding index
- DeepSeek V4 Pro 0813 56.1 from 5 benchmarks, GPT-5 Nano not benchmarked.
- Widest category
- Reasoning, 32.7 points apart on average over 11 shared benchmarks.
- Widest single gap
- ARC-AGI: DeepSeek V4 Pro 0813 90.5% against GPT-5 Nano 20.7%, 69.8 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Pro 0813 $1.98 against GPT-5 Nano $0.40 per million output tokens, 4.9x apart.
- Context window
- DeepSeek V4 Pro 0813 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 Pro 0813 | Bars | GPT-5 Nano | Ties |
|---|---|---|---|---|
| Coding | 4 | 0 | ||
| Intelligence | 2 | 0 | ||
| Reasoning | 11 | 0 | ||
| Math | 6 | 0 | ||
| Knowledge | 9 | 0 | ||
| General | 17 | 0 | ||
| All shared boards | 49 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
| Benchmark | DeepSeek V4 Pro 0813 | GPT-5 Nano | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 34.8% | Only GPT-5 Nano measured |
| LMArena WebDev | 1582 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| AA Coding Index | 68.8 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LiveCodeBench (Vals AI) | 87.5% | 70.2% | DeepSeek V4 Pro 0813 |
| SWE-bench Verified (Vals AI) | 96.4% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Terminal-Bench 4 (Vals AI) | 14.1% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Vibe Code Bench (Vals AI) | 82.3% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Code migration (Vals AI) | 41.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| IOI (Vals AI) | 51.6% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Terminal-Bench 2.1 (Vals AI) | 54.7% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Vibe Code Bench 1-100 (Vals AI) | 17.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| SkillsBench (Vals AI) | 53.8% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| CyberBench (Vals AI) | 72.9% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 78.9% | Only GPT-5 Nano measured |
| SciCode | 51.0% | Not measured | Only DeepSeek V4 Pro 0813 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 | 28.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| WeirdML | 66.2% | 38.1% | DeepSeek V4 Pro 0813 |
| ALE-Bench | 1403.2 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: coding | 1469 | 1351 | DeepSeek V4 Pro 0813 |
| LMArena Text: software and IT | 1462 | 1345 | DeepSeek V4 Pro 0813 |
| LMArena WebDev: React | 1588 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena WebDev: HTML | 1566 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Neither model has a result on the other 18 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4 Pro 0813 | GPT-5 Nano | Leader |
|---|---|---|---|
| AA Intelligence Index | 36.0 | 13.0 | DeepSeek V4 Pro 0813 |
| Epoch Capabilities Index | 155.6 | 139.2 | DeepSeek V4 Pro 0813 |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | DeepSeek V4 Pro 0813 | GPT-5 Nano | Leader |
|---|---|---|---|
| AA Agentic Index | 41.3 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| SREBench (Vals AI) | 1.9% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Finance Agent (Vals AI) | 50.4% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Tax Agent Bench (Vals AI) | 26.8% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Legal Research Bench (Vals AI) | 40.9% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 10.8% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Excel Modeling Benchmark (Vals AI) | 52.8% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Terminal-Bench-Science (Vals AI) | 4.3% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| τ²-Bench Telecom | Not measured | 36.5% | Only GPT-5 Nano measured |
| APEX-Agents | 47.3% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 51.4% | Only GPT-5 Nano measured |
| Neither model has a result on the other 31 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index110
| Benchmark | DeepSeek V4 Pro 0813 | GPT-5 Nano | Leader |
|---|---|---|---|
| GPQA Diamond | 92.4% | 63.4% | DeepSeek V4 Pro 0813 |
| MMMU Pro (Vals AI) | Not measured | 70.9% | Only GPT-5 Nano measured |
| GPQA Diamond (Artificial Analysis run) | 92.8% | 67.6% | DeepSeek V4 Pro 0813 |
| Humanity's Last Exam (Artificial Analysis run) | 41.0% | 9.5% | DeepSeek V4 Pro 0813 |
| GPQA Diamond (Epoch AI run) | 91.7% | 69.4% | DeepSeek V4 Pro 0813 |
| ARC-AGI | 90.5% | 20.7% | DeepSeek V4 Pro 0813 |
| ARC-AGI-2 | 61.3% | 2.6% | DeepSeek V4 Pro 0813 |
| Chess puzzles | 46.0% | 22.0% | DeepSeek V4 Pro 0813 |
| Mystery game puzzles | 43.0% | 9.0% | DeepSeek V4 Pro 0813 |
| VPCT | Not measured | 37.2% | Only GPT-5 Nano measured |
| LMArena Text: hard prompts | 1458 | 1328 | DeepSeek V4 Pro 0813 |
| LMArena Text: expert | 1459 | 1321 | DeepSeek V4 Pro 0813 |
| LMArena Text: hard prompts in English | 1463 | 1338 | DeepSeek V4 Pro 0813 |
| LMArena Vision: diagrams | Not measured | 1141 | Only GPT-5 Nano measured |
| LMArena Vision: homework | Not measured | 1157 | Only GPT-5 Nano measured |
| Kagi LLM Benchmark | Not measured | 62.2% | Only GPT-5 Nano measured |
| Neither model has a result on the other 7 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | DeepSeek V4 Pro 0813 | 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 | 98.6% | 81.1% | DeepSeek V4 Pro 0813 |
| FrontierMath, tiers 1 to 3 | 64.6% | 20.0% | DeepSeek V4 Pro 0813 |
| FrontierMath, tier 4 | 26.8% | 2.4% | DeepSeek V4 Pro 0813 |
| ProofBench | 50.0% | 12.0% | DeepSeek V4 Pro 0813 |
| MATH level 5 | Not measured | 95.2% | Only GPT-5 Nano measured |
| LMArena Text: math | 1441 | 1317 | DeepSeek V4 Pro 0813 |
| LMArena Text: mathematical work | 1443 | 1326 | DeepSeek V4 Pro 0813 |
| MathArena | Not measured | 12.8% | Only GPT-5 Nano measured |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index90
| Benchmark | DeepSeek V4 Pro 0813 | GPT-5 Nano | Leader |
|---|---|---|---|
| MMLU Pro | 87.0% | 76.1% | DeepSeek V4 Pro 0813 |
| LegalBench (Vals AI) | 82.4% | 50.1% | DeepSeek V4 Pro 0813 |
| CaseLaw (Vals AI) | Not measured | 52.6% | Only GPT-5 Nano measured |
| CorpFin (Vals AI) | 65.4% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| TaxEval (Vals AI) | 73.1% | 67.4% | DeepSeek V4 Pro 0813 |
| 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) | 42.5% | 30.4% | DeepSeek V4 Pro 0813 |
| MMLU Pro (Artificial Analysis run) | Not measured | 78.0% | Only GPT-5 Nano measured |
| SimpleQA Verified | 52.9% | 11.7% | DeepSeek V4 Pro 0813 |
| LMArena Text: science | 1457 | 1318 | DeepSeek V4 Pro 0813 |
| LMArena Text: medicine and healthcare | 1446 | 1335 | DeepSeek V4 Pro 0813 |
| LMArena Text: legal and government | 1459 | 1298 | DeepSeek V4 Pro 0813 |
| LMArena Text: business and finance | 1442 | 1311 | DeepSeek V4 Pro 0813 |
| Neither model has a result on the other 6 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | DeepSeek V4 Pro 0813 | GPT-5 Nano | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1101 | Only GPT-5 Nano measured |
| Design Arena: UI components | Not measured | 1064 | Only GPT-5 Nano measured |
| Design Arena: game dev | Not measured | 1044 | Only GPT-5 Nano measured |
| Design Arena: data viz | Not measured | 1056 | Only GPT-5 Nano measured |
| Design Arena: 3D | Not measured | 965 | Only GPT-5 Nano measured |
| Design Arena: all code categories | Not measured | 1084 | Only GPT-5 Nano measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index170
| Benchmark | DeepSeek V4 Pro 0813 | GPT-5 Nano | Leader |
|---|---|---|---|
| Vals Index | 53.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| SAGE (Vals AI) | Not measured | 30.4% | Only GPT-5 Nano measured |
| MedScribe (Vals AI) | 80.2% | 72.9% | DeepSeek V4 Pro 0813 |
| IFBench | Not measured | 67.6% | Only GPT-5 Nano measured |
| Long context reasoning (AA-LCR) | 80.3% | 45.0% | DeepSeek V4 Pro 0813 |
| DTBench | 93.9% | 62.7% | DeepSeek V4 Pro 0813 |
| LMCA | 45.5% | 7.9% | DeepSeek V4 Pro 0813 |
| Fiction.LiveBench | Not measured | 44.4% | Only GPT-5 Nano measured |
| LMArena Text | 1446 | 1320 | DeepSeek V4 Pro 0813 |
| LMArena Text: instruction following | 1448 | 1306 | DeepSeek V4 Pro 0813 |
| LMArena Text: creative writing | 1439 | 1249 | DeepSeek V4 Pro 0813 |
| LMArena Vision | Not measured | 1159 | Only GPT-5 Nano measured |
| LMArena Text: English | 1457 | 1321 | DeepSeek V4 Pro 0813 |
| LMArena Text: non-English | 1431 | 1313 | DeepSeek V4 Pro 0813 |
| LMArena Text: Chinese | 1481 | 1356 | DeepSeek V4 Pro 0813 |
| LMArena Text: German | Not measured | 1327 | Only GPT-5 Nano measured |
| LMArena Text: Japanese | Not measured | 1227 | Only GPT-5 Nano measured |
| LMArena Text: Korean | 1449 | 1269 | DeepSeek V4 Pro 0813 |
| LMArena Text: French | 1446 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: Spanish | 1435 | 1360 | DeepSeek V4 Pro 0813 |
| LMArena Text: Russian | 1448 | 1296 | DeepSeek V4 Pro 0813 |
| LMArena Text: Polish | Not measured | 1314 | Only GPT-5 Nano measured |
| LMArena Text: multi-turn | 1454 | 1311 | DeepSeek V4 Pro 0813 |
| LMArena Text: longer queries | 1458 | 1312 | DeepSeek V4 Pro 0813 |
| LMArena Text: writing and language | 1443 | 1283 | DeepSeek V4 Pro 0813 |
| LMArena Text: entertainment and media | 1424 | 1268 | DeepSeek V4 Pro 0813 |
| LMArena Vision: English | Not measured | 1170 | Only GPT-5 Nano measured |
| LMArena Vision: Chinese | Not measured | 1176 | Only GPT-5 Nano measured |
| LMArena Vision: reading text | Not measured | 1152 | Only GPT-5 Nano measured |
| LMArena Vision: humor | Not measured | 1110 | Only GPT-5 Nano measured |
| Neither model has a result on the other 18 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Pro 0813 | 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
GPT-5 Nano costs less
Repository review
50K input + 3K output tokens
GPT-5 Nano costs less
Agent loop
200K input + 10K output tokens
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 Pro 0813 | OpenAI GPT-5 Nano |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | August 12, 2026 | August 7, 2025 |
| Context window | 1.05M | 400K |
| Input $/Mtok | $0.66 | $0.05 |
| Output $/Mtok | $1.98 | $0.40 |
| Coding index | 56.1 | Not benchmarked |
| Independent measurements behind it | 5 | 0 |
| Cost per solved task | $0.10 | $0.109 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Pro 0813 better than GPT-5 Nano for coding?
Only DeepSeek V4 Pro 0813 has a coding score, 56.1 on the Vibe Coding Index; GPT-5 Nano has none, so the two cannot be ranked on coding. Pick GPT-5 Nano for output tokens 4.9x cheaper ($0.40 vs $1.98 per million).
Which is cheaper, DeepSeek V4 Pro 0813 or GPT-5 Nano?
DeepSeek V4 Pro 0813 costs $0.66 per million input tokens and $1.98 per million output tokens; GPT-5 Nano costs $0.05 and $0.40. Per solved SWE-bench Verified task, DeepSeek V4 Pro 0813 costs $0.10 and GPT-5 Nano $0.109.
Which has the bigger context window, DeepSeek V4 Pro 0813 or GPT-5 Nano?
DeepSeek V4 Pro 0813, with 1.05M tokens against 400K.
How many benchmarks is the DeepSeek V4 Pro 0813 vs GPT-5 Nano comparison based on?
49 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4 Pro 0813's coding index rests on 5 independent benchmarks and GPT-5 Nano's on 0. Where only one model has a result, the page says so instead of naming a winner.