DeepSeek V4 Pro 0423 vs GPT-5 Nano
DeepSeek V4 Pro 0423 leads GPT-5 Nano on the Artificial Analysis Intelligence Index, 30.4 to 13.0, and GPT-5 Nano costs 7.4x less per output token ($0.40 vs $2.976 per million). Updated October 22, 2026; 55 benchmarks have a result for both.
DeepSeek V4 Pro 0423 or GPT-5 Nano: which to pick
Only DeepSeek V4 Pro 0423 has a coding score, 38.7 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 7.4x cheaper ($0.40 vs $2.976 per million) and a lower cost per solved task ($0.109 against $0.57).
- Shared results
- 55
- DeepSeek V4 Pro 0423 only
- 29
- GPT-5 Nano only
- 32
- Like-for-like categories
- 7 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 0423 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 30.4 against 13.0.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only DeepSeek V4 Pro 0423 has a AA Agentic Index result, 26.3.
Directional only
Reasoning
GPQA Diamond
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 89.4% 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 0423
DeepSeek V4 Pro 0423 leads, 87.3% against 76.1%.
Same board, both measured
Web design
Design Arena: websites
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 1234 against 1101.
Same board, both measured
Long documents
Listed context window
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5 Nano
DeepSeek V4 Pro 0423 $0.0028, GPT-5 Nano $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5 Nano
DeepSeek V4 Pro 0423 $0.57, GPT-5 Nano $0.109.
Published attempt cost and pass rate
At a glance
- Coding index
- DeepSeek V4 Pro 0423 38.7 from 5 benchmarks, GPT-5 Nano not benchmarked.
- Widest category
- General, 24.2 points apart on average over 19 shared benchmarks.
- Widest single gap
- LMArena Text: Japanese: DeepSeek V4 Pro 0423 1446 against GPT-5 Nano 1227, 36.6 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Pro 0423 $2.976 against GPT-5 Nano $0.40 per million output tokens, 7.4x apart.
- Context window
- DeepSeek V4 Pro 0423 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 0423 | Bars | GPT-5 Nano | Ties |
|---|---|---|---|---|
| Coding | 4 | 0 | ||
| Intelligence | 2 | 0 | ||
| Reasoning | 6 | 2 | ||
| Math | 5 | 0 | 1 tie | |
| Knowledge | 10 | 0 | ||
| Web design | 6 | 0 | ||
| General | 19 | 0 | ||
| All shared boards | 52 | 2 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
| Benchmark | DeepSeek V4 Pro 0423 | GPT-5 Nano | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 34.8% | Only GPT-5 Nano measured |
| LMArena WebDev | 1447 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| AA Coding Index | 59.4 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LiveCodeBench (Vals AI) | 87.5% | 70.2% | DeepSeek V4 Pro 0423 |
| SWE-bench Verified (Vals AI) | 77.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Terminal-Bench 4 (Vals AI) | 11.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Vibe Code Bench (Vals AI) | 49.9% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Code migration (Vals AI) | 26.2% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Terminal-Bench 2.1 (Vals AI) | 50.2% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| SkillsBench (Vals AI) | 51.3% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 78.9% | Only GPT-5 Nano 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 | 17.6% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| WeirdML | 48.9% | 38.1% | DeepSeek V4 Pro 0423 |
| ALE-Bench | 1006.1 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: coding | 1470 | 1351 | DeepSeek V4 Pro 0423 |
| LMArena Text: software and IT | 1469 | 1345 | DeepSeek V4 Pro 0423 |
| LMArena WebDev: React | 1441 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena WebDev: HTML | 1435 | Not measured | Only DeepSeek V4 Pro 0423 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 0423 | GPT-5 Nano | Leader |
|---|---|---|---|
| AA Intelligence Index | 30.4 | 13.0 | DeepSeek V4 Pro 0423 |
| Epoch Capabilities Index | 149.1 | 139.2 | DeepSeek V4 Pro 0423 |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | DeepSeek V4 Pro 0423 | GPT-5 Nano | Leader |
|---|---|---|---|
| AA Agentic Index | 26.3 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Design Arena: agents building web apps | 1000 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Design Arena: agents building full-stack apps | 948 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Design Arena: agents building Godot games | 1059 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Finance Agent (Vals AI) | 44.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Tax Agent Bench (Vals AI) | 25.2% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Legal Research Bench (Vals AI) | 23.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Excel Modeling Benchmark (Vals AI) | 51.6% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| τ²-Bench Telecom | Not measured | 36.5% | Only GPT-5 Nano measured |
| Vending-Bench 2 | $3,285 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 51.4% | Only GPT-5 Nano measured |
| Neither model has a result on the other 30 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index62
| Benchmark | DeepSeek V4 Pro 0423 | GPT-5 Nano | Leader |
|---|---|---|---|
| GPQA Diamond | 89.4% | 63.4% | DeepSeek V4 Pro 0423 |
| 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) | Not measured | 9.5% | Only GPT-5 Nano measured |
| GPQA Diamond (Epoch AI run) | 90.9% | 69.4% | DeepSeek V4 Pro 0423 |
| ARC-AGI | Not measured | 20.7% | Only GPT-5 Nano measured |
| ARC-AGI-2 | Not measured | 2.6% | Only GPT-5 Nano measured |
| Chess puzzles | 19.0% | 22.0% | GPT-5 Nano |
| Mystery game puzzles | 17.0% | 9.0% | DeepSeek V4 Pro 0423 |
| VPCT | Not measured | 37.2% | Only GPT-5 Nano measured |
| LMArena Text: hard prompts | 1461 | 1328 | DeepSeek V4 Pro 0423 |
| LMArena Text: expert | 1463 | 1321 | DeepSeek V4 Pro 0423 |
| LMArena Text: hard prompts in English | 1466 | 1338 | DeepSeek V4 Pro 0423 |
| 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 | 53.5% | 62.2% | GPT-5 Nano |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | DeepSeek V4 Pro 0423 | 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 | 96.7% | 81.1% | DeepSeek V4 Pro 0423 |
| FrontierMath, tiers 1 to 3 | 45.3% | 20.0% | DeepSeek V4 Pro 0423 |
| FrontierMath, tier 4 | 2.4% | 2.4% | Tie |
| ProofBench | 16.0% | 12.0% | DeepSeek V4 Pro 0423 |
| MATH level 5 | Not measured | 95.2% | Only GPT-5 Nano measured |
| LMArena Text: math | 1443 | 1317 | DeepSeek V4 Pro 0423 |
| LMArena Text: mathematical work | 1455 | 1326 | DeepSeek V4 Pro 0423 |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index100
| Benchmark | DeepSeek V4 Pro 0423 | GPT-5 Nano | Leader |
|---|---|---|---|
| MMLU Pro | 87.3% | 76.1% | DeepSeek V4 Pro 0423 |
| LegalBench (Vals AI) | 80.3% | 50.1% | DeepSeek V4 Pro 0423 |
| CaseLaw (Vals AI) | 59.4% | 52.6% | DeepSeek V4 Pro 0423 |
| CorpFin (Vals AI) | 61.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| TaxEval (Vals AI) | 72.1% | 67.4% | DeepSeek V4 Pro 0423 |
| 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) | 40.5% | 30.4% | DeepSeek V4 Pro 0423 |
| MMLU Pro (Artificial Analysis run) | Not measured | 78.0% | Only GPT-5 Nano measured |
| SimpleQA Verified | 47.0% | 11.7% | DeepSeek V4 Pro 0423 |
| LMArena Text: science | 1470 | 1318 | DeepSeek V4 Pro 0423 |
| LMArena Text: medicine and healthcare | 1466 | 1335 | DeepSeek V4 Pro 0423 |
| LMArena Text: legal and government | 1464 | 1298 | DeepSeek V4 Pro 0423 |
| LMArena Text: business and finance | 1443 | 1311 | DeepSeek V4 Pro 0423 |
| 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 Pro 0423 | GPT-5 Nano | Leader |
|---|---|---|---|
| Design Arena: websites | 1234 | 1101 | DeepSeek V4 Pro 0423 |
| Design Arena: UI components | 1219 | 1064 | DeepSeek V4 Pro 0423 |
| Design Arena: game dev | 1228 | 1044 | DeepSeek V4 Pro 0423 |
| Design Arena: data viz | 1203 | 1056 | DeepSeek V4 Pro 0423 |
| Design Arena: 3D | 1253 | 965 | DeepSeek V4 Pro 0423 |
| Design Arena: all code categories | 1240 | 1084 | DeepSeek V4 Pro 0423 |
| Design Arena: SVG | 1139 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Design Arena: ASCII art | 1149 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index190
| Benchmark | DeepSeek V4 Pro 0423 | GPT-5 Nano | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 62.9% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| SAGE (Vals AI) | Not measured | 30.4% | Only GPT-5 Nano measured |
| MedScribe (Vals AI) | 75.1% | 72.9% | DeepSeek V4 Pro 0423 |
| IFBench | Not measured | 67.6% | Only GPT-5 Nano measured |
| Long context reasoning (AA-LCR) | Not measured | 45.0% | Only GPT-5 Nano measured |
| DTBench | 90.7% | 62.7% | DeepSeek V4 Pro 0423 |
| LMCA | 41.2% | 7.9% | DeepSeek V4 Pro 0423 |
| Fiction.LiveBench | Not measured | 44.4% | Only GPT-5 Nano measured |
| Surface Evolver Bench | 40.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| CL-bench Life | 13.5% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text | 1451 | 1320 | DeepSeek V4 Pro 0423 |
| LMArena Text: instruction following | 1445 | 1306 | DeepSeek V4 Pro 0423 |
| LMArena Text: creative writing | 1446 | 1249 | DeepSeek V4 Pro 0423 |
| LMArena Vision | Not measured | 1159 | Only GPT-5 Nano measured |
| LMArena Text: English | 1459 | 1321 | DeepSeek V4 Pro 0423 |
| LMArena Text: non-English | 1439 | 1313 | DeepSeek V4 Pro 0423 |
| LMArena Text: Chinese | 1481 | 1356 | DeepSeek V4 Pro 0423 |
| LMArena Text: German | 1458 | 1327 | DeepSeek V4 Pro 0423 |
| LMArena Text: Japanese | 1446 | 1227 | DeepSeek V4 Pro 0423 |
| LMArena Text: Korean | 1409 | 1269 | DeepSeek V4 Pro 0423 |
| LMArena Text: French | 1473 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: Spanish | 1457 | 1360 | DeepSeek V4 Pro 0423 |
| LMArena Text: Russian | 1453 | 1296 | DeepSeek V4 Pro 0423 |
| LMArena Text: Polish | 1461 | 1314 | DeepSeek V4 Pro 0423 |
| LMArena Text: multi-turn | 1468 | 1311 | DeepSeek V4 Pro 0423 |
| LMArena Text: longer queries | 1458 | 1312 | DeepSeek V4 Pro 0423 |
| LMArena Text: writing and language | 1447 | 1283 | DeepSeek V4 Pro 0423 |
| LMArena Text: entertainment and media | 1431 | 1268 | DeepSeek V4 Pro 0423 |
| 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 14 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Pro 0423 | 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 0423 | OpenAI GPT-5 Nano |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | April 24, 2026 | August 7, 2025 |
| Context window | 1.05M | 400K |
| Input $/Mtok | $1.358 | $0.05 |
| Output $/Mtok | $2.976 | $0.40 |
| Coding index | 38.7 | Not benchmarked |
| Independent measurements behind it | 5 | 0 |
| Cost per solved task | $0.57 | $0.109 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Pro 0423 better than GPT-5 Nano for coding?
Only DeepSeek V4 Pro 0423 has a coding score, 38.7 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 7.4x cheaper ($0.40 vs $2.976 per million) and a lower cost per solved task ($0.109 against $0.57).
Which is cheaper, DeepSeek V4 Pro 0423 or GPT-5 Nano?
DeepSeek V4 Pro 0423 costs $1.358 per million input tokens and $2.976 per million output tokens; GPT-5 Nano costs $0.05 and $0.40. Per solved SWE-bench Verified task, DeepSeek V4 Pro 0423 costs $0.57 and GPT-5 Nano $0.109.
Which has the bigger context window, DeepSeek V4 Pro 0423 or GPT-5 Nano?
DeepSeek V4 Pro 0423, with 1.05M tokens against 400K.
How many benchmarks is the DeepSeek V4 Pro 0423 vs GPT-5 Nano comparison based on?
55 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4 Pro 0423'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.