DeepSeek V3 0324 vs GPT-5 Nano
Updated October 21, 2026. 28 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick DeepSeek V3 0324 for coding: it scores 42.0 on the Vibe Coding Index against 34.8 for GPT-5 Nano.
Pick GPT-5 Nano when output tokens cost 2.8x less, or when the context window is larger, 400K against 164K tokens.
- Shared results
- 28
- DeepSeek V3 0324 only
- 7
- GPT-5 Nano only
- 57
- 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 V3 0324
DeepSeek V3 0324 scores 42.0 against 34.8.
One benchmark behind each score
Intelligence
AA Intelligence Index
GPT-5 Nano
GPT-5 Nano leads, 13.0 against 9.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only DeepSeek V3 0324 has a AA Agentic Index result, 0.8.
Directional only
Reasoning
GPQA Diamond
GPT-5 Nano
GPT-5 Nano leads, 63.4% against 61.6%.
Same board, both measured
Math
AIME
GPT-5 Nano
GPT-5 Nano leads, 81.2% against 52.2%.
Same board, both measured
Knowledge
MMLU Pro
DeepSeek V3 0324
DeepSeek V3 0324 leads, 79.5% 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
Long documents
Listed context window
GPT-5 Nano
GPT-5 Nano takes 400K tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5 Nano
DeepSeek V3 0324 $0.0009, 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 V3 0324 42.0 from 1 benchmark, GPT-5 Nano 34.8 from 1 benchmark.
- Widest category
- Math, 26.4 points apart on average over 7 shared benchmarks.
- Widest single gap
- OTIS Mock AIME: DeepSeek V3 0324 37.8% against GPT-5 Nano 81.1%, 43.3 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3 0324 $1.14 against GPT-5 Nano $0.40 per million output tokens, 2.8x apart.
- Context window
- DeepSeek V3 0324 takes 164K 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 V3 0324 | Bars | GPT-5 Nano | Ties |
|---|---|---|---|---|
| Coding | 1 | 4 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 0 | 4 | ||
| Math | 1 | 6 | ||
| Knowledge | 4 | 1 | ||
| General | 3 | 2 | ||
| All shared boards | 10 | 18 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index14
| Benchmark | DeepSeek V3 0324 | GPT-5 Nano | Leader |
|---|---|---|---|
| SWE-bench Verified | 42.0% | 34.8% | DeepSeek V3 0324 |
| AA Coding Index | 21.2 | Not measured | Only DeepSeek V3 0324 measured |
| LiveCodeBench (Vals AI) | 65.5% | 70.2% | GPT-5 Nano |
| LiveCodeBench (Artificial Analysis run) | 40.5% | 78.9% | GPT-5 Nano |
| SciCode | 39.0% | Not measured | Only DeepSeek V3 0324 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 15.2% | 17.4% | GPT-5 Nano |
| Terminal-Bench | Not measured | 21.8% | Only GPT-5 Nano measured |
| WeirdML | 36.1% | 38.1% | GPT-5 Nano |
| LMArena Text: coding | Not measured | 1351 | Only GPT-5 Nano measured |
| LMArena Text: software and IT | Not measured | 1345 | Only GPT-5 Nano measured |
| Neither model has a result on the other 28 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3 0324 | GPT-5 Nano | Leader |
|---|---|---|---|
| AA Intelligence Index | 9.7 | 13.0 | GPT-5 Nano |
| Epoch Capabilities Index | Not measured | 139.2 | Only GPT-5 Nano measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V3 0324 | GPT-5 Nano | Leader |
|---|---|---|---|
| AA Agentic Index | 0.8 | Not measured | Only DeepSeek V3 0324 measured |
| τ²-Bench Telecom | 47.1% | 36.5% | DeepSeek V3 0324 |
| METR time horizons | 49.6% | Not measured | Only DeepSeek V3 0324 measured |
| Neither model has a result on the other 36 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | DeepSeek V3 0324 | GPT-5 Nano | Leader |
|---|---|---|---|
| GPQA Diamond | 61.6% | 63.4% | GPT-5 Nano |
| MMMU Pro (Vals AI) | Not measured | 70.9% | Only GPT-5 Nano measured |
| GPQA Diamond (Artificial Analysis run) | 65.5% | 67.6% | GPT-5 Nano |
| Humanity's Last Exam (Artificial Analysis run) | 4.7% | 9.5% | GPT-5 Nano |
| GPQA Diamond (Epoch AI run) | 67.6% | 69.4% | GPT-5 Nano |
| ARC-AGI | Not measured | 20.7% | Only GPT-5 Nano measured |
| ARC-AGI-2 | Not measured | 2.6% | Only GPT-5 Nano measured |
| SimpleBench | 27.2% | Not measured | Only DeepSeek V3 0324 measured |
| Chess puzzles | Not measured | 22.0% | Only GPT-5 Nano measured |
| Mystery game puzzles | Not measured | 9.0% | Only GPT-5 Nano measured |
| VPCT | Not measured | 37.2% | Only GPT-5 Nano measured |
| LMArena Text: hard prompts | Not measured | 1328 | Only GPT-5 Nano measured |
| LMArena Text: expert | Not measured | 1321 | Only GPT-5 Nano measured |
| LMArena Text: hard prompts in English | Not measured | 1338 | Only GPT-5 Nano measured |
| LMArena Vision: diagrams | Not measured | 1141 | Only GPT-5 Nano measured |
| LMArena Vision: homework | Not measured | 1157 | Only GPT-5 Nano measured |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index16
| Benchmark | DeepSeek V3 0324 | GPT-5 Nano | Leader |
|---|---|---|---|
| AIME | 52.2% | 81.2% | GPT-5 Nano |
| MATH 500 (Vals AI) | 88.6% | 93.8% | GPT-5 Nano |
| MGSM (Vals AI) | 91.7% | 89.3% | DeepSeek V3 0324 |
| AA Math Index | 41.0 | 83.7 | GPT-5 Nano |
| AIME 2025 (Artificial Analysis run) | 41.0% | 83.7% | GPT-5 Nano |
| OTIS Mock AIME | 37.8% | 81.1% | GPT-5 Nano |
| FrontierMath, tiers 1 to 3 | Not measured | 20.0% | Only GPT-5 Nano measured |
| FrontierMath, tier 4 | Not measured | 2.4% | Only GPT-5 Nano measured |
| ProofBench | Not measured | 12.0% | Only GPT-5 Nano measured |
| MATH level 5 | 75.5% | 95.2% | GPT-5 Nano |
| LMArena Text: math | Not measured | 1317 | Only GPT-5 Nano measured |
| LMArena Text: mathematical work | Not measured | 1326 | 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 index41
| Benchmark | DeepSeek V3 0324 | GPT-5 Nano | Leader |
|---|---|---|---|
| MMLU Pro | 79.5% | 76.1% | DeepSeek V3 0324 |
| LegalBench (Vals AI) | 77.7% | 50.1% | DeepSeek V3 0324 |
| CaseLaw (Vals AI) | Not measured | 52.6% | Only GPT-5 Nano measured |
| CorpFin (Vals AI) | 54.7% | Not measured | Only DeepSeek V3 0324 measured |
| TaxEval (Vals AI) | 71.1% | 67.4% | DeepSeek V3 0324 |
| MortgageTax (Vals AI) | Not measured | 53.6% | Only GPT-5 Nano measured |
| MedQA (Vals AI) | 82.0% | 93.3% | GPT-5 Nano |
| MedCode (Vals AI) | Not measured | 30.4% | Only GPT-5 Nano measured |
| MMLU Pro (Artificial Analysis run) | 81.9% | 78.0% | DeepSeek V3 0324 |
| SimpleQA Verified | Not measured | 11.7% | Only GPT-5 Nano measured |
| LMArena Text: science | Not measured | 1318 | Only GPT-5 Nano measured |
| LMArena Text: medicine and healthcare | Not measured | 1335 | Only GPT-5 Nano measured |
| LMArena Text: legal and government | Not measured | 1298 | Only GPT-5 Nano measured |
| LMArena Text: business and finance | Not measured | 1311 | Only GPT-5 Nano measured |
| Neither model has a result on the other 3 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | DeepSeek V3 0324 | GPT-5 Nano | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1101 | Only GPT-5 Nano measured |
| Design Arena: UI components | Not measured | 1066 | Only GPT-5 Nano measured |
| Design Arena: game dev | Not measured | 1045 | Only GPT-5 Nano measured |
| Design Arena: data viz | Not measured | 1056 | Only GPT-5 Nano measured |
| Design Arena: 3D | Not measured | 966 | 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 index32
| Benchmark | DeepSeek V3 0324 | GPT-5 Nano | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 30.4% | Only GPT-5 Nano measured |
| MedScribe (Vals AI) | Not measured | 72.9% | Only GPT-5 Nano measured |
| IFBench | 41.0% | 67.6% | GPT-5 Nano |
| Long context reasoning (AA-LCR) | 40.7% | 45.0% | GPT-5 Nano |
| DTBench | 64.8% | 62.7% | DeepSeek V3 0324 |
| LMCA | 15.5% | 7.9% | DeepSeek V3 0324 |
| Fiction.LiveBench | 50.0% | 44.4% | DeepSeek V3 0324 |
| Creative writing (Lech Mazur) | 77.0% | Not measured | Only DeepSeek V3 0324 measured |
| LMArena Text | Not measured | 1320 | Only GPT-5 Nano measured |
| LMArena Text: instruction following | Not measured | 1306 | Only GPT-5 Nano measured |
| LMArena Text: creative writing | Not measured | 1249 | Only GPT-5 Nano measured |
| LMArena Vision | Not measured | 1159 | Only GPT-5 Nano measured |
| LMArena Text: English | Not measured | 1321 | Only GPT-5 Nano measured |
| LMArena Text: non-English | Not measured | 1313 | Only GPT-5 Nano measured |
| LMArena Text: Chinese | Not measured | 1356 | Only GPT-5 Nano measured |
| 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 | Not measured | 1269 | Only GPT-5 Nano measured |
| LMArena Text: Spanish | Not measured | 1360 | Only GPT-5 Nano measured |
| LMArena Text: Russian | Not measured | 1296 | Only GPT-5 Nano measured |
| LMArena Text: Polish | Not measured | 1314 | Only GPT-5 Nano measured |
| LMArena Text: multi-turn | Not measured | 1311 | Only GPT-5 Nano measured |
| LMArena Text: longer queries | Not measured | 1312 | Only GPT-5 Nano measured |
| LMArena Text: writing and language | Not measured | 1283 | Only GPT-5 Nano measured |
| LMArena Text: entertainment and media | Not measured | 1268 | Only GPT-5 Nano measured |
| 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 17 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3 0324 | 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 V3 0324 | OpenAI GPT-5 Nano |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | March 24, 2025 | August 7, 2025 |
| Context window | 164K | 400K |
| Input $/Mtok | $0.29 | $0.05 |
| Output $/Mtok | $1.14 | $0.40 |
| Coding index | 42.0 | 34.8 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | Not computable | $0.109 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3 0324 better than GPT-5 Nano for coding?
Pick DeepSeek V3 0324 for coding: it scores 42.0 on the Vibe Coding Index against 34.8 for GPT-5 Nano. Pick GPT-5 Nano when output tokens cost 2.8x less, or when the context window is larger, 400K against 164K tokens.
Which is cheaper, DeepSeek V3 0324 or GPT-5 Nano?
DeepSeek V3 0324 costs $0.29 per million input tokens and $1.14 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 V3 0324 or GPT-5 Nano?
GPT-5 Nano, with 400K tokens against 164K.
How many benchmarks is the DeepSeek V3 0324 vs GPT-5 Nano comparison based on?
28 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3 0324's coding index rests on 1 independent benchmark and GPT-5 Nano's on 1. Where only one model has a result, the page says so instead of naming a winner.