DeepSeek V3 0324 vs GPT-5.2
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 GPT-5.2 for coding: it scores 58.9 on the Vibe Coding Index against 42.0 for DeepSeek V3 0324.
Pick DeepSeek V3 0324 when output tokens cost 12.3x less.
- Shared results
- 28
- DeepSeek V3 0324 only
- 7
- GPT-5.2 only
- 83
- 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
GPT-5.2
GPT-5.2 scores 58.9 against 42.0.
1 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.2
GPT-5.2 leads, 30.4 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-5.2
GPT-5.2 leads, 91.7% against 61.6%.
Same board, both measured
Math
AIME
GPT-5.2
GPT-5.2 leads, 96.9% against 52.2%.
Same board, both measured
Knowledge
MMLU Pro
GPT-5.2
GPT-5.2 leads, 86.2% against 79.5%.
Same board, both measured
Web design
Design Arena: websites
No call
Only GPT-5.2 has a Design Arena: websites result, 1196.
Directional only
Long documents
Listed context window
GPT-5.2
GPT-5.2 takes 400K tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3 0324
DeepSeek V3 0324 $0.0009, GPT-5.2 $0.0088 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.2 has the number, $1.91.
Directional only
At a glance
- Coding index
- DeepSeek V3 0324 42.0 from 1 benchmark, GPT-5.2 58.9 from 2 benchmarks.
- Widest category
- Math, 44.3 points apart on average over 5 shared benchmarks.
- Widest single gap
- OTIS Mock AIME: DeepSeek V3 0324 37.8% against GPT-5.2 96.1%, 58.3 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3 0324 $1.14 against GPT-5.2 $14.00 per million output tokens, 12.3x apart.
- Context window
- DeepSeek V3 0324 takes 164K tokens, GPT-5.2 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-5.2 | Ties |
|---|---|---|---|---|
| Coding | 0 | 5 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 2 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 5 | ||
| Knowledge | 0 | 6 | ||
| General | 0 | 4 | ||
| All shared boards | 0 | 28 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index05
| Benchmark | DeepSeek V3 0324 | GPT-5.2 | Leader |
|---|---|---|---|
| SWE-bench Verified | 42.0% | 72.8% | GPT-5.2 |
| SWE-bench Multilingual | Not measured | 66.7% | Only GPT-5.2 measured |
| LMArena WebDev | Not measured | 1417 | Only GPT-5.2 measured |
| AA Coding Index | 21.2 | Not measured | Only DeepSeek V3 0324 measured |
| LiveCodeBench (Vals AI) | 65.5% | 85.4% | GPT-5.2 |
| SWE-bench Verified (Vals AI) | Not measured | 75.8% | Only GPT-5.2 measured |
| Vibe Code Bench (Vals AI) | Not measured | 53.5% | Only GPT-5.2 measured |
| LiveCodeBench (Artificial Analysis run) | 40.5% | 89.4% | GPT-5.2 |
| SciCode | 39.0% | Not measured | Only DeepSeek V3 0324 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 15.2% | 47.0% | GPT-5.2 |
| Terminal-Bench | Not measured | 64.9% | Only GPT-5.2 measured |
| GSO-Bench | Not measured | 27.5% | Only GPT-5.2 measured |
| WeirdML | 36.1% | 72.2% | GPT-5.2 |
| ALE-Bench | Not measured | 1293.5 | Only GPT-5.2 measured |
| LMArena Text: coding | Not measured | 1442 | Only GPT-5.2 measured |
| LMArena Text: software and IT | Not measured | 1434 | Only GPT-5.2 measured |
| LMArena WebDev: HTML | Not measured | 1427 | Only GPT-5.2 measured |
| Neither model has a result on the other 21 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3 0324 | GPT-5.2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 9.7 | 30.4 | GPT-5.2 |
| Epoch Capabilities Index | Not measured | 153.7 | Only GPT-5.2 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3 0324 | GPT-5.2 | Leader |
|---|---|---|---|
| AA Agentic Index | 0.8 | Not measured | Only DeepSeek V3 0324 measured |
| Design Arena: agents building web apps | Not measured | 1086 | Only GPT-5.2 measured |
| Design Arena: agents building full-stack apps | Not measured | 1043 | Only GPT-5.2 measured |
| Design Arena: agents building mobile apps | Not measured | 1094 | Only GPT-5.2 measured |
| Design Arena: agents building Android apps | Not measured | 1064 | Only GPT-5.2 measured |
| Design Arena: agents building Godot games | Not measured | 1142 | Only GPT-5.2 measured |
| τ²-Bench Telecom | 47.1% | 84.8% | GPT-5.2 |
| METR time horizons | 49.6% | 75.3% | GPT-5.2 |
| Remote Labor Index | Not measured | 2.5% | Only GPT-5.2 measured |
| GDPval | Not measured | 49.7% | Only GPT-5.2 measured |
| DeepResearch Bench | Not measured | 41.1% | Only GPT-5.2 measured |
| Vending-Bench 2 | Not measured | $3,591 | Only GPT-5.2 measured |
| Neither model has a result on the other 27 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | DeepSeek V3 0324 | GPT-5.2 | Leader |
|---|---|---|---|
| GPQA Diamond | 61.6% | 91.7% | GPT-5.2 |
| MMMU Pro (Vals AI) | Not measured | 86.7% | Only GPT-5.2 measured |
| GPQA Diamond (Artificial Analysis run) | 65.5% | 90.3% | GPT-5.2 |
| Humanity's Last Exam (Artificial Analysis run) | 4.7% | 37.7% | GPT-5.2 |
| GPQA Diamond (Epoch AI run) | 67.6% | 91.4% | GPT-5.2 |
| ARC-AGI | Not measured | 86.2% | Only GPT-5.2 measured |
| ARC-AGI-2 | Not measured | 52.9% | Only GPT-5.2 measured |
| SimpleBench | 27.2% | 45.8% | GPT-5.2 |
| Chess puzzles | Not measured | 48.0% | Only GPT-5.2 measured |
| Mystery game puzzles | Not measured | 23.0% | Only GPT-5.2 measured |
| EBR-bench | Not measured | 23.0% | Only GPT-5.2 measured |
| VPCT | Not measured | 84.0% | Only GPT-5.2 measured |
| LMArena Text: hard prompts | Not measured | 1428 | Only GPT-5.2 measured |
| LMArena Text: expert | Not measured | 1445 | Only GPT-5.2 measured |
| LMArena Text: hard prompts in English | Not measured | 1431 | Only GPT-5.2 measured |
| LMArena Vision: diagrams | Not measured | 1265 | Only GPT-5.2 measured |
| LMArena Vision: homework | Not measured | 1276 | Only GPT-5.2 measured |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | DeepSeek V3 0324 | GPT-5.2 | Leader |
|---|---|---|---|
| AIME | 52.2% | 96.9% | GPT-5.2 |
| MATH 500 (Vals AI) | 88.6% | Not measured | Only DeepSeek V3 0324 measured |
| MGSM (Vals AI) | 91.7% | 94.0% | GPT-5.2 |
| AA Math Index | 41.0 | 99.0 | GPT-5.2 |
| AIME 2025 (Artificial Analysis run) | 41.0% | 99.0% | GPT-5.2 |
| OTIS Mock AIME | 37.8% | 96.1% | GPT-5.2 |
| FrontierMath, tiers 1 to 3 | Not measured | 67.4% | Only GPT-5.2 measured |
| FrontierMath, tier 4 | Not measured | 31.7% | Only GPT-5.2 measured |
| ProofBench | Not measured | 15.0% | Only GPT-5.2 measured |
| MATH level 5 | 75.5% | Not measured | Only DeepSeek V3 0324 measured |
| LMArena Text: math | Not measured | 1440 | Only GPT-5.2 measured |
| LMArena Text: mathematical work | Not measured | 1442 | Only GPT-5.2 measured |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | DeepSeek V3 0324 | GPT-5.2 | Leader |
|---|---|---|---|
| MMLU Pro | 79.5% | 86.2% | GPT-5.2 |
| LegalBench (Vals AI) | 77.7% | 82.8% | GPT-5.2 |
| CaseLaw (Vals AI) | Not measured | 66.0% | Only GPT-5.2 measured |
| CorpFin (Vals AI) | 54.7% | 65.9% | GPT-5.2 |
| TaxEval (Vals AI) | 71.1% | 75.8% | GPT-5.2 |
| MortgageTax (Vals AI) | Not measured | 67.1% | Only GPT-5.2 measured |
| MedQA (Vals AI) | 82.0% | 94.1% | GPT-5.2 |
| MedCode (Vals AI) | Not measured | 49.8% | Only GPT-5.2 measured |
| MMLU Pro (Artificial Analysis run) | 81.9% | 87.4% | GPT-5.2 |
| SimpleQA Verified | Not measured | 37.1% | Only GPT-5.2 measured |
| LMArena Text: science | Not measured | 1425 | Only GPT-5.2 measured |
| LMArena Text: medicine and healthcare | Not measured | 1425 | Only GPT-5.2 measured |
| LMArena Text: legal and government | Not measured | 1427 | Only GPT-5.2 measured |
| LMArena Text: business and finance | Not measured | 1414 | Only GPT-5.2 measured |
| Neither model has a result on the other 3 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | DeepSeek V3 0324 | GPT-5.2 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1196 | Only GPT-5.2 measured |
| Design Arena: UI components | Not measured | 1190 | Only GPT-5.2 measured |
| Design Arena: game dev | Not measured | 1196 | Only GPT-5.2 measured |
| Design Arena: data viz | Not measured | 1202 | Only GPT-5.2 measured |
| Design Arena: 3D | Not measured | 1079 | Only GPT-5.2 measured |
| Design Arena: all code categories | Not measured | 1171 | Only GPT-5.2 measured |
| Design Arena: SVG | Not measured | 1137 | Only GPT-5.2 measured |
| Design Arena: ASCII art | Not measured | 1153 | Only GPT-5.2 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | DeepSeek V3 0324 | GPT-5.2 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 49.3% | Only GPT-5.2 measured |
| MedScribe (Vals AI) | Not measured | 84.4% | Only GPT-5.2 measured |
| IFBench | 41.0% | 75.4% | GPT-5.2 |
| Long context reasoning (AA-LCR) | 40.7% | 82.7% | GPT-5.2 |
| DTBench | 64.8% | 90.9% | GPT-5.2 |
| LMCA | 15.5% | 43.9% | GPT-5.2 |
| Fiction.LiveBench | 50.0% | Not measured | Only DeepSeek V3 0324 measured |
| Creative writing (Lech Mazur) | 77.0% | Not measured | Only DeepSeek V3 0324 measured |
| CL-bench | Not measured | 18.2% | Only GPT-5.2 measured |
| Furniture assembly | Not measured | 38.3% | Only GPT-5.2 measured |
| LMArena Text | Not measured | 1416 | Only GPT-5.2 measured |
| LMArena Text: instruction following | Not measured | 1409 | Only GPT-5.2 measured |
| LMArena Text: creative writing | Not measured | 1376 | Only GPT-5.2 measured |
| LMArena Vision | Not measured | 1246 | Only GPT-5.2 measured |
| LMArena Text: English | Not measured | 1422 | Only GPT-5.2 measured |
| LMArena Text: non-English | Not measured | 1405 | Only GPT-5.2 measured |
| LMArena Text: Chinese | Not measured | 1447 | Only GPT-5.2 measured |
| LMArena Text: German | Not measured | 1444 | Only GPT-5.2 measured |
| LMArena Text: Japanese | Not measured | 1420 | Only GPT-5.2 measured |
| LMArena Text: Korean | Not measured | 1374 | Only GPT-5.2 measured |
| LMArena Text: French | Not measured | 1454 | Only GPT-5.2 measured |
| LMArena Text: Spanish | Not measured | 1413 | Only GPT-5.2 measured |
| LMArena Text: Russian | Not measured | 1415 | Only GPT-5.2 measured |
| LMArena Text: Polish | Not measured | 1426 | Only GPT-5.2 measured |
| LMArena Text: multi-turn | Not measured | 1419 | Only GPT-5.2 measured |
| LMArena Text: longer queries | Not measured | 1413 | Only GPT-5.2 measured |
| LMArena Text: writing and language | Not measured | 1392 | Only GPT-5.2 measured |
| LMArena Text: entertainment and media | Not measured | 1378 | Only GPT-5.2 measured |
| LMArena Vision: English | Not measured | 1243 | Only GPT-5.2 measured |
| LMArena Vision: Chinese | Not measured | 1305 | Only GPT-5.2 measured |
| LMArena Vision: reading text | Not measured | 1257 | Only GPT-5.2 measured |
| LMArena Vision: humor | Not measured | 1227 | Only GPT-5.2 measured |
| LMArena Vision: captioning | Not measured | 1267 | Only GPT-5.2 measured |
| LMArena Vision: recognizing things | Not measured | 1232 | Only GPT-5.2 measured |
| LMArena Vision: creative writing | Not measured | 1225 | Only GPT-5.2 measured |
| LMArena Document | Not measured | 1405 | Only GPT-5.2 measured |
| Neither model has a result on the other 10 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3 0324 | GPT-5.2 | 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 V3 0324 costs less
Repository review
50K input + 3K output tokens
DeepSeek V3 0324 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V3 0324 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-5.2 |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | March 24, 2025 | December 10, 2025 |
| Context window | 164K | 400K |
| Input $/Mtok | $0.29 | $1.75 |
| Output $/Mtok | $1.14 | $14.00 |
| Coding index | 42.0 | 58.9 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | Not computable | $1.91 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3 0324 better than GPT-5.2 for coding?
Pick GPT-5.2 for coding: it scores 58.9 on the Vibe Coding Index against 42.0 for DeepSeek V3 0324. Pick DeepSeek V3 0324 when output tokens cost 12.3x less.
Which is cheaper, DeepSeek V3 0324 or GPT-5.2?
DeepSeek V3 0324 costs $0.29 per million input tokens and $1.14 per million output tokens; GPT-5.2 costs $1.75 and $14.00. Cost per solved task is known for GPT-5.2 only.
Which has the bigger context window, DeepSeek V3 0324 or GPT-5.2?
GPT-5.2, with 400K tokens against 164K.
How many benchmarks is the DeepSeek V3 0324 vs GPT-5.2 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-5.2's on 2. Where only one model has a result, the page says so instead of naming a winner.