DeepSeek V3 0324 vs o3
o3 leads DeepSeek V3 0324 on the Artificial Analysis Intelligence Index, 20.2 to 9.7, and DeepSeek V3 0324 costs 7.5x less per output token ($1.07 vs $8.00 per million). Updated October 22, 2026; 32 benchmarks have a result for both.
DeepSeek V3 0324 or o3: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick DeepSeek V3 0324 for output tokens 7.5x cheaper ($1.07 vs $8.00 per million) and work measured by SWE-bench Verified or LiveCodeBench (Vals AI), where it leads.
- Shared results
- 32
- DeepSeek V3 0324 only
- 3
- o3 only
- 70
- 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
o3
o3 leads, 20.2 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
o3
o3 leads, 84.1% against 61.6%.
Same board, both measured
Math
AIME
o3
o3 leads, 85.3% against 52.2%.
Same board, both measured
Knowledge
MMLU Pro
o3
o3 leads, 85.6% against 79.5%.
Same board, both measured
Web design
Design Arena: websites
No call
Only o3 has a Design Arena: websites result, 1036.
Directional only
Long documents
Listed context window
o3
o3 takes 200K tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3 0324
DeepSeek V3 0324 $0.0008, o3 $0.0060 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only o3 has the number, $0.571.
Directional only
At a glance
- Coding index
- DeepSeek V3 0324 not benchmarked, o3 not benchmarked.
- Widest category
- Math, 29.0 points apart on average over 7 shared benchmarks.
- Widest single gap
- AIME 2025 (Artificial Analysis run): DeepSeek V3 0324 41.0% against o3 88.3%, 47.3 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3 0324 $1.07 against o3 $8.00 per million output tokens, 7.5x apart.
- Context window
- DeepSeek V3 0324 takes 164K tokens, o3 200K.
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 | o3 | Ties |
|---|---|---|---|---|
| Coding | 0 | 5 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 2 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 7 | ||
| Knowledge | 0 | 6 | ||
| General | 0 | 6 | ||
| All shared boards | 0 | 32 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index05
| Benchmark | DeepSeek V3 0324 | o3 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 81.3% | Only o3 measured |
| SWE-bench Verified | 42.0% | 58.4% | o3 |
| SWE-bench Multimodal | Not measured | 36.0% | Only o3 measured |
| AA Coding Index | 21.2 | Not measured | Only DeepSeek V3 0324 measured |
| LiveCodeBench (Vals AI) | 65.5% | 83.9% | o3 |
| LiveCodeBench (Artificial Analysis run) | 40.5% | 80.8% | o3 |
| SciCode | 39.0% | Not measured | Only DeepSeek V3 0324 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 15.2% | 37.1% | o3 |
| GSO-Bench | Not measured | 8.8% | Only o3 measured |
| WeirdML | 36.1% | 52.4% | o3 |
| ALE-Bench | Not measured | 933.5 | Only o3 measured |
| LMArena Text: coding | Not measured | 1407 | Only o3 measured |
| LMArena Text: software and IT | Not measured | 1415 | Only o3 measured |
| SciCode | Not measured | 9.2% | Only o3 measured |
| Neither model has a result on the other 25 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3 0324 | o3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 9.7 | 20.2 | o3 |
| Epoch Capabilities Index | Not measured | 146.7 | Only o3 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3 0324 | o3 | Leader |
|---|---|---|---|
| AA Agentic Index | 0.8 | Not measured | Only DeepSeek V3 0324 measured |
| τ²-Bench Telecom | 47.1% | 80.7% | o3 |
| METR time horizons | 49.6% | 65.4% | o3 |
| GDPval | Not measured | 30.8% | Only o3 measured |
| DeepResearch Bench | Not measured | 45.2% | Only o3 measured |
| OSWorld | Not measured | 23.0% | Only o3 measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 63.0% | Only o3 measured |
| BrowseComp-Plus | Not measured | 65.9% | Only o3 measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | DeepSeek V3 0324 | o3 | Leader |
|---|---|---|---|
| GPQA Diamond | 61.6% | 84.1% | o3 |
| MMMU Pro (Vals AI) | Not measured | 80.4% | Only o3 measured |
| GPQA Diamond (Artificial Analysis run) | 65.5% | 82.7% | o3 |
| Humanity's Last Exam (Artificial Analysis run) | 4.7% | 20.1% | o3 |
| GPQA Diamond (Epoch AI run) | 67.6% | 81.8% | o3 |
| ARC-AGI | Not measured | 60.8% | Only o3 measured |
| ARC-AGI-2 | Not measured | 6.5% | Only o3 measured |
| SimpleBench | 27.2% | 53.1% | o3 |
| Chess puzzles | Not measured | 37.0% | Only o3 measured |
| Mystery game puzzles | Not measured | 29.0% | Only o3 measured |
| VPCT | Not measured | 52.0% | Only o3 measured |
| LMArena Text: hard prompts | Not measured | 1402 | Only o3 measured |
| LMArena Text: expert | Not measured | 1402 | Only o3 measured |
| LMArena Text: hard prompts in English | Not measured | 1411 | Only o3 measured |
| LMArena Vision: diagrams | Not measured | 1220 | Only o3 measured |
| LMArena Vision: homework | Not measured | 1234 | Only o3 measured |
| Kagi LLM Benchmark | Not measured | 67.6% | Only o3 measured |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | DeepSeek V3 0324 | o3 | Leader |
|---|---|---|---|
| AIME | 52.2% | 85.3% | o3 |
| MATH 500 (Vals AI) | 88.6% | 94.6% | o3 |
| MGSM (Vals AI) | 91.7% | 91.8% | o3 |
| AA Math Index | 41.0 | 88.3 | o3 |
| AIME 2025 (Artificial Analysis run) | 41.0% | 88.3% | o3 |
| OTIS Mock AIME | 37.8% | 84.4% | o3 |
| FrontierMath, tiers 1 to 3 | Not measured | 33.3% | Only o3 measured |
| MATH level 5 | 75.5% | 97.8% | o3 |
| LMArena Text: math | Not measured | 1426 | Only o3 measured |
| LMArena Text: mathematical work | Not measured | 1423 | Only o3 measured |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | DeepSeek V3 0324 | o3 | Leader |
|---|---|---|---|
| MMLU Pro | 79.5% | 85.6% | o3 |
| LegalBench (Vals AI) | 77.7% | 83.8% | o3 |
| CorpFin (Vals AI) | 54.7% | 59.7% | o3 |
| TaxEval (Vals AI) | 71.1% | 74.6% | o3 |
| MortgageTax (Vals AI) | Not measured | 65.7% | Only o3 measured |
| MedQA (Vals AI) | 82.0% | 96.1% | o3 |
| MedCode (Vals AI) | Not measured | 47.3% | Only o3 measured |
| MMLU Pro (Artificial Analysis run) | 81.9% | 85.3% | o3 |
| SimpleQA Verified | Not measured | 49.4% | Only o3 measured |
| GeoBench | Not measured | 74.0% | Only o3 measured |
| LMArena Text: science | Not measured | 1429 | Only o3 measured |
| LMArena Text: medicine and healthcare | Not measured | 1448 | Only o3 measured |
| LMArena Text: legal and government | Not measured | 1428 | Only o3 measured |
| LMArena Text: business and finance | Not measured | 1393 | Only o3 measured |
| FACTS Benchmark Suite | Not measured | 44.4% | Only o3 measured |
| Neither model has a result on the other 3 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | DeepSeek V3 0324 | o3 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1036 | Only o3 measured |
| Design Arena: UI components | Not measured | 1017 | Only o3 measured |
| Design Arena: game dev | Not measured | 1034 | Only o3 measured |
| Design Arena: data viz | Not measured | 1200 | Only o3 measured |
| Design Arena: all code categories | Not measured | 1020 | Only o3 measured |
| Neither model has a result on the other 4 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | DeepSeek V3 0324 | o3 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 41.8% | Only o3 measured |
| MedScribe (Vals AI) | Not measured | 76.7% | Only o3 measured |
| IFBench | 41.0% | 71.4% | o3 |
| Long context reasoning (AA-LCR) | 40.7% | 74.7% | o3 |
| DTBench | 64.8% | 84.8% | o3 |
| LMCA | 15.5% | 39.7% | o3 |
| Fiction.LiveBench | 50.0% | 88.9% | o3 |
| Creative writing (Lech Mazur) | 77.0% | 83.9% | o3 |
| CL-bench | Not measured | 17.8% | Only o3 measured |
| LMArena Text | Not measured | 1410 | Only o3 measured |
| LMArena Text: instruction following | Not measured | 1368 | Only o3 measured |
| LMArena Text: creative writing | Not measured | 1359 | Only o3 measured |
| LMArena Vision | Not measured | 1214 | Only o3 measured |
| LMArena Text: English | Not measured | 1417 | Only o3 measured |
| LMArena Text: non-English | Not measured | 1401 | Only o3 measured |
| LMArena Text: Chinese | Not measured | 1437 | Only o3 measured |
| LMArena Text: German | Not measured | 1420 | Only o3 measured |
| LMArena Text: Japanese | Not measured | 1403 | Only o3 measured |
| LMArena Text: Korean | Not measured | 1370 | Only o3 measured |
| LMArena Text: French | Not measured | 1430 | Only o3 measured |
| LMArena Text: Spanish | Not measured | 1395 | Only o3 measured |
| LMArena Text: Russian | Not measured | 1406 | Only o3 measured |
| LMArena Text: Polish | Not measured | 1438 | Only o3 measured |
| LMArena Text: multi-turn | Not measured | 1405 | Only o3 measured |
| LMArena Text: longer queries | Not measured | 1372 | Only o3 measured |
| LMArena Text: writing and language | Not measured | 1372 | Only o3 measured |
| LMArena Text: entertainment and media | Not measured | 1373 | Only o3 measured |
| LMArena Vision: English | Not measured | 1221 | Only o3 measured |
| LMArena Vision: Chinese | Not measured | 1237 | Only o3 measured |
| LMArena Vision: reading text | Not measured | 1218 | Only o3 measured |
| LMArena Vision: humor | Not measured | 1222 | Only o3 measured |
| LMArena Vision: captioning | Not measured | 1210 | Only o3 measured |
| LMArena Vision: recognizing things | Not measured | 1234 | Only o3 measured |
| LMArena Vision: creative writing | Not measured | 1201 | Only o3 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 V3 0324 | o3 | 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 o3 |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | March 24, 2025 | April 16, 2025 |
| Context window | 164K | 200K |
| Input $/Mtok | $0.27 | $2.00 |
| Output $/Mtok | $1.07 | $8.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | $0.571 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3 0324 better than o3 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3 0324 for output tokens 7.5x cheaper ($1.07 vs $8.00 per million) and work measured by SWE-bench Verified or LiveCodeBench (Vals AI), where it leads.
Which is cheaper, DeepSeek V3 0324 or o3?
DeepSeek V3 0324 costs $0.27 per million input tokens and $1.07 per million output tokens; o3 costs $2.00 and $8.00. Cost per solved task is known for o3 only.
Which has the bigger context window, DeepSeek V3 0324 or o3?
o3, with 200K tokens against 164K.
How many benchmarks is the DeepSeek V3 0324 vs o3 comparison based on?
32 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3 0324's coding index rests on 0 independent benchmarks and o3's on 0. Where only one model has a result, the page says so instead of naming a winner.