DeepSeek V3.1 vs o1
o1 leads DeepSeek V3.1 on the Artificial Analysis Intelligence Index, 15.2 to 13.7, and DeepSeek V3.1 costs 40.0x less per output token ($1.50 vs $60.00 per million). Updated October 21, 2026; 3 benchmarks have a result for both.
DeepSeek V3.1 or o1: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick DeepSeek V3.1 for output tokens 40.0x cheaper ($1.50 vs $60.00 per million) and work measured by AA Intelligence Index, where it leads.
- Shared results
- 3
- DeepSeek V3.1 only
- 8
- o1 only
- 66
- Like-for-like categories
- 2 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
o1
o1 leads, 15.2 against 13.7.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only o1 has a GPQA Diamond result, 73.2%.
Directional only
Math
AIME
No call
Only o1 has a AIME result, 71.5%.
Directional only
Knowledge
MMLU Pro
No call
Only o1 has a MMLU Pro result, 83.5%.
Directional only
Web design
Design Arena: websites
No call
Only DeepSeek V3.1 has a Design Arena: websites result, 1123.
Directional only
Long documents
Listed context window
o1
o1 takes 200K tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.1
DeepSeek V3.1 $0.0013, o1 $0.045 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3.1 not benchmarked, o1 not benchmarked.
- Widest category
- General, 5.0 points apart on average over 2 shared benchmarks.
- Widest single gap
- DTBench: DeepSeek V3.1 82.7% against o1 74.7%, 8.0 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.1 $1.50 against o1 $60.00 per million output tokens, 40.0x apart.
- Context window
- DeepSeek V3.1 takes 164K tokens, o1 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 V3.1 | Bars | o1 | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| General | 2 | 0 | ||
| All shared boards | 2 | 1 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index9 boards, none shared
| Benchmark | DeepSeek V3.1 | o1 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 61.7% | Only o1 measured |
| AA Coding Index | Not measured | 39.7 | Only o1 measured |
| LiveCodeBench (Vals AI) | Not measured | 50.3% | Only o1 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 67.9% | Only o1 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 12.9% | Only o1 measured |
| WeirdML | Not measured | 46.1% | Only o1 measured |
| LMArena Text: coding | Not measured | 1367 | Only o1 measured |
| LMArena Text: software and IT | Not measured | 1366 | Only o1 measured |
| SciCode | Not measured | 4.6% | Only o1 measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.1 | o1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.7 | 15.2 | o1 |
| Epoch Capabilities Index | Not measured | 141.7 | Only o1 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V3.1 | o1 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 62.6% | Only o1 measured |
| METR time horizons | Not measured | 51.1% | Only o1 measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index13 boards, none shared
| Benchmark | DeepSeek V3.1 | o1 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 73.2% | Only o1 measured |
| MMMU Pro (Vals AI) | Not measured | 77.4% | Only o1 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 74.7% | Only o1 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 7.0% | Only o1 measured |
| GPQA Diamond (Epoch AI run) | Not measured | 76.8% | Only o1 measured |
| ARC-AGI | Not measured | 30.7% | Only o1 measured |
| SimpleBench | Not measured | 40.1% | Only o1 measured |
| Chess puzzles | Not measured | 10.0% | Only o1 measured |
| VPCT | Not measured | 37.0% | Only o1 measured |
| LMArena Text: hard prompts | Not measured | 1371 | Only o1 measured |
| LMArena Text: expert | Not measured | 1361 | Only o1 measured |
| LMArena Text: hard prompts in English | Not measured | 1372 | Only o1 measured |
| Kagi LLM Benchmark | 53.2% | Not measured | Only DeepSeek V3.1 measured |
| Neither model has a result on the other 9 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | DeepSeek V3.1 | o1 | Leader |
|---|---|---|---|
| AIME | Not measured | 71.5% | Only o1 measured |
| MATH 500 (Vals AI) | Not measured | 90.4% | Only o1 measured |
| MGSM (Vals AI) | Not measured | 89.3% | Only o1 measured |
| OTIS Mock AIME | Not measured | 73.3% | Only o1 measured |
| FrontierMath, tiers 1 to 3 | Not measured | 14.7% | Only o1 measured |
| MATH level 5 | Not measured | 94.7% | Only o1 measured |
| LMArena Text: math | Not measured | 1388 | Only o1 measured |
| LMArena Text: mathematical work | Not measured | 1390 | Only o1 measured |
| Neither model has a result on the other 5 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | DeepSeek V3.1 | o1 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 83.5% | Only o1 measured |
| LegalBench (Vals AI) | Not measured | 80.4% | Only o1 measured |
| TaxEval (Vals AI) | Not measured | 74.3% | Only o1 measured |
| MedQA (Vals AI) | Not measured | 96.5% | Only o1 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 84.1% | Only o1 measured |
| SimpleQA Verified | Not measured | 41.1% | Only o1 measured |
| GeoBench | Not measured | 80.0% | Only o1 measured |
| LMArena Text: science | Not measured | 1369 | Only o1 measured |
| LMArena Text: medicine and healthcare | Not measured | 1355 | Only o1 measured |
| LMArena Text: legal and government | Not measured | 1368 | Only o1 measured |
| LMArena Text: business and finance | Not measured | 1328 | Only o1 measured |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | DeepSeek V3.1 | o1 | Leader |
|---|---|---|---|
| Design Arena: websites | 1123 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: UI components | 1085 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: game dev | 1084 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: data viz | 1095 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: 3D | 1077 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: all code categories | 1112 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: SVG | 967 | Not measured | Only DeepSeek V3.1 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V3.1 | o1 | Leader |
|---|---|---|---|
| IFBench | Not measured | 70.3% | Only o1 measured |
| Long context reasoning (AA-LCR) | Not measured | 65.0% | Only o1 measured |
| DTBench | 82.7% | 74.7% | DeepSeek V3.1 |
| LMCA | 24.3% | 22.3% | DeepSeek V3.1 |
| Fiction.LiveBench | Not measured | 83.3% | Only o1 measured |
| Creative writing (Lech Mazur) | Not measured | 70.2% | Only o1 measured |
| LMArena Text | Not measured | 1366 | Only o1 measured |
| LMArena Text: instruction following | Not measured | 1367 | Only o1 measured |
| LMArena Text: creative writing | Not measured | 1347 | Only o1 measured |
| LMArena Vision | Not measured | 1168 | Only o1 measured |
| LMArena Text: English | Not measured | 1372 | Only o1 measured |
| LMArena Text: non-English | Not measured | 1358 | Only o1 measured |
| LMArena Text: Chinese | Not measured | 1394 | Only o1 measured |
| LMArena Text: German | Not measured | 1337 | Only o1 measured |
| LMArena Text: Japanese | Not measured | 1346 | Only o1 measured |
| LMArena Text: Korean | Not measured | 1396 | Only o1 measured |
| LMArena Text: French | Not measured | 1344 | Only o1 measured |
| LMArena Text: Spanish | Not measured | 1345 | Only o1 measured |
| LMArena Text: Russian | Not measured | 1356 | Only o1 measured |
| LMArena Text: multi-turn | Not measured | 1355 | Only o1 measured |
| LMArena Text: longer queries | Not measured | 1378 | Only o1 measured |
| LMArena Text: writing and language | Not measured | 1360 | Only o1 measured |
| LMArena Text: entertainment and media | Not measured | 1358 | Only o1 measured |
| LMArena Vision: English | Not measured | 1162 | Only o1 measured |
| LMArena Vision: Chinese | Not measured | 1235 | Only o1 measured |
| Neither model has a result on the other 21 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.1 | o1 | 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 V3.1 costs less
Repository review
50K input + 3K output tokens
DeepSeek V3.1 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V3.1 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 V3.1 | OpenAI o1 |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | August 21, 2025 | December 17, 2024 |
| Context window | 164K | 200K |
| Input $/Mtok | $0.55 | $15.00 |
| Output $/Mtok | $1.50 | $60.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.1 better than o1 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3.1 for output tokens 40.0x cheaper ($1.50 vs $60.00 per million) and work measured by AA Intelligence Index, where it leads.
Which is cheaper, DeepSeek V3.1 or o1?
DeepSeek V3.1 costs $0.55 per million input tokens and $1.50 per million output tokens; o1 costs $15.00 and $60.00. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V3.1 or o1?
o1, with 200K tokens against 164K.
How many benchmarks is the DeepSeek V3.1 vs o1 comparison based on?
3 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.1's coding index rests on 0 independent benchmarks and o1's on 0. Where only one model has a result, the page says so instead of naming a winner.