DeepSeek V3.1 Terminus vs o1
Updated October 21, 2026. 31 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
DeepSeek V3.1 Terminus has the edge where output tokens cost 59.2x less, and where what matters is AA Intelligence Index or τ²-Bench Telecom, where it leads.
- Shared results
- 31
- DeepSeek V3.1 Terminus only
- 13
- o1 only
- 37
- 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, 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 14.8.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only DeepSeek V3.1 Terminus has a AA Agentic Index result, 6.8.
Directional only
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 Terminus has a Design Arena: websites result, 1187.
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 Terminus
DeepSeek V3.1 Terminus $0.0008, o1 $0.045 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3.1 Terminus not benchmarked, o1 not benchmarked.
- Widest category
- Agentic work, 25.4 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: DeepSeek V3.1 Terminus 37.1% against o1 62.6%, 25.4 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.1 Terminus $1.0142 against o1 $60.00 per million output tokens, 59.2x apart.
- Context window
- DeepSeek V3.1 Terminus 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 Terminus | Bars | o1 | Ties |
|---|---|---|---|---|
| Coding | 5 | 0 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 4 | 0 | ||
| Math | 1 | 0 | ||
| Knowledge | 5 | 0 | ||
| General | 13 | 1 | ||
| All shared boards | 28 | 3 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index50
| Benchmark | DeepSeek V3.1 Terminus | o1 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 61.7% | Only o1 measured |
| AA Coding Index | 43.5 | 39.7 | DeepSeek V3.1 Terminus |
| LiveCodeBench (Vals AI) | Not measured | 50.3% | Only o1 measured |
| LiveCodeBench (Artificial Analysis run) | 79.8% | 67.9% | DeepSeek V3.1 Terminus |
| SciCode | 38.0% | Not measured | Only DeepSeek V3.1 Terminus measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.8% | 12.9% | DeepSeek V3.1 Terminus |
| WeirdML | Not measured | 46.1% | Only o1 measured |
| ALE-Bench | 745.2 | Not measured | Only DeepSeek V3.1 Terminus measured |
| LMArena Text: coding | 1425 | 1367 | DeepSeek V3.1 Terminus |
| LMArena Text: software and IT | 1426 | 1366 | DeepSeek V3.1 Terminus |
| 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 V3.1 Terminus | o1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 14.8 | 15.2 | o1 |
| Epoch Capabilities Index | Not measured | 141.7 | Only o1 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.1 Terminus | o1 | Leader |
|---|---|---|---|
| AA Agentic Index | 6.8 | Not measured | Only DeepSeek V3.1 Terminus measured |
| τ²-Bench Telecom | 37.1% | 62.6% | o1 |
| METR time horizons | Not measured | 51.1% | Only o1 measured |
| Neither model has a result on the other 36 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | DeepSeek V3.1 Terminus | 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) | 79.2% | 74.7% | DeepSeek V3.1 Terminus |
| Humanity's Last Exam (Artificial Analysis run) | 16.4% | 7.0% | DeepSeek V3.1 Terminus |
| 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 | 1426 | 1371 | DeepSeek V3.1 Terminus |
| LMArena Text: expert | Not measured | 1361 | Only o1 measured |
| LMArena Text: hard prompts in English | 1432 | 1372 | DeepSeek V3.1 Terminus |
| Neither model has a result on the other 9 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V3.1 Terminus | 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 |
| AA Math Index | 89.7 | Not measured | Only DeepSeek V3.1 Terminus measured |
| AIME 2025 (Artificial Analysis run) | 89.7% | Not measured | Only DeepSeek V3.1 Terminus 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 | 1402 | 1388 | DeepSeek V3.1 Terminus |
| LMArena Text: mathematical work | Not measured | 1390 | Only o1 measured |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | DeepSeek V3.1 Terminus | 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) | 85.1% | 84.1% | DeepSeek V3.1 Terminus |
| SimpleQA Verified | Not measured | 41.1% | Only o1 measured |
| GeoBench | Not measured | 80.0% | Only o1 measured |
| LMArena Text: science | 1451 | 1369 | DeepSeek V3.1 Terminus |
| LMArena Text: medicine and healthcare | 1468 | 1355 | DeepSeek V3.1 Terminus |
| LMArena Text: legal and government | 1457 | 1368 | DeepSeek V3.1 Terminus |
| LMArena Text: business and finance | 1414 | 1328 | DeepSeek V3.1 Terminus |
| Neither model has a result on the other 6 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | DeepSeek V3.1 Terminus | o1 | Leader |
|---|---|---|---|
| Design Arena: websites | 1187 | Not measured | Only DeepSeek V3.1 Terminus measured |
| Design Arena: UI components | 1182 | Not measured | Only DeepSeek V3.1 Terminus measured |
| Design Arena: game dev | 1131 | Not measured | Only DeepSeek V3.1 Terminus measured |
| Design Arena: data viz | 1167 | Not measured | Only DeepSeek V3.1 Terminus measured |
| Design Arena: 3D | 1141 | Not measured | Only DeepSeek V3.1 Terminus measured |
| Design Arena: all code categories | 1175 | Not measured | Only DeepSeek V3.1 Terminus measured |
| Design Arena: SVG | 1065 | Not measured | Only DeepSeek V3.1 Terminus measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index131
| Benchmark | DeepSeek V3.1 Terminus | o1 | Leader |
|---|---|---|---|
| IFBench | 57.0% | 70.3% | o1 |
| Long context reasoning (AA-LCR) | 69.3% | 65.0% | DeepSeek V3.1 Terminus |
| DTBench | 81.3% | 74.7% | DeepSeek V3.1 Terminus |
| LMCA | 28.6% | 22.3% | DeepSeek V3.1 Terminus |
| Fiction.LiveBench | Not measured | 83.3% | Only o1 measured |
| Creative writing (Lech Mazur) | Not measured | 70.2% | Only o1 measured |
| LMArena Text | 1419 | 1366 | DeepSeek V3.1 Terminus |
| LMArena Text: instruction following | 1404 | 1367 | DeepSeek V3.1 Terminus |
| LMArena Text: creative writing | 1403 | 1347 | DeepSeek V3.1 Terminus |
| LMArena Vision | Not measured | 1168 | Only o1 measured |
| LMArena Text: English | 1429 | 1372 | DeepSeek V3.1 Terminus |
| LMArena Text: non-English | 1408 | 1358 | DeepSeek V3.1 Terminus |
| 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 | 1435 | 1356 | DeepSeek V3.1 Terminus |
| LMArena Text: Polish | 1427 | Not measured | Only DeepSeek V3.1 Terminus measured |
| LMArena Text: multi-turn | 1411 | 1355 | DeepSeek V3.1 Terminus |
| LMArena Text: longer queries | 1422 | 1378 | DeepSeek V3.1 Terminus |
| LMArena Text: writing and language | 1409 | 1360 | DeepSeek V3.1 Terminus |
| LMArena Text: entertainment and media | 1391 | 1358 | DeepSeek V3.1 Terminus |
| 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 20 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.1 Terminus | 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 Terminus costs less
Repository review
50K input + 3K output tokens
DeepSeek V3.1 Terminus costs less
Agent loop
200K input + 10K output tokens
DeepSeek V3.1 Terminus 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 Terminus | OpenAI o1 |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | September 22, 2025 | December 17, 2024 |
| Context window | 164K | 200K |
| Input $/Mtok | $0.3063 | $15.00 |
| Output $/Mtok | $1.0142 | $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 Terminus better than o1 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. DeepSeek V3.1 Terminus has the edge where output tokens cost 59.2x less, and where what matters is AA Intelligence Index or τ²-Bench Telecom, where it leads.
Which is cheaper, DeepSeek V3.1 Terminus or o1?
DeepSeek V3.1 Terminus costs $0.3063 per million input tokens and $1.0142 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 Terminus or o1?
o1, with 200K tokens against 164K.
How many benchmarks is the DeepSeek V3.1 Terminus vs o1 comparison based on?
31 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.1 Terminus'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.