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DeepSeek V3.2 vs o1

DeepSeek V3.2 leads o1 on the Artificial Analysis Intelligence Index, 21.5 to 15.2, and DeepSeek V3.2 costs 68.2x less per output token ($0.88 vs $60.00 per million). Updated October 22, 2026; 49 benchmarks have a result for both.

Replace DeepSeek V3.2 Replace o1
DeepSeek V3.2DeepSeek
Coding
30.0
Overall
37.9
Output $/M
$0.88
43
o1OpenAI
Coding
No data
Overall
44.6
Output $/M
$60.00
6
vs boards won of 49 shared

DeepSeek V3.2 or o1: which to pick

Only DeepSeek V3.2 has a coding score, 30.0 on the Vibe Coding Index; o1 has none, so the two cannot be ranked on coding.

Pick o1 for a larger context window (200K against 164K tokens) and work measured by MGSM (Vals AI) or LegalBench (Vals AI), where it leads.

Model ADeepSeek V3.2DeepSeek30.0/1003 independent benchmarks$0.99 per solved taskModel Bo1OpenAINot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
49
DeepSeek V3.2 only
26
o1 only
20
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.

At a glance

Coding index
DeepSeek V3.2 30.0 from 3 benchmarks, o1 not benchmarked.
Widest category
Agentic work, 28.1 points apart on average over 1 shared benchmark.
Widest single gap
LiveCodeBench (Vals AI): DeepSeek V3.2 80.7% against o1 50.3%, 30.4 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 $0.88 against o1 $60.00 per million output tokens, 68.2x apart.
Context window
DeepSeek V3.2 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.

Boards won, where both have a result
CategoryDeepSeek V3.2Barso1Ties
Coding60
Intelligence20
Agentic work10
Reasoning70
Math31
Knowledge63
General182
All shared boards436
CodingLike-for-likeSix of these boards feed the Vibe Coding Index60
Coding benchmarks for DeepSeek V3.2 and o1
BenchmarkDeepSeek V3.2o1Leader
Aider polyglotNot measured61.7%Only o1 measured
SWE-bench Verified70.0%Not measuredOnly DeepSeek V3.2 measured
SWE-bench Multilingual59.0%Not measuredOnly DeepSeek V3.2 measured
LMArena WebDev1362Not measuredOnly DeepSeek V3.2 measured
AA Coding Index44.239.7DeepSeek V3.2
LiveCodeBench (Vals AI)80.7%50.3%DeepSeek V3.2
SWE-bench Verified (Vals AI)67.6%Not measuredOnly DeepSeek V3.2 measured
Vibe Code Bench (Vals AI)5.1%Not measuredOnly DeepSeek V3.2 measured
LiveCodeBench (Artificial Analysis run)86.2%67.9%DeepSeek V3.2
Terminal-Bench Hard (Artificial Analysis run)35.6%12.9%DeepSeek V3.2
Terminal-Bench39.6%Not measuredOnly DeepSeek V3.2 measured
WeirdMLNot measured46.1%Only o1 measured
LMArena Text: coding14531367DeepSeek V3.2
LMArena Text: software and IT14491366DeepSeek V3.2
LMArena WebDev: React1350Not measuredOnly DeepSeek V3.2 measured
LMArena WebDev: HTML1371Not measuredOnly DeepSeek V3.2 measured
SciCodeNot measured4.6%Only o1 measured
Neither model has a result on the other 22 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for DeepSeek V3.2 and o1
BenchmarkDeepSeek V3.2o1Leader
AA Intelligence Index21.515.2DeepSeek V3.2
Epoch Capabilities Index146.1141.7DeepSeek V3.2
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for DeepSeek V3.2 and o1
BenchmarkDeepSeek V3.2o1Leader
τ²-Bench Telecom90.6%62.6%DeepSeek V3.2
APEX-Agents21.3%Not measuredOnly DeepSeek V3.2 measured
METR time horizonsNot measured51.1%Only o1 measured
Vending-Bench 2$1,034Not measuredOnly DeepSeek V3.2 measured
Neither model has a result on the other 37 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index70
Reasoning benchmarks for DeepSeek V3.2 and o1
BenchmarkDeepSeek V3.2o1Leader
GPQA Diamond80.3%73.2%DeepSeek V3.2
MMMU Pro (Vals AI)Not measured77.4%Only o1 measured
GPQA Diamond (Artificial Analysis run)84.0%74.7%DeepSeek V3.2
Humanity's Last Exam (Artificial Analysis run)24.6%7.0%DeepSeek V3.2
GPQA Diamond (Epoch AI run)Not measured76.8%Only o1 measured
ARC-AGI57.0%30.7%DeepSeek V3.2
ARC-AGI-24.0%Not measuredOnly DeepSeek V3.2 measured
SimpleBenchNot measured40.1%Only o1 measured
Chess puzzlesNot measured10.0%Only o1 measured
VPCTNot measured37.0%Only o1 measured
LMArena Text: hard prompts14341371DeepSeek V3.2
LMArena Text: expert14361361DeepSeek V3.2
LMArena Text: hard prompts in English14461372DeepSeek V3.2
Neither model has a result on the other 9 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index31
Math benchmarks for DeepSeek V3.2 and o1
BenchmarkDeepSeek V3.2o1Leader
AIME84.6%71.5%DeepSeek V3.2
MATH 500 (Vals AI)Not measured90.4%Only o1 measured
MGSM (Vals AI)86.0%89.3%o1
AA Math Index92.0Not measuredOnly DeepSeek V3.2 measured
AIME 2025 (Artificial Analysis run)92.0%Not measuredOnly DeepSeek V3.2 measured
OTIS Mock AIMENot measured73.3%Only o1 measured
FrontierMath, tiers 1 to 3Not measured14.7%Only o1 measured
MATH level 5Not measured94.7%Only o1 measured
LMArena Text: math14351388DeepSeek V3.2
LMArena Text: mathematical work14401390DeepSeek V3.2
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index63
Knowledge benchmarks for DeepSeek V3.2 and o1
BenchmarkDeepSeek V3.2o1Leader
MMLU Pro84.9%83.5%DeepSeek V3.2
LegalBench (Vals AI)76.1%80.4%o1
CaseLaw (Vals AI)55.4%Not measuredOnly DeepSeek V3.2 measured
CorpFin (Vals AI)51.0%Not measuredOnly DeepSeek V3.2 measured
TaxEval (Vals AI)68.2%74.3%o1
MedQA (Vals AI)93.9%96.5%o1
MMLU Pro (Artificial Analysis run)86.2%84.1%DeepSeek V3.2
SimpleQA VerifiedNot measured41.1%Only o1 measured
GeoBenchNot measured80.0%Only o1 measured
LMArena Text: science14461369DeepSeek V3.2
LMArena Text: medicine and healthcare14401355DeepSeek V3.2
LMArena Text: legal and government14251368DeepSeek V3.2
LMArena Text: business and finance14101328DeepSeek V3.2
Neither model has a result on the other 5 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for DeepSeek V3.2 and o1
BenchmarkDeepSeek V3.2o1Leader
Design Arena: websites1175Not measuredOnly DeepSeek V3.2 measured
Design Arena: UI components1146Not measuredOnly DeepSeek V3.2 measured
Design Arena: game dev1131Not measuredOnly DeepSeek V3.2 measured
Design Arena: data viz1158Not measuredOnly DeepSeek V3.2 measured
Design Arena: 3D1132Not measuredOnly DeepSeek V3.2 measured
Design Arena: all code categories1163Not measuredOnly DeepSeek V3.2 measured
Design Arena: SVG1032Not measuredOnly DeepSeek V3.2 measured
Design Arena: ASCII art1083Not measuredOnly DeepSeek V3.2 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index182
General benchmarks for DeepSeek V3.2 and o1
BenchmarkDeepSeek V3.2o1Leader
IFBench60.7%70.3%o1
Long context reasoning (AA-LCR)73.3%65.0%DeepSeek V3.2
DTBench85.6%74.7%DeepSeek V3.2
LMCA28.8%22.3%DeepSeek V3.2
Fiction.LiveBenchNot measured83.3%Only o1 measured
Creative writing (Lech Mazur)Not measured70.2%Only o1 measured
CL-bench12.4%Not measuredOnly DeepSeek V3.2 measured
CL-bench Life7.4%Not measuredOnly DeepSeek V3.2 measured
LMArena Text14251366DeepSeek V3.2
LMArena Text: instruction following14131367DeepSeek V3.2
LMArena Text: creative writing14001347DeepSeek V3.2
LMArena VisionNot measured1168Only o1 measured
LMArena Text: English14391372DeepSeek V3.2
LMArena Text: non-English14091358DeepSeek V3.2
LMArena Text: Chinese14551394DeepSeek V3.2
LMArena Text: German14131337DeepSeek V3.2
LMArena Text: Japanese13741346DeepSeek V3.2
LMArena Text: Korean13701396o1
LMArena Text: French14291344DeepSeek V3.2
LMArena Text: Spanish14241345DeepSeek V3.2
LMArena Text: Russian14241356DeepSeek V3.2
LMArena Text: Polish1419Not measuredOnly DeepSeek V3.2 measured
LMArena Text: multi-turn14271355DeepSeek V3.2
LMArena Text: longer queries14281378DeepSeek V3.2
LMArena Text: writing and language14071360DeepSeek V3.2
LMArena Text: entertainment and media13951358DeepSeek V3.2
LMArena Vision: EnglishNot measured1162Only o1 measured
LMArena Vision: ChineseNot measured1235Only o1 measured
Neither model has a result on the other 18 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for DeepSeek V3.2 and o1
BenchmarkDeepSeek V3.2o1Leader
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 V3.2$0.0007Fits in one request
o1$0.045Fits in one request

DeepSeek V3.2 costs less

Repository review

50K input + 3K output tokens

DeepSeek V3.2$0.017Fits in one request
o1$0.930Fits in one request

DeepSeek V3.2 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.2$0.067Exceeds the context window
o1$3.60Exceeds the context window

DeepSeek V3.2 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.

Specifications of DeepSeek V3.2 and o1
SpecificationDeepSeek V3.2OpenAI o1
MakerDeepSeekOpenAI
WeightsOpen weightsNo public weights listed
ReleasedDecember 1, 2025December 17, 2024
Context window164K200K
Input $/Mtok$0.29$15.00
Output $/Mtok$0.88$60.00
Coding index30.0Not benchmarked
Independent measurements behind it30
Cost per solved task$0.99Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V3.2 better than o1 for coding?

Only DeepSeek V3.2 has a coding score, 30.0 on the Vibe Coding Index; o1 has none, so the two cannot be ranked on coding. Pick o1 for a larger context window (200K against 164K tokens) and work measured by MGSM (Vals AI) or LegalBench (Vals AI), where it leads.

Which is cheaper, DeepSeek V3.2 or o1?

DeepSeek V3.2 costs $0.29 per million input tokens and $0.88 per million output tokens; o1 costs $15.00 and $60.00. Cost per solved task is known for DeepSeek V3.2 only.

Which has the bigger context window, DeepSeek V3.2 or o1?

o1, with 200K tokens against 164K.

How many benchmarks is the DeepSeek V3.2 vs o1 comparison based on?

49 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.2's coding index rests on 3 independent benchmarks and o1's on 0. Where only one model has a result, the page says so instead of naming a winner.

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