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R1 0528 vs DeepSeek V3.2

DeepSeek V3.2 leads R1 0528 on the Artificial Analysis Intelligence Index, 21.5 to 13.1, and DeepSeek V3.2 costs 2.5x less per output token ($0.88 vs $2.18 per million). Updated October 22, 2026; 48 benchmarks have a result for both.

Replace R1 0528 Replace DeepSeek V3.2
R1 0528DeepSeek
Coding
No data
Overall
46.8
Output $/M
$2.18
9
DeepSeek V3.2DeepSeek
Coding
30.0
Overall
37.9
Output $/M
$0.88
39
vs boards won of 48 shared

R1 0528 or DeepSeek V3.2: which to pick

Only DeepSeek V3.2 has a coding score, 30.0 on the Vibe Coding Index; R1 0528 has none, so the two cannot be ranked on coding.

Pick R1 0528 for work measured by LMArena Text: medicine and healthcare or Design Arena: data viz, where it leads.

Model AR1 0528DeepSeekNot benchmarkedNo coding benchmarkNo cost per solved taskModel BDeepSeek V3.2DeepSeek30.0/1003 independent benchmarks$0.99 per solved task
Shared results
48
R1 0528 only
12
DeepSeek V3.2 only
27
Like-for-like categories
8 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
R1 0528 not benchmarked, DeepSeek V3.2 30.0 from 3 benchmarks.
Widest category
Agentic work, 54.1 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: R1 0528 36.5% against DeepSeek V3.2 90.6%, 54.1 points apart on a 0 to 100 scale.
Output price
R1 0528 $2.18 against DeepSeek V3.2 $0.88 per million output tokens, 2.5x apart.
Context window
Both take 164K tokens.

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
CategoryR1 0528BarsDeepSeek V3.2Ties
Coding04
Intelligence01
Agentic work01
Reasoning07
Math04
Knowledge14
Web design25
General613
All shared boards939
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
Coding benchmarks for R1 0528 and DeepSeek V3.2
BenchmarkR1 0528DeepSeek V3.2Leader
Aider polyglot71.4%Not measuredOnly R1 0528 measured
SWE-bench VerifiedNot measured70.0%Only DeepSeek V3.2 measured
SWE-bench MultilingualNot measured59.0%Only DeepSeek V3.2 measured
LMArena WebDevNot measured1362Only DeepSeek V3.2 measured
AA Coding IndexNot measured44.2Only DeepSeek V3.2 measured
LiveCodeBench (Vals AI)Not measured80.7%Only DeepSeek V3.2 measured
SWE-bench Verified (Vals AI)Not measured67.6%Only DeepSeek V3.2 measured
Vibe Code Bench (Vals AI)Not measured5.1%Only DeepSeek V3.2 measured
LiveCodeBench (Artificial Analysis run)77.0%86.2%DeepSeek V3.2
Terminal-Bench Hard (Artificial Analysis run)15.9%35.6%DeepSeek V3.2
Terminal-BenchNot measured39.6%Only DeepSeek V3.2 measured
WeirdML41.6%Not measuredOnly R1 0528 measured
ALE-Bench804.1Not measuredOnly R1 0528 measured
LMArena Text: coding14271453DeepSeek V3.2
LMArena Text: software and IT14351449DeepSeek V3.2
LMArena WebDev: ReactNot measured1350Only DeepSeek V3.2 measured
LMArena WebDev: HTMLNot measured1371Only DeepSeek V3.2 measured
Neither model has a result on the other 22 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for R1 0528 and DeepSeek V3.2
BenchmarkR1 0528DeepSeek V3.2Leader
AA Intelligence Index13.121.5DeepSeek V3.2
Epoch Capabilities IndexNot measured146.1Only DeepSeek V3.2 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for R1 0528 and DeepSeek V3.2
BenchmarkR1 0528DeepSeek V3.2Leader
τ²-Bench Telecom36.5%90.6%DeepSeek V3.2
APEX-AgentsNot measured21.3%Only DeepSeek V3.2 measured
METR time horizons53.8%Not measuredOnly R1 0528 measured
DeepResearch Bench35.1%Not measuredOnly R1 0528 measured
Vending-Bench 2Not measured$1,034Only DeepSeek V3.2 measured
BrowseComp-Plus16.4%Not measuredOnly R1 0528 measured
Neither model has a result on the other 35 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index07
Reasoning benchmarks for R1 0528 and DeepSeek V3.2
BenchmarkR1 0528DeepSeek V3.2Leader
GPQA DiamondNot measured80.3%Only DeepSeek V3.2 measured
GPQA Diamond (Artificial Analysis run)81.3%84.0%DeepSeek V3.2
Humanity's Last Exam (Artificial Analysis run)15.8%24.6%DeepSeek V3.2
GPQA Diamond (Epoch AI run)76.3%Not measuredOnly R1 0528 measured
ARC-AGI21.2%57.0%DeepSeek V3.2
ARC-AGI-21.1%4.0%DeepSeek V3.2
SimpleBench40.8%Not measuredOnly R1 0528 measured
LMArena Text: hard prompts14151434DeepSeek V3.2
LMArena Text: expert13941436DeepSeek V3.2
LMArena Text: hard prompts in English14221446DeepSeek V3.2
Neither model has a result on the other 12 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index04
Math benchmarks for R1 0528 and DeepSeek V3.2
BenchmarkR1 0528DeepSeek V3.2Leader
AIMENot measured84.6%Only DeepSeek V3.2 measured
MGSM (Vals AI)Not measured86.0%Only DeepSeek V3.2 measured
AA Math Index76.092.0DeepSeek V3.2
AIME 2025 (Artificial Analysis run)76.0%92.0%DeepSeek V3.2
OTIS Mock AIME66.4%Not measuredOnly R1 0528 measured
MATH level 596.6%Not measuredOnly R1 0528 measured
LMArena Text: math14001435DeepSeek V3.2
LMArena Text: mathematical work14151440DeepSeek V3.2
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index14
Knowledge benchmarks for R1 0528 and DeepSeek V3.2
BenchmarkR1 0528DeepSeek V3.2Leader
MMLU ProNot measured84.9%Only DeepSeek V3.2 measured
LegalBench (Vals AI)Not measured76.1%Only DeepSeek V3.2 measured
CaseLaw (Vals AI)Not measured55.4%Only DeepSeek V3.2 measured
CorpFin (Vals AI)Not measured51.0%Only DeepSeek V3.2 measured
TaxEval (Vals AI)Not measured68.2%Only DeepSeek V3.2 measured
MedQA (Vals AI)Not measured93.9%Only DeepSeek V3.2 measured
MMLU Pro (Artificial Analysis run)84.9%86.2%DeepSeek V3.2
LMArena Text: science14301446DeepSeek V3.2
LMArena Text: medicine and healthcare14451440R1 0528
LMArena Text: legal and government14191425DeepSeek V3.2
LMArena Text: business and finance14021410DeepSeek V3.2
Neither model has a result on the other 7 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index25
Web design benchmarks for R1 0528 and DeepSeek V3.2
BenchmarkR1 0528DeepSeek V3.2Leader
Design Arena: websites11491175DeepSeek V3.2
Design Arena: UI components11001146DeepSeek V3.2
Design Arena: game dev10971131DeepSeek V3.2
Design Arena: data viz11821158R1 0528
Design Arena: 3D11101132DeepSeek V3.2
Design Arena: all code categories11381163DeepSeek V3.2
Design Arena: SVG10381032R1 0528
Design Arena: ASCII artNot measured1083Only 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 index613
General benchmarks for R1 0528 and DeepSeek V3.2
BenchmarkR1 0528DeepSeek V3.2Leader
IFBench39.6%60.7%DeepSeek V3.2
Long context reasoning (AA-LCR)55.7%73.3%DeepSeek V3.2
DTBenchNot measured85.6%Only DeepSeek V3.2 measured
LMCANot measured28.8%Only DeepSeek V3.2 measured
Fiction.LiveBench75.0%Not measuredOnly R1 0528 measured
Creative writing (Lech Mazur)81.9%Not measuredOnly R1 0528 measured
CL-benchNot measured12.4%Only DeepSeek V3.2 measured
CL-bench LifeNot measured7.4%Only DeepSeek V3.2 measured
LMArena Text14281425R1 0528
LMArena Text: instruction following13821413DeepSeek V3.2
LMArena Text: creative writing14051400R1 0528
LMArena Text: English14371439DeepSeek V3.2
LMArena Text: non-English14121409R1 0528
LMArena Text: Chinese14421455DeepSeek V3.2
LMArena Text: German14041413DeepSeek V3.2
LMArena Text: Japanese13911374R1 0528
LMArena Text: Korean13601370DeepSeek V3.2
LMArena Text: French14171429DeepSeek V3.2
LMArena Text: Spanish14111424DeepSeek V3.2
LMArena Text: Russian14231424DeepSeek V3.2
LMArena Text: Polish14281419R1 0528
LMArena Text: multi-turn14051427DeepSeek V3.2
LMArena Text: longer queries13911428DeepSeek V3.2
LMArena Text: writing and language14021407DeepSeek V3.2
LMArena Text: entertainment and media14001395R1 0528
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
Image generation benchmarks for R1 0528 and DeepSeek V3.2
BenchmarkR1 0528DeepSeek V3.2Leader
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

R1 0528$0.0016Fits in one request
DeepSeek V3.2$0.0007Fits in one request

DeepSeek V3.2 costs less

Repository review

50K input + 3K output tokens

R1 0528$0.032Fits in one request
DeepSeek V3.2$0.017Fits in one request

DeepSeek V3.2 costs less

Agent loop

200K input + 10K output tokens

R1 0528$0.122Exceeds the context window
DeepSeek V3.2$0.067Exceeds 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 R1 0528 and DeepSeek V3.2
SpecificationDeepSeek R1 0528DeepSeek V3.2
MakerDeepSeekDeepSeek
WeightsOpen weightsOpen weights
ReleasedMay 28, 2025December 1, 2025
Context window164K164K
Input $/Mtok$0.50$0.29
Output $/Mtok$2.18$0.88
Coding indexNot benchmarked30.0
Independent measurements behind it03
Cost per solved taskNot computable$0.99
SourceOpenRouter listingOpenRouter listing

Questions

Is R1 0528 better than DeepSeek V3.2 for coding?

Only DeepSeek V3.2 has a coding score, 30.0 on the Vibe Coding Index; R1 0528 has none, so the two cannot be ranked on coding. Pick R1 0528 for work measured by LMArena Text: medicine and healthcare or Design Arena: data viz, where it leads.

Which is cheaper, R1 0528 or DeepSeek V3.2?

R1 0528 costs $0.50 per million input tokens and $2.18 per million output tokens; DeepSeek V3.2 costs $0.29 and $0.88. Cost per solved task is known for DeepSeek V3.2 only.

Which has the bigger context window, R1 0528 or DeepSeek V3.2?

Neither: both take 164K tokens.

How many benchmarks is the R1 0528 vs DeepSeek V3.2 comparison based on?

48 benchmarks have a result for both models as of October 22, 2026. R1 0528's coding index rests on 0 independent benchmarks and DeepSeek V3.2's on 3. Where only one model has a result, the page says so instead of naming a winner.

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