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R1 0528 vs DeepSeek V4 Pro 0423

DeepSeek V4 Pro 0423 leads R1 0528 on the Artificial Analysis Intelligence Index, 30.4 to 13.1, and R1 0528 costs 1.4x less per output token ($2.18 vs $2.976 per million). Updated October 22, 2026; 40 benchmarks have a result for both.

Replace R1 0528 Replace DeepSeek V4 Pro 0423
R1 0528DeepSeek
Coding
No data
Overall
46.8
Output $/M
$2.18
0
DeepSeek V4 Pro 0423DeepSeek
Coding
38.7
Overall
50.0
Output $/M
$2.976
40
vs boards won of 40 shared

R1 0528 or DeepSeek V4 Pro 0423: which to pick

Only DeepSeek V4 Pro 0423 has a coding score, 38.7 on the Vibe Coding Index; R1 0528 has none, so the two cannot be ranked on coding.

Pick R1 0528 for output tokens 1.4x cheaper ($2.18 vs $2.976 per million).

Model AR1 0528DeepSeekNot benchmarkedNo coding benchmarkNo cost per solved taskModel BDeepSeek V4 Pro 0423DeepSeek38.7/1005 independent benchmarks$0.57 per solved task
Shared results
40
R1 0528 only
20
DeepSeek V4 Pro 0423 only
44
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
R1 0528 not benchmarked, DeepSeek V4 Pro 0423 38.7 from 5 benchmarks.
Widest category
Intelligence, 17.3 points apart on average over 1 shared benchmark.
Widest single gap
OTIS Mock AIME: R1 0528 66.4% against DeepSeek V4 Pro 0423 96.7%, 30.3 points apart on a 0 to 100 scale.
Output price
R1 0528 $2.18 against DeepSeek V4 Pro 0423 $2.976 per million output tokens, 1.4x apart.
Context window
R1 0528 takes 164K tokens, DeepSeek V4 Pro 0423 1.05M.

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 V4 Pro 0423Ties
Coding04
Intelligence01
Reasoning04
Math03
Knowledge04
Web design07
General017
All shared boards040
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
Coding benchmarks for R1 0528 and DeepSeek V4 Pro 0423
BenchmarkR1 0528DeepSeek V4 Pro 0423Leader
Aider polyglot71.4%Not measuredOnly R1 0528 measured
LMArena WebDevNot measured1447Only DeepSeek V4 Pro 0423 measured
AA Coding IndexNot measured59.4Only DeepSeek V4 Pro 0423 measured
LiveCodeBench (Vals AI)Not measured87.5%Only DeepSeek V4 Pro 0423 measured
SWE-bench Verified (Vals AI)Not measured77.4%Only DeepSeek V4 Pro 0423 measured
Terminal-Bench 4 (Vals AI)Not measured11.1%Only DeepSeek V4 Pro 0423 measured
Vibe Code Bench (Vals AI)Not measured49.9%Only DeepSeek V4 Pro 0423 measured
Code migration (Vals AI)Not measured26.2%Only DeepSeek V4 Pro 0423 measured
ProgramBench (Vals AI)Not measured0.0%Only DeepSeek V4 Pro 0423 measured
Terminal-Bench 2.1 (Vals AI)Not measured50.2%Only DeepSeek V4 Pro 0423 measured
SkillsBench (Vals AI)Not measured51.3%Only DeepSeek V4 Pro 0423 measured
LiveCodeBench (Artificial Analysis run)77.0%Not measuredOnly R1 0528 measured
Terminal-Bench Hard (Artificial Analysis run)15.9%Not measuredOnly R1 0528 measured
FrontierCodeNot measured17.6%Only DeepSeek V4 Pro 0423 measured
WeirdML41.6%48.9%DeepSeek V4 Pro 0423
ALE-Bench804.11006.1DeepSeek V4 Pro 0423
LMArena Text: coding14271470DeepSeek V4 Pro 0423
LMArena Text: software and IT14351469DeepSeek V4 Pro 0423
LMArena WebDev: ReactNot measured1441Only DeepSeek V4 Pro 0423 measured
LMArena WebDev: HTMLNot measured1435Only DeepSeek V4 Pro 0423 measured
Neither model has a result on the other 19 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for R1 0528 and DeepSeek V4 Pro 0423
BenchmarkR1 0528DeepSeek V4 Pro 0423Leader
AA Intelligence Index13.130.4DeepSeek V4 Pro 0423
Epoch Capabilities IndexNot measured149.1Only DeepSeek V4 Pro 0423 measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index13 boards, none shared
Agentic work benchmarks for R1 0528 and DeepSeek V4 Pro 0423
BenchmarkR1 0528DeepSeek V4 Pro 0423Leader
AA Agentic IndexNot measured26.3Only DeepSeek V4 Pro 0423 measured
Design Arena: agents building web appsNot measured1000Only DeepSeek V4 Pro 0423 measured
Design Arena: agents building full-stack appsNot measured948Only DeepSeek V4 Pro 0423 measured
Design Arena: agents building Godot gamesNot measured1059Only DeepSeek V4 Pro 0423 measured
Finance Agent (Vals AI)Not measured44.1%Only DeepSeek V4 Pro 0423 measured
Tax Agent Bench (Vals AI)Not measured25.2%Only DeepSeek V4 Pro 0423 measured
Legal Research Bench (Vals AI)Not measured23.1%Only DeepSeek V4 Pro 0423 measured
Excel Modeling Benchmark (Vals AI)Not measured51.6%Only DeepSeek V4 Pro 0423 measured
τ²-Bench Telecom36.5%Not measuredOnly R1 0528 measured
METR time horizons53.8%Not measuredOnly R1 0528 measured
DeepResearch Bench35.1%Not measuredOnly R1 0528 measured
Vending-Bench 2Not measured$3,285Only DeepSeek V4 Pro 0423 measured
BrowseComp-Plus16.4%Not measuredOnly R1 0528 measured
Neither model has a result on the other 28 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
Reasoning benchmarks for R1 0528 and DeepSeek V4 Pro 0423
BenchmarkR1 0528DeepSeek V4 Pro 0423Leader
GPQA DiamondNot measured89.4%Only DeepSeek V4 Pro 0423 measured
GPQA Diamond (Artificial Analysis run)81.3%Not measuredOnly R1 0528 measured
Humanity's Last Exam (Artificial Analysis run)15.8%Not measuredOnly R1 0528 measured
GPQA Diamond (Epoch AI run)76.3%90.9%DeepSeek V4 Pro 0423
ARC-AGI21.2%Not measuredOnly R1 0528 measured
ARC-AGI-21.1%Not measuredOnly R1 0528 measured
SimpleBench40.8%Not measuredOnly R1 0528 measured
Chess puzzlesNot measured19.0%Only DeepSeek V4 Pro 0423 measured
Mystery game puzzlesNot measured17.0%Only DeepSeek V4 Pro 0423 measured
LMArena Text: hard prompts14151461DeepSeek V4 Pro 0423
LMArena Text: expert13941463DeepSeek V4 Pro 0423
LMArena Text: hard prompts in English14221466DeepSeek V4 Pro 0423
Kagi LLM BenchmarkNot measured53.5%Only DeepSeek V4 Pro 0423 measured
Neither model has a result on the other 9 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index03
Math benchmarks for R1 0528 and DeepSeek V4 Pro 0423
BenchmarkR1 0528DeepSeek V4 Pro 0423Leader
AA Math Index76.0Not measuredOnly R1 0528 measured
AIME 2025 (Artificial Analysis run)76.0%Not measuredOnly R1 0528 measured
OTIS Mock AIME66.4%96.7%DeepSeek V4 Pro 0423
FrontierMath, tiers 1 to 3Not measured45.3%Only DeepSeek V4 Pro 0423 measured
FrontierMath, tier 4Not measured2.4%Only DeepSeek V4 Pro 0423 measured
ProofBenchNot measured16.0%Only DeepSeek V4 Pro 0423 measured
MATH level 596.6%Not measuredOnly R1 0528 measured
LMArena Text: math14001443DeepSeek V4 Pro 0423
LMArena Text: mathematical work14151455DeepSeek V4 Pro 0423
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for R1 0528 and DeepSeek V4 Pro 0423
BenchmarkR1 0528DeepSeek V4 Pro 0423Leader
MMLU ProNot measured87.3%Only DeepSeek V4 Pro 0423 measured
LegalBench (Vals AI)Not measured80.3%Only DeepSeek V4 Pro 0423 measured
CaseLaw (Vals AI)Not measured59.4%Only DeepSeek V4 Pro 0423 measured
CorpFin (Vals AI)Not measured61.4%Only DeepSeek V4 Pro 0423 measured
TaxEval (Vals AI)Not measured72.1%Only DeepSeek V4 Pro 0423 measured
MedCode (Vals AI)Not measured40.5%Only DeepSeek V4 Pro 0423 measured
MMLU Pro (Artificial Analysis run)84.9%Not measuredOnly R1 0528 measured
SimpleQA VerifiedNot measured47.0%Only DeepSeek V4 Pro 0423 measured
LMArena Text: science14301470DeepSeek V4 Pro 0423
LMArena Text: medicine and healthcare14451466DeepSeek V4 Pro 0423
LMArena Text: legal and government14191464DeepSeek V4 Pro 0423
LMArena Text: business and finance14021443DeepSeek V4 Pro 0423
Neither model has a result on the other 6 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index07
Web design benchmarks for R1 0528 and DeepSeek V4 Pro 0423
BenchmarkR1 0528DeepSeek V4 Pro 0423Leader
Design Arena: websites11491234DeepSeek V4 Pro 0423
Design Arena: UI components11001219DeepSeek V4 Pro 0423
Design Arena: game dev10971228DeepSeek V4 Pro 0423
Design Arena: data viz11821203DeepSeek V4 Pro 0423
Design Arena: 3D11101253DeepSeek V4 Pro 0423
Design Arena: all code categories11381240DeepSeek V4 Pro 0423
Design Arena: SVG10381139DeepSeek V4 Pro 0423
Design Arena: ASCII artNot measured1149Only DeepSeek V4 Pro 0423 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index017
General benchmarks for R1 0528 and DeepSeek V4 Pro 0423
BenchmarkR1 0528DeepSeek V4 Pro 0423Leader
Public Benefits Bench (Vals AI)Not measured62.9%Only DeepSeek V4 Pro 0423 measured
MedScribe (Vals AI)Not measured75.1%Only DeepSeek V4 Pro 0423 measured
IFBench39.6%Not measuredOnly R1 0528 measured
Long context reasoning (AA-LCR)55.7%Not measuredOnly R1 0528 measured
DTBenchNot measured90.7%Only DeepSeek V4 Pro 0423 measured
LMCANot measured41.2%Only DeepSeek V4 Pro 0423 measured
Fiction.LiveBench75.0%Not measuredOnly R1 0528 measured
Creative writing (Lech Mazur)81.9%Not measuredOnly R1 0528 measured
Surface Evolver BenchNot measured40.0%Only DeepSeek V4 Pro 0423 measured
CL-bench LifeNot measured13.5%Only DeepSeek V4 Pro 0423 measured
LMArena Text14281451DeepSeek V4 Pro 0423
LMArena Text: instruction following13821445DeepSeek V4 Pro 0423
LMArena Text: creative writing14051446DeepSeek V4 Pro 0423
LMArena Text: English14371459DeepSeek V4 Pro 0423
LMArena Text: non-English14121439DeepSeek V4 Pro 0423
LMArena Text: Chinese14421481DeepSeek V4 Pro 0423
LMArena Text: German14041458DeepSeek V4 Pro 0423
LMArena Text: Japanese13911446DeepSeek V4 Pro 0423
LMArena Text: Korean13601409DeepSeek V4 Pro 0423
LMArena Text: French14171473DeepSeek V4 Pro 0423
LMArena Text: Spanish14111457DeepSeek V4 Pro 0423
LMArena Text: Russian14231453DeepSeek V4 Pro 0423
LMArena Text: Polish14281461DeepSeek V4 Pro 0423
LMArena Text: multi-turn14051468DeepSeek V4 Pro 0423
LMArena Text: longer queries13911458DeepSeek V4 Pro 0423
LMArena Text: writing and language14021447DeepSeek V4 Pro 0423
LMArena Text: entertainment and media14001431DeepSeek V4 Pro 0423
Neither model has a result on the other 19 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 V4 Pro 0423
BenchmarkR1 0528DeepSeek V4 Pro 0423Leader
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 V4 Pro 0423$0.0028Fits in one request

R1 0528 costs less

Repository review

50K input + 3K output tokens

R1 0528$0.032Fits in one request
DeepSeek V4 Pro 0423$0.077Fits in one request

R1 0528 costs less

Agent loop

200K input + 10K output tokens

R1 0528$0.122Exceeds the context window
DeepSeek V4 Pro 0423$0.301Fits in one request

R1 0528 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 V4 Pro 0423
SpecificationDeepSeek R1 0528DeepSeek V4 Pro 0423
MakerDeepSeekDeepSeek
WeightsOpen weightsOpen weights
ReleasedMay 28, 2025April 24, 2026
Context window164K1.05M
Input $/Mtok$0.50$1.358
Output $/Mtok$2.18$2.976
Coding indexNot benchmarked38.7
Independent measurements behind it05
Cost per solved taskNot computable$0.57
SourceOpenRouter listingOpenRouter listing

Questions

Is R1 0528 better than DeepSeek V4 Pro 0423 for coding?

Only DeepSeek V4 Pro 0423 has a coding score, 38.7 on the Vibe Coding Index; R1 0528 has none, so the two cannot be ranked on coding. Pick R1 0528 for output tokens 1.4x cheaper ($2.18 vs $2.976 per million).

Which is cheaper, R1 0528 or DeepSeek V4 Pro 0423?

R1 0528 costs $0.50 per million input tokens and $2.18 per million output tokens; DeepSeek V4 Pro 0423 costs $1.358 and $2.976. Cost per solved task is known for DeepSeek V4 Pro 0423 only.

Which has the bigger context window, R1 0528 or DeepSeek V4 Pro 0423?

DeepSeek V4 Pro 0423, with 1.05M tokens against 164K.

How many benchmarks is the R1 0528 vs DeepSeek V4 Pro 0423 comparison based on?

40 benchmarks have a result for both models as of October 22, 2026. R1 0528's coding index rests on 0 independent benchmarks and DeepSeek V4 Pro 0423's on 5. Where only one model has a result, the page says so instead of naming a winner.

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