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R1 0528 vs o1-pro

R1 0528 leads o1-pro on the Artificial Analysis Intelligence Index, 13.1 to 12.4, and R1 0528 costs 275.2x less per output token ($2.18 vs $600.00 per million). Updated October 21, 2026; 2 benchmarks have a result for both.

R1 0528DeepSeek
Coding
No data
Overall
13.1
Output $/M
$2.18
1
o1-proOpenAI
Coding
No data
Overall
12.4
Output $/M
$600.00
1
vs boards won of 2 shared

R1 0528 or o1-pro: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick R1 0528 for output tokens 275.2x cheaper ($2.18 vs $600.00 per million) and work measured by ARC-AGI, where it leads.

Shared results
2
R1 0528 only
57
o1-pro only
0
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.

At a glance

Coding index
R1 0528 not benchmarked, o1-pro not benchmarked.
Widest category
Reasoning, 2.1 points apart on average over 1 shared benchmark.
Widest single gap
ARC-AGI: R1 0528 21.2% against o1-pro 23.3%, 2.1 points apart on a 0 to 100 scale.
Output price
R1 0528 $2.18 against o1-pro $600.00 per million output tokens, 275.2x apart.
Context window
R1 0528 takes 164K tokens, o1-pro 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
CategoryR1 0528Barso1-proTies
Intelligence10
Reasoning01
All shared boards11
CodingDirectional onlySix of these boards feed the Vibe Coding Index7 boards, none shared
Coding benchmarks for R1 0528 and o1-pro
BenchmarkR1 0528o1-proLeader
Aider polyglot71.4%Not measuredOnly R1 0528 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
WeirdML41.6%Not measuredOnly R1 0528 measured
ALE-Bench804.1Not measuredOnly R1 0528 measured
LMArena Text: coding1427Not measuredOnly R1 0528 measured
LMArena Text: software and IT1435Not measuredOnly R1 0528 measured
Neither model has a result on the other 31 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for R1 0528 and o1-pro
BenchmarkR1 0528o1-proLeader
AA Intelligence Index13.112.4R1 0528
Neither model has a result on the other 1 board here
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
Agentic work benchmarks for R1 0528 and o1-pro
BenchmarkR1 0528o1-proLeader
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
Neither model has a result on the other 36 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
Reasoning benchmarks for R1 0528 and o1-pro
BenchmarkR1 0528o1-proLeader
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%Not measuredOnly R1 0528 measured
ARC-AGI21.2%23.3%o1-pro
ARC-AGI-21.1%Not measuredOnly R1 0528 measured
SimpleBench40.8%Not measuredOnly R1 0528 measured
LMArena Text: hard prompts1415Not measuredOnly R1 0528 measured
LMArena Text: expert1394Not measuredOnly R1 0528 measured
LMArena Text: hard prompts in English1422Not measuredOnly R1 0528 measured
Neither model has a result on the other 12 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Math benchmarks for R1 0528 and o1-pro
BenchmarkR1 0528o1-proLeader
AA Math Index76.0Not measuredOnly R1 0528 measured
AIME 2025 (Artificial Analysis run)76.0%Not measuredOnly R1 0528 measured
OTIS Mock AIME66.4%Not measuredOnly R1 0528 measured
MATH level 596.6%Not measuredOnly R1 0528 measured
LMArena Text: math1400Not measuredOnly R1 0528 measured
LMArena Text: mathematical work1415Not measuredOnly R1 0528 measured
Neither model has a result on the other 7 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
Knowledge benchmarks for R1 0528 and o1-pro
BenchmarkR1 0528o1-proLeader
MMLU Pro (Artificial Analysis run)84.9%Not measuredOnly R1 0528 measured
LMArena Text: science1430Not measuredOnly R1 0528 measured
LMArena Text: medicine and healthcare1445Not measuredOnly R1 0528 measured
LMArena Text: legal and government1419Not measuredOnly R1 0528 measured
LMArena Text: business and finance1402Not measuredOnly R1 0528 measured
Neither model has a result on the other 12 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for R1 0528 and o1-pro
BenchmarkR1 0528o1-proLeader
Design Arena: websites1149Not measuredOnly R1 0528 measured
Design Arena: UI components1103Not measuredOnly R1 0528 measured
Design Arena: game dev1097Not measuredOnly R1 0528 measured
Design Arena: data viz1182Not measuredOnly R1 0528 measured
Design Arena: 3D1111Not measuredOnly R1 0528 measured
Design Arena: all code categories1139Not measuredOnly R1 0528 measured
Design Arena: SVG1039Not measuredOnly R1 0528 measured
Neither model has a result on the other 2 boards here
GeneralDirectional onlyPublic boards, shown as published, kept out of the index21 boards, none shared
General benchmarks for R1 0528 and o1-pro
BenchmarkR1 0528o1-proLeader
IFBench39.6%Not measuredOnly R1 0528 measured
Long context reasoning (AA-LCR)55.7%Not measuredOnly R1 0528 measured
Fiction.LiveBench75.0%Not measuredOnly R1 0528 measured
Creative writing (Lech Mazur)81.9%Not measuredOnly R1 0528 measured
LMArena Text1428Not measuredOnly R1 0528 measured
LMArena Text: instruction following1382Not measuredOnly R1 0528 measured
LMArena Text: creative writing1405Not measuredOnly R1 0528 measured
LMArena Text: English1437Not measuredOnly R1 0528 measured
LMArena Text: non-English1412Not measuredOnly R1 0528 measured
LMArena Text: Chinese1442Not measuredOnly R1 0528 measured
LMArena Text: German1404Not measuredOnly R1 0528 measured
LMArena Text: Japanese1391Not measuredOnly R1 0528 measured
LMArena Text: Korean1360Not measuredOnly R1 0528 measured
LMArena Text: French1417Not measuredOnly R1 0528 measured
LMArena Text: Spanish1411Not measuredOnly R1 0528 measured
LMArena Text: Russian1423Not measuredOnly R1 0528 measured
LMArena Text: Polish1428Not measuredOnly R1 0528 measured
LMArena Text: multi-turn1405Not measuredOnly R1 0528 measured
LMArena Text: longer queries1391Not measuredOnly R1 0528 measured
LMArena Text: writing and language1402Not measuredOnly R1 0528 measured
LMArena Text: entertainment and media1400Not measuredOnly R1 0528 measured
Neither model has a result on the other 25 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for R1 0528 and o1-pro
BenchmarkR1 0528o1-proLeader
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
o1-pro$0.450Fits in one request

R1 0528 costs less

Repository review

50K input + 3K output tokens

R1 0528$0.032Fits in one request
o1-pro$9.30Fits in one request

R1 0528 costs less

Agent loop

200K input + 10K output tokens

R1 0528$0.122Exceeds the context window
o1-pro$36.00Exceeds the context window

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 o1-pro
SpecificationDeepSeek R1 0528OpenAI o1-pro
MakerDeepSeekOpenAI
WeightsOpen weightsNo public weights listed
ReleasedMay 28, 2025March 19, 2025
Context window164K200K
Input $/Mtok$0.50$150.00
Output $/Mtok$2.18$600.00
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is R1 0528 better than o1-pro for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick R1 0528 for output tokens 275.2x cheaper ($2.18 vs $600.00 per million) and work measured by ARC-AGI, where it leads.

Which is cheaper, R1 0528 or o1-pro?

R1 0528 costs $0.50 per million input tokens and $2.18 per million output tokens; o1-pro costs $150.00 and $600.00. Cost per solved task is known for neither of them.

Which has the bigger context window, R1 0528 or o1-pro?

o1-pro, with 200K tokens against 164K.

How many benchmarks is the R1 0528 vs o1-pro comparison based on?

2 benchmarks have a result for both models as of October 21, 2026. R1 0528's coding index rests on 0 independent benchmarks and o1-pro's on 0. Where only one model has a result, the page says so instead of naming a winner.

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