Enter opens the highlighted result. Esc closes.

R1 0528 vs Gemini 2.5 Pro

Gemini 2.5 Pro leads R1 0528 on the Artificial Analysis Intelligence Index, 16.1 to 13.1, and R1 0528 costs 4.6x less per output token ($2.18 vs $10.00 per million). Updated October 22, 2026; 53 benchmarks have a result for both.

Replace R1 0528 Replace Gemini 2.5 Pro
R1 0528DeepSeek
Coding
No data
Overall
46.8
Output $/M
$2.18
1
Gemini 2.5 ProGoogle
Coding
19.1
Overall
41.8
Output $/M
$10.00
52
vs boards won of 53 shared

R1 0528 or Gemini 2.5 Pro: which to pick

Only Gemini 2.5 Pro has a coding score, 19.1 on the Vibe Coding Index; R1 0528 has none, so the two cannot be ranked on coding.

Pick R1 0528 for output tokens 4.6x cheaper ($2.18 vs $10.00 per million) and work measured by Design Arena: 3D, where it leads.

Model AR1 0528DeepSeekNot benchmarkedNo coding benchmarkNo cost per solved taskModel BGemini 2.5 ProGoogle19.1/1003 independent benchmarks$0.64 per solved task
Shared results
53
R1 0528 only
7
Gemini 2.5 Pro only
37
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, Gemini 2.5 Pro 19.1 from 3 benchmarks.
Widest category
Agentic work, 12.4 points apart on average over 3 shared benchmarks.
Widest single gap
ARC-AGI: R1 0528 21.2% against Gemini 2.5 Pro 41.0%, 19.8 points apart on a 0 to 100 scale.
Output price
R1 0528 $2.18 against Gemini 2.5 Pro $10.00 per million output tokens, 4.6x apart.
Context window
R1 0528 takes 164K tokens, Gemini 2.5 Pro 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 0528BarsGemini 2.5 ProTies
Coding05
Intelligence01
Agentic work03
Reasoning08
Math05
Knowledge05
Web design15
General020
All shared boards152
CodingLike-for-likeSix of these boards feed the Vibe Coding Index05
Coding benchmarks for R1 0528 and Gemini 2.5 Pro
BenchmarkR1 0528Gemini 2.5 ProLeader
Aider polyglot71.4%Not measuredOnly R1 0528 measured
SWE-bench VerifiedNot measured53.6%Only Gemini 2.5 Pro measured
LMArena WebDevNot measured1226Only Gemini 2.5 Pro measured
AA Coding IndexNot measured33.3Only Gemini 2.5 Pro measured
SWE-bench Verified (Vals AI)Not measured54.4%Only Gemini 2.5 Pro measured
Vibe Code Bench (Vals AI)Not measured0.4%Only Gemini 2.5 Pro measured
LiveCodeBench (Artificial Analysis run)77.0%80.1%Gemini 2.5 Pro
SciCodeNot measured46.3%Only Gemini 2.5 Pro measured
Terminal-Bench Hard (Artificial Analysis run)15.9%26.5%Gemini 2.5 Pro
Terminal-BenchNot measured32.6%Only Gemini 2.5 Pro measured
GSO-BenchNot measured3.9%Only Gemini 2.5 Pro measured
WeirdML41.6%54.0%Gemini 2.5 Pro
ALE-Bench804.1Not measuredOnly R1 0528 measured
LMArena Text: coding14271452Gemini 2.5 Pro
LMArena Text: software and IT14351459Gemini 2.5 Pro
LMArena WebDev: HTMLNot measured1235Only Gemini 2.5 Pro measured
LMArena WebDev: from an imageNot measured1285Only Gemini 2.5 Pro 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 Gemini 2.5 Pro
BenchmarkR1 0528Gemini 2.5 ProLeader
AA Intelligence Index13.116.1Gemini 2.5 Pro
Epoch Capabilities IndexNot measured145.1Only Gemini 2.5 Pro measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index03
Agentic work benchmarks for R1 0528 and Gemini 2.5 Pro
BenchmarkR1 0528Gemini 2.5 ProLeader
AA Agentic IndexNot measured1.6Only Gemini 2.5 Pro measured
τ²-Bench Telecom36.5%54.1%Gemini 2.5 Pro
METR time horizons53.8%Not measuredOnly R1 0528 measured
GDPvalNot measured23.3%Only Gemini 2.5 Pro measured
DeepResearch Bench35.1%41.5%Gemini 2.5 Pro
Vending-Bench 2Not measured$574Only Gemini 2.5 Pro measured
BrowseComp-Plus16.4%29.5%Gemini 2.5 Pro
Neither model has a result on the other 34 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index08
Reasoning benchmarks for R1 0528 and Gemini 2.5 Pro
BenchmarkR1 0528Gemini 2.5 ProLeader
GPQA Diamond (Artificial Analysis run)81.3%84.4%Gemini 2.5 Pro
Humanity's Last Exam (Artificial Analysis run)15.8%22.5%Gemini 2.5 Pro
GPQA Diamond (Epoch AI run)76.3%85.3%Gemini 2.5 Pro
ARC-AGI21.2%41.0%Gemini 2.5 Pro
ARC-AGI-21.1%4.9%Gemini 2.5 Pro
SimpleBench40.8%Not measuredOnly R1 0528 measured
LMArena Text: hard prompts14151455Gemini 2.5 Pro
LMArena Text: expert13941452Gemini 2.5 Pro
LMArena Text: hard prompts in English14221453Gemini 2.5 Pro
LMArena Vision: diagramsNot measured1274Only Gemini 2.5 Pro measured
LMArena Vision: homeworkNot measured1281Only Gemini 2.5 Pro measured
Kagi LLM BenchmarkNot measured70.3%Only Gemini 2.5 Pro measured
Neither model has a result on the other 10 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index05
Math benchmarks for R1 0528 and Gemini 2.5 Pro
BenchmarkR1 0528Gemini 2.5 ProLeader
AA Math Index76.087.7Gemini 2.5 Pro
AIME 2025 (Artificial Analysis run)76.0%87.7%Gemini 2.5 Pro
OTIS Mock AIME66.4%84.7%Gemini 2.5 Pro
FrontierMath, tiers 1 to 3Not measured24.6%Only Gemini 2.5 Pro measured
FrontierMath, tier 4Not measured0.0%Only Gemini 2.5 Pro measured
MATH level 596.6%Not measuredOnly R1 0528 measured
LMArena Text: math14001450Gemini 2.5 Pro
LMArena Text: mathematical work14151454Gemini 2.5 Pro
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
Knowledge benchmarks for R1 0528 and Gemini 2.5 Pro
BenchmarkR1 0528Gemini 2.5 ProLeader
CaseLaw (Vals AI)Not measured63.9%Only Gemini 2.5 Pro measured
CorpFin (Vals AI)Not measured60.8%Only Gemini 2.5 Pro measured
MortgageTax (Vals AI)Not measured68.9%Only Gemini 2.5 Pro measured
MedCode (Vals AI)Not measured50.6%Only Gemini 2.5 Pro measured
MMLU Pro (Artificial Analysis run)84.9%86.2%Gemini 2.5 Pro
LMArena Text: science14301480Gemini 2.5 Pro
LMArena Text: medicine and healthcare14451476Gemini 2.5 Pro
LMArena Text: legal and government14191480Gemini 2.5 Pro
LMArena Text: business and finance14021439Gemini 2.5 Pro
FACTS Benchmark SuiteNot measured61.5%Only Gemini 2.5 Pro measured
Neither model has a result on the other 8 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index15
Web design benchmarks for R1 0528 and Gemini 2.5 Pro
BenchmarkR1 0528Gemini 2.5 ProLeader
Design Arena: websites11491167Gemini 2.5 Pro
Design Arena: UI components11001137Gemini 2.5 Pro
Design Arena: game dev10971111Gemini 2.5 Pro
Design Arena: data viz11821222Gemini 2.5 Pro
Design Arena: 3D11101080R1 0528
Design Arena: all code categories11381152Gemini 2.5 Pro
Design Arena: SVG1038Not measuredOnly R1 0528 measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index020
General benchmarks for R1 0528 and Gemini 2.5 Pro
BenchmarkR1 0528Gemini 2.5 ProLeader
SAGE (Vals AI)Not measured41.9%Only Gemini 2.5 Pro measured
MedScribe (Vals AI)Not measured73.5%Only Gemini 2.5 Pro measured
IFBench39.6%48.7%Gemini 2.5 Pro
Long context reasoning (AA-LCR)55.7%69.0%Gemini 2.5 Pro
DTBenchNot measured82.4%Only Gemini 2.5 Pro measured
LMCANot measured34.8%Only Gemini 2.5 Pro measured
Fiction.LiveBench75.0%Not measuredOnly R1 0528 measured
Creative writing (Lech Mazur)81.9%83.8%Gemini 2.5 Pro
LMArena Text14281458Gemini 2.5 Pro
LMArena Text: instruction following13821437Gemini 2.5 Pro
LMArena Text: creative writing14051454Gemini 2.5 Pro
LMArena VisionNot measured1263Only Gemini 2.5 Pro measured
LMArena Text: English14371458Gemini 2.5 Pro
LMArena Text: non-English14121452Gemini 2.5 Pro
LMArena Text: Chinese14421507Gemini 2.5 Pro
LMArena Text: German14041487Gemini 2.5 Pro
LMArena Text: Japanese13911461Gemini 2.5 Pro
LMArena Text: Korean13601434Gemini 2.5 Pro
LMArena Text: French14171473Gemini 2.5 Pro
LMArena Text: Spanish14111474Gemini 2.5 Pro
LMArena Text: Russian14231461Gemini 2.5 Pro
LMArena Text: Polish14281479Gemini 2.5 Pro
LMArena Text: multi-turn14051453Gemini 2.5 Pro
LMArena Text: longer queries13911449Gemini 2.5 Pro
LMArena Text: writing and language14021454Gemini 2.5 Pro
LMArena Text: entertainment and media14001441Gemini 2.5 Pro
LMArena Vision: EnglishNot measured1261Only Gemini 2.5 Pro measured
LMArena Vision: ChineseNot measured1300Only Gemini 2.5 Pro measured
LMArena Vision: reading textNot measured1270Only Gemini 2.5 Pro measured
LMArena Vision: humorNot measured1262Only Gemini 2.5 Pro measured
LMArena Vision: captioningNot measured1304Only Gemini 2.5 Pro measured
LMArena Vision: recognizing thingsNot measured1283Only Gemini 2.5 Pro measured
LMArena Vision: creative writingNot measured1265Only Gemini 2.5 Pro measured
LMArena DocumentNot measured1421Only Gemini 2.5 Pro measured
Neither model has a result on the other 12 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for R1 0528 and Gemini 2.5 Pro
BenchmarkR1 0528Gemini 2.5 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
Gemini 2.5 Pro$0.0063Fits in one request

R1 0528 costs less

Repository review

50K input + 3K output tokens

R1 0528$0.032Fits in one request
Gemini 2.5 Pro$0.092Fits in one request

R1 0528 costs less

Agent loop

200K input + 10K output tokens

R1 0528$0.122Exceeds the context window
Gemini 2.5 Pro$0.350Fits 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 Gemini 2.5 Pro
SpecificationDeepSeek R1 0528Google Gemini 2.5 Pro
MakerDeepSeekGoogle
WeightsOpen weightsNo public weights listed
ReleasedMay 28, 2025June 17, 2025
Context window164K1.05M
Input $/Mtok$0.50$1.25
Output $/Mtok$2.18$10.00
Coding indexNot benchmarked19.1
Independent measurements behind it03
Cost per solved taskNot computable$0.64
SourceOpenRouter listingOpenRouter listing

Questions

Is R1 0528 better than Gemini 2.5 Pro for coding?

Only Gemini 2.5 Pro has a coding score, 19.1 on the Vibe Coding Index; R1 0528 has none, so the two cannot be ranked on coding. Pick R1 0528 for output tokens 4.6x cheaper ($2.18 vs $10.00 per million) and work measured by Design Arena: 3D, where it leads.

Which is cheaper, R1 0528 or Gemini 2.5 Pro?

R1 0528 costs $0.50 per million input tokens and $2.18 per million output tokens; Gemini 2.5 Pro costs $1.25 and $10.00. Cost per solved task is known for Gemini 2.5 Pro only.

Which has the bigger context window, R1 0528 or Gemini 2.5 Pro?

Gemini 2.5 Pro, with 1.05M tokens against 164K.

How many benchmarks is the R1 0528 vs Gemini 2.5 Pro comparison based on?

53 benchmarks have a result for both models as of October 22, 2026. R1 0528's coding index rests on 0 independent benchmarks and Gemini 2.5 Pro's on 3. Where only one model has a result, the page says so instead of naming a winner.

Compare next