Enter opens the highlighted result. Esc closes.

R1 0528 vs Grok 4.7

Grok 4.7 leads R1 0528 on the Artificial Analysis Intelligence Index, 46.4 to 13.1, and R1 0528 costs 2.8x less per output token ($2.18 vs $6.00 per million). Updated October 22, 2026; 41 benchmarks have a result for both.

R1 0528DeepSeek
Coding
No data
Overall
46.8
Output $/M
$2.18
17
Grok 4.7xAI
Coding
66.7
Overall
66.6
Output $/M
$6.00
24
vs boards won of 41 shared

R1 0528 or Grok 4.7: which to pick

Only Grok 4.7 has a coding score, 66.7 on the Vibe Coding Index; R1 0528 has none, so the two cannot be ranked on coding.

Pick R1 0528 for output tokens 2.8x cheaper ($2.18 vs $6.00 per million) and work measured by LMArena Text: software and IT or LMArena Text: hard prompts, where it leads.

Shared results
41
R1 0528 only
19
Grok 4.7 only
58
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, Grok 4.7 66.7 from 5 benchmarks.
Widest category
Intelligence, 33.3 points apart on average over 1 shared benchmark.
Widest single gap
ARC-AGI: R1 0528 21.2% against Grok 4.7 90.2%, 69.0 points apart on a 0 to 100 scale.
Output price
R1 0528 $2.18 against Grok 4.7 $6.00 per million output tokens, 2.8x apart.
Context window
R1 0528 takes 164K tokens, Grok 4.7 500K.

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 0528BarsGrok 4.7Ties
Coding11
Intelligence01
Reasoning16
Math03
Knowledge31
Web design06
General126
All shared boards1724
CodingLike-for-likeSix of these boards feed the Vibe Coding Index11
Coding benchmarks for R1 0528 and Grok 4.7
BenchmarkR1 0528Grok 4.7Leader
Aider polyglot71.4%Not measuredOnly R1 0528 measured
LiveBench codingNot measured84.5%Only Grok 4.7 measured
LMArena WebDevNot measured1637Only Grok 4.7 measured
Terminal-Bench 4 (Vals AI)Not measured28.8%Only Grok 4.7 measured
Vibe Code Bench (Vals AI)Not measured86.2%Only Grok 4.7 measured
Code migration (Vals AI)Not measured44.8%Only Grok 4.7 measured
ProgramBench (Vals AI)Not measured0.5%Only Grok 4.7 measured
IOI (Vals AI)Not measured57.7%Only Grok 4.7 measured
Terminal-Bench 2.1 (Vals AI)Not measured73.4%Only Grok 4.7 measured
SWE-bench Multimodal (Vals AI)Not measured42.9%Only Grok 4.7 measured
CyberBench (Vals AI)Not measured69.5%Only Grok 4.7 measured
LiveCodeBench (Artificial Analysis run)77.0%Not measuredOnly R1 0528 measured
SciCodeNot measured57.8%Only Grok 4.7 measured
Terminal-Bench Hard (Artificial Analysis run)15.9%Not measuredOnly R1 0528 measured
FrontierCodeNot measured47.6%Only Grok 4.7 measured
FrontierSWENot measured29.5%Only Grok 4.7 measured
WeirdML41.6%Not measuredOnly R1 0528 measured
ALE-Bench804.1Not measuredOnly R1 0528 measured
LMArena Text: coding14271432Grok 4.7
LMArena Text: software and IT14351421R1 0528
LMArena WebDev: ReactNot measured1663Only Grok 4.7 measured
LMArena WebDev: HTMLNot measured1627Only Grok 4.7 measured
LMArena WebDev: from an imageNot measured1586Only Grok 4.7 measured
Neither model has a result on the other 16 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for R1 0528 and Grok 4.7
BenchmarkR1 0528Grok 4.7Leader
AA Intelligence Index13.146.4Grok 4.7
Epoch Capabilities IndexNot measured154.4Only Grok 4.7 measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index19 boards, none shared
Agentic work benchmarks for R1 0528 and Grok 4.7
BenchmarkR1 0528Grok 4.7Leader
LiveBench researchNot measured86.4%Only Grok 4.7 measured
LiveBench browserNot measured60.0%Only Grok 4.7 measured
LiveBench long agenticNot measured50.1%Only Grok 4.7 measured
SREBench (Vals AI)Not measured3.8%Only Grok 4.7 measured
Finance Agent (Vals AI)Not measured52.3%Only Grok 4.7 measured
Tax Agent Bench (Vals AI)Not measured34.2%Only Grok 4.7 measured
Legal Research Bench (Vals AI)Not measured47.1%Only Grok 4.7 measured
Harvey's Legal Agent Benchmark (Vals AI)Not measured18.3%Only Grok 4.7 measured
Excel Modeling Benchmark (Vals AI)Not measured67.0%Only Grok 4.7 measured
BioMysteryBench (Vals AI)Not measured69.3%Only Grok 4.7 measured
Terminal-Bench-Science (Vals AI)Not measured10.0%Only Grok 4.7 measured
Vals Science IndexNot measured34.8%Only Grok 4.7 measured
Robo-bench (Vals AI)Not measured0.2%Only Grok 4.7 measured
τ²-Bench Telecom36.5%Not measuredOnly R1 0528 measured
APEX-AgentsNot measured54.6%Only Grok 4.7 measured
METR time horizons53.8%Not measuredOnly R1 0528 measured
DeepResearch Bench35.1%Not measuredOnly R1 0528 measured
Vending-Bench 2Not measured$10,537Only Grok 4.7 measured
BrowseComp-Plus16.4%Not measuredOnly R1 0528 measured
Neither model has a result on the other 22 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index16
Reasoning benchmarks for R1 0528 and Grok 4.7
BenchmarkR1 0528Grok 4.7Leader
LiveBench reasoningNot measured82.7%Only Grok 4.7 measured
MysteryMechanism (Vals AI)Not measured25.2%Only Grok 4.7 measured
GPQA Diamond (Artificial Analysis run)81.3%Not measuredOnly R1 0528 measured
Humanity's Last Exam (Artificial Analysis run)15.8%43.1%Grok 4.7
GPQA Diamond (Epoch AI run)76.3%92.7%Grok 4.7
ARC-AGI21.2%90.2%Grok 4.7
ARC-AGI-21.1%61.4%Grok 4.7
SimpleBench40.8%Not measuredOnly R1 0528 measured
Chess puzzlesNot measured41.0%Only Grok 4.7 measured
Mystery game puzzlesNot measured29.0%Only Grok 4.7 measured
LMArena Text: hard prompts14151415R1 0528
LMArena Text: expert13941439Grok 4.7
LMArena Text: hard prompts in English14221427Grok 4.7
LMArena Vision: diagramsNot measured1254Only Grok 4.7 measured
LMArena Vision: homeworkNot measured1309Only Grok 4.7 measured
Neither model has a result on the other 7 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index03
Math benchmarks for R1 0528 and Grok 4.7
BenchmarkR1 0528Grok 4.7Leader
AA Math Index76.0Not measuredOnly R1 0528 measured
AIME 2025 (Artificial Analysis run)76.0%Not measuredOnly R1 0528 measured
OTIS Mock AIME66.4%98.1%Grok 4.7
FrontierMath, tiers 1 to 3Not measured53.0%Only Grok 4.7 measured
FrontierMath, tier 4Not measured17.1%Only Grok 4.7 measured
ProofBenchNot measured26.0%Only Grok 4.7 measured
MATH level 596.6%Not measuredOnly R1 0528 measured
LMArena Text: math14001414Grok 4.7
LMArena Text: mathematical work14151435Grok 4.7
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index31
Knowledge benchmarks for R1 0528 and Grok 4.7
BenchmarkR1 0528Grok 4.7Leader
LegalBench (Vals AI)Not measured84.4%Only Grok 4.7 measured
MedCode (Vals AI)Not measured49.5%Only Grok 4.7 measured
MMLU Pro (Artificial Analysis run)84.9%Not measuredOnly R1 0528 measured
SimpleQA VerifiedNot measured56.0%Only Grok 4.7 measured
LMArena Text: science14301419R1 0528
LMArena Text: medicine and healthcare14451395R1 0528
LMArena Text: legal and government14191423Grok 4.7
LMArena Text: business and finance14021389R1 0528
Neither model has a result on the other 10 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index06
Web design benchmarks for R1 0528 and Grok 4.7
BenchmarkR1 0528Grok 4.7Leader
Design Arena: websites11491255Grok 4.7
Design Arena: UI components11001275Grok 4.7
Design Arena: game dev10971314Grok 4.7
Design Arena: data viz11821238Grok 4.7
Design Arena: 3D11101274Grok 4.7
Design Arena: all code categories11381273Grok 4.7
Design Arena: SVG1038Not measuredOnly R1 0528 measured
LiveBench designNot measured78.0%Only Grok 4.7 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index126
General benchmarks for R1 0528 and Grok 4.7
BenchmarkR1 0528Grok 4.7Leader
Vals IndexNot measured58.6%Only Grok 4.7 measured
LiveBench data analysisNot measured76.9%Only Grok 4.7 measured
Public Benefits Bench (Vals AI)Not measured65.6%Only Grok 4.7 measured
SAGE (Vals AI)Not measured40.8%Only Grok 4.7 measured
MedScribe (Vals AI)Not measured89.4%Only Grok 4.7 measured
IFBench39.6%Not measuredOnly R1 0528 measured
Long context reasoning (AA-LCR)55.7%80.0%Grok 4.7
DTBenchNot measured96.0%Only Grok 4.7 measured
LMCANot measured49.4%Only Grok 4.7 measured
Fiction.LiveBench75.0%Not measuredOnly R1 0528 measured
Creative writing (Lech Mazur)81.9%Not measuredOnly R1 0528 measured
Furniture assemblyNot measured20.8%Only Grok 4.7 measured
LMArena Text14281399R1 0528
LMArena Text: instruction following13821406Grok 4.7
LMArena Text: creative writing14051381R1 0528
LMArena VisionNot measured1231Only Grok 4.7 measured
LMArena Text: English14371411R1 0528
LMArena Text: non-English14121386R1 0528
LMArena Text: Chinese14421449Grok 4.7
LMArena Text: German14041366R1 0528
LMArena Text: Japanese13911359R1 0528
LMArena Text: Korean13601383Grok 4.7
LMArena Text: French14171431Grok 4.7
LMArena Text: Spanish14111406R1 0528
LMArena Text: Russian14231399R1 0528
LMArena Text: Polish14281420R1 0528
LMArena Text: multi-turn14051394R1 0528
LMArena Text: longer queries13911416Grok 4.7
LMArena Text: writing and language14021394R1 0528
LMArena Text: entertainment and media14001376R1 0528
LMArena Vision: EnglishNot measured1229Only Grok 4.7 measured
LMArena Vision: ChineseNot measured1250Only Grok 4.7 measured
LMArena Vision: reading textNot measured1241Only Grok 4.7 measured
LMArena Vision: creative writingNot measured1217Only Grok 4.7 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 Grok 4.7
BenchmarkR1 0528Grok 4.7Leader
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
Grok 4.7$0.0050Fits in one request

R1 0528 costs less

Repository review

50K input + 3K output tokens

R1 0528$0.032Fits in one request
Grok 4.7$0.118Fits in one request

R1 0528 costs less

Agent loop

200K input + 10K output tokens

R1 0528$0.122Exceeds the context window
Grok 4.7$0.460Fits 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 Grok 4.7
SpecificationDeepSeek R1 0528SpaceXAI Grok 4.7
MakerDeepSeekxAI
WeightsOpen weightsNo public weights listed
ReleasedMay 28, 2025September 21, 2026
Context window164K500K
Input $/Mtok$0.50$2.00
Output $/Mtok$2.18$6.00
Coding indexNot benchmarked66.7
Independent measurements behind it05
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is R1 0528 better than Grok 4.7 for coding?

Only Grok 4.7 has a coding score, 66.7 on the Vibe Coding Index; R1 0528 has none, so the two cannot be ranked on coding. Pick R1 0528 for output tokens 2.8x cheaper ($2.18 vs $6.00 per million) and work measured by LMArena Text: software and IT or LMArena Text: hard prompts, where it leads.

Which is cheaper, R1 0528 or Grok 4.7?

R1 0528 costs $0.50 per million input tokens and $2.18 per million output tokens; Grok 4.7 costs $2.00 and $6.00. Cost per solved task is known for neither of them.

Which has the bigger context window, R1 0528 or Grok 4.7?

Grok 4.7, with 500K tokens against 164K.

How many benchmarks is the R1 0528 vs Grok 4.7 comparison based on?

41 benchmarks have a result for both models as of October 22, 2026. R1 0528's coding index rests on 0 independent benchmarks and Grok 4.7's on 5. Where only one model has a result, the page says so instead of naming a winner.

Compare next