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GPT-5.2 vs Grok 4.7

Grok 4.7 leads GPT-5.2 on coding, 66.7 to 55.9 on the Vibe Coding Index, and Grok 4.7 costs 2.3x less per output token ($6.00 vs $14.00 per million). Updated October 22, 2026; 66 benchmarks have a result for both.

Replace GPT-5.2 Replace Grok 4.7
GPT-5.2OpenAI
Coding
55.9
Overall
60.7
Output $/M
$14.00
36
Grok 4.7xAI
Coding
66.7
Overall
66.6
Output $/M
$6.00
30
vs boards won of 66 shared

GPT-5.2 or Grok 4.7: which to pick

Pick Grok 4.7 for coding: it scores 66.7 on the Vibe Coding Index against 55.9 for GPT-5.2.

Pick GPT-5.2 for work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.

Model AGPT-5.2OpenAI55.9/1003 independent benchmarks$1.91 per solved taskModel BGrok 4.7xAI66.7/1005 independent benchmarksNo cost per solved task
Shared results
66
GPT-5.2 only
48
Grok 4.7 only
33
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
GPT-5.2 55.9 from 3 benchmarks, Grok 4.7 66.7 from 5 benchmarks.
Widest category
Agentic work, 43.4 points apart on average over 1 shared benchmark.
Widest single gap
Vending-Bench 2: GPT-5.2 $3,591 against Grok 4.7 $10,537, 43.4 points apart on a 0 to 100 scale.
Output price
GPT-5.2 $14.00 against Grok 4.7 $6.00 per million output tokens, 2.3x apart.
Context window
GPT-5.2 takes 400K 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
CategoryGPT-5.2BarsGrok 4.7Ties
Coding23
Intelligence02
Agentic work01
Reasoning56
Math42
Knowledge52
Web design06
General208
All shared boards3630
CodingLike-for-likeSix of these boards feed the Vibe Coding Index23
Coding benchmarks for GPT-5.2 and Grok 4.7
BenchmarkGPT-5.2Grok 4.7Leader
SWE-bench Verified72.8%Not measuredOnly GPT-5.2 measured
SWE-bench Multilingual66.7%Not measuredOnly GPT-5.2 measured
LiveBench codingNot measured84.5%Only Grok 4.7 measured
LMArena WebDev14171637Grok 4.7
LiveCodeBench (Vals AI)85.4%Not measuredOnly GPT-5.2 measured
SWE-bench Verified (Vals AI)75.8%Not measuredOnly GPT-5.2 measured
Terminal-Bench 4 (Vals AI)Not measured28.8%Only Grok 4.7 measured
Vibe Code Bench (Vals AI)53.5%86.2%Grok 4.7
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)89.4%Not measuredOnly GPT-5.2 measured
SciCodeNot measured57.8%Only Grok 4.7 measured
Terminal-Bench Hard (Artificial Analysis run)47.0%Not measuredOnly GPT-5.2 measured
Terminal-Bench64.9%Not measuredOnly GPT-5.2 measured
FrontierCodeNot measured47.6%Only Grok 4.7 measured
FrontierSWENot measured29.5%Only Grok 4.7 measured
GSO-Bench27.5%Not measuredOnly GPT-5.2 measured
WeirdML72.2%Not measuredOnly GPT-5.2 measured
ALE-Bench1293.5Not measuredOnly GPT-5.2 measured
LMArena Text: coding14421432GPT-5.2
LMArena Text: software and IT14341421GPT-5.2
LMArena WebDev: ReactNot measured1663Only Grok 4.7 measured
LMArena WebDev: HTML14271627Grok 4.7
LMArena WebDev: from an imageNot measured1586Only Grok 4.7 measured
Neither model has a result on the other 11 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for GPT-5.2 and Grok 4.7
BenchmarkGPT-5.2Grok 4.7Leader
AA Intelligence Index30.446.4Grok 4.7
Epoch Capabilities Index153.7154.4Grok 4.7
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GPT-5.2 and Grok 4.7
BenchmarkGPT-5.2Grok 4.7Leader
Design Arena: agents building web apps1086Not measuredOnly GPT-5.2 measured
Design Arena: agents building full-stack apps1043Not measuredOnly GPT-5.2 measured
Design Arena: agents building mobile apps1093Not measuredOnly GPT-5.2 measured
Design Arena: agents building Android apps1059Not measuredOnly GPT-5.2 measured
Design Arena: agents building Godot games1142Not measuredOnly GPT-5.2 measured
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 Telecom84.8%Not measuredOnly GPT-5.2 measured
APEX-AgentsNot measured54.6%Only Grok 4.7 measured
METR time horizons75.3%Not measuredOnly GPT-5.2 measured
Remote Labor Index2.5%Not measuredOnly GPT-5.2 measured
GDPval49.7%Not measuredOnly GPT-5.2 measured
DeepResearch Bench41.1%Not measuredOnly GPT-5.2 measured
Vending-Bench 2$3,591$10,537Grok 4.7
Berkeley Function Calling Leaderboard (BFCL)55.9%Not measuredOnly GPT-5.2 measured
Neither model has a result on the other 15 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index56
Reasoning benchmarks for GPT-5.2 and Grok 4.7
BenchmarkGPT-5.2Grok 4.7Leader
GPQA Diamond91.7%Not measuredOnly GPT-5.2 measured
LiveBench reasoningNot measured82.7%Only Grok 4.7 measured
MysteryMechanism (Vals AI)Not measured25.2%Only Grok 4.7 measured
MMMU Pro (Vals AI)86.7%Not measuredOnly GPT-5.2 measured
GPQA Diamond (Artificial Analysis run)90.3%Not measuredOnly GPT-5.2 measured
Humanity's Last Exam (Artificial Analysis run)37.7%43.1%Grok 4.7
GPQA Diamond (Epoch AI run)91.4%92.7%Grok 4.7
ARC-AGI86.2%90.2%Grok 4.7
ARC-AGI-252.9%61.4%Grok 4.7
SimpleBench45.8%Not measuredOnly GPT-5.2 measured
Chess puzzles48.0%41.0%GPT-5.2
Mystery game puzzles23.0%29.0%Grok 4.7
EBR-bench23.0%Not measuredOnly GPT-5.2 measured
VPCT84.0%Not measuredOnly GPT-5.2 measured
LMArena Text: hard prompts14281415GPT-5.2
LMArena Text: expert14451439GPT-5.2
LMArena Text: hard prompts in English14311427GPT-5.2
LMArena Vision: diagrams12651254GPT-5.2
LMArena Vision: homework12761309Grok 4.7
Kagi LLM Benchmark73.3%Not measuredOnly GPT-5.2 measured
Neither model has a result on the other 2 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index42
Math benchmarks for GPT-5.2 and Grok 4.7
BenchmarkGPT-5.2Grok 4.7Leader
AIME96.9%Not measuredOnly GPT-5.2 measured
MGSM (Vals AI)94.0%Not measuredOnly GPT-5.2 measured
AA Math Index99.0Not measuredOnly GPT-5.2 measured
AIME 2025 (Artificial Analysis run)99.0%Not measuredOnly GPT-5.2 measured
OTIS Mock AIME96.1%98.1%Grok 4.7
FrontierMath, tiers 1 to 367.4%53.0%GPT-5.2
FrontierMath, tier 431.7%17.1%GPT-5.2
ProofBench15.0%26.0%Grok 4.7
LMArena Text: math14401414GPT-5.2
LMArena Text: mathematical work14421435GPT-5.2
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index52
Knowledge benchmarks for GPT-5.2 and Grok 4.7
BenchmarkGPT-5.2Grok 4.7Leader
MMLU Pro86.2%Not measuredOnly GPT-5.2 measured
LegalBench (Vals AI)82.8%84.4%Grok 4.7
CaseLaw (Vals AI)66.0%Not measuredOnly GPT-5.2 measured
CorpFin (Vals AI)65.9%Not measuredOnly GPT-5.2 measured
TaxEval (Vals AI)75.8%Not measuredOnly GPT-5.2 measured
MortgageTax (Vals AI)67.1%Not measuredOnly GPT-5.2 measured
MedQA (Vals AI)94.1%Not measuredOnly GPT-5.2 measured
MedCode (Vals AI)49.8%49.5%GPT-5.2
MMLU Pro (Artificial Analysis run)87.4%Not measuredOnly GPT-5.2 measured
SimpleQA Verified37.1%56.0%Grok 4.7
LMArena Text: science14251419GPT-5.2
LMArena Text: medicine and healthcare14251395GPT-5.2
LMArena Text: legal and government14271423GPT-5.2
LMArena Text: business and finance14141389GPT-5.2
FACTS Benchmark Suite48.5%Not measuredOnly GPT-5.2 measured
Neither model has a result on the other 3 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index06
Web design benchmarks for GPT-5.2 and Grok 4.7
BenchmarkGPT-5.2Grok 4.7Leader
Design Arena: websites11951255Grok 4.7
Design Arena: UI components11901275Grok 4.7
Design Arena: game dev11951314Grok 4.7
Design Arena: data viz12021238Grok 4.7
Design Arena: 3D10791274Grok 4.7
Design Arena: all code categories11701273Grok 4.7
Design Arena: SVG1139Not measuredOnly GPT-5.2 measured
Design Arena: ASCII art1152Not measuredOnly GPT-5.2 measured
LiveBench designNot measured78.0%Only Grok 4.7 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index208
General benchmarks for GPT-5.2 and Grok 4.7
BenchmarkGPT-5.2Grok 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)49.3%40.8%GPT-5.2
MedScribe (Vals AI)84.4%89.4%Grok 4.7
IFBench75.4%Not measuredOnly GPT-5.2 measured
Long context reasoning (AA-LCR)82.7%80.0%GPT-5.2
DTBench90.9%96.0%Grok 4.7
LMCA43.9%49.4%Grok 4.7
CL-bench18.2%Not measuredOnly GPT-5.2 measured
Furniture assembly38.3%20.8%GPT-5.2
LMArena Text14161399GPT-5.2
LMArena Text: instruction following14091406GPT-5.2
LMArena Text: creative writing13761381Grok 4.7
LMArena Vision12461231GPT-5.2
LMArena Text: English14221411GPT-5.2
LMArena Text: non-English14051386GPT-5.2
LMArena Text: Chinese14471449Grok 4.7
LMArena Text: German14441366GPT-5.2
LMArena Text: Japanese14201359GPT-5.2
LMArena Text: Korean13741383Grok 4.7
LMArena Text: French14541431GPT-5.2
LMArena Text: Spanish14131406GPT-5.2
LMArena Text: Russian14151399GPT-5.2
LMArena Text: Polish14261420GPT-5.2
LMArena Text: multi-turn14191394GPT-5.2
LMArena Text: longer queries14131416Grok 4.7
LMArena Text: writing and language13921394Grok 4.7
LMArena Text: entertainment and media13781376GPT-5.2
LMArena Vision: English12431229GPT-5.2
LMArena Vision: Chinese13051250GPT-5.2
LMArena Vision: reading text12571241GPT-5.2
LMArena Vision: humor1227Not measuredOnly GPT-5.2 measured
LMArena Vision: captioning1267Not measuredOnly GPT-5.2 measured
LMArena Vision: recognizing things1232Not measuredOnly GPT-5.2 measured
LMArena Vision: creative writing12251217GPT-5.2
LMArena Document1405Not measuredOnly GPT-5.2 measured
Neither model has a result on the other 9 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-5.2 and Grok 4.7
BenchmarkGPT-5.2Grok 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

GPT-5.2$0.0088Fits in one request
Grok 4.7$0.0050Fits in one request

Grok 4.7 costs less

Repository review

50K input + 3K output tokens

GPT-5.2$0.130Fits in one request
Grok 4.7$0.118Fits in one request

Grok 4.7 costs less

Agent loop

200K input + 10K output tokens

GPT-5.2$0.490Fits in one request
Grok 4.7$0.460Fits in one request

Grok 4.7 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 GPT-5.2 and Grok 4.7
SpecificationOpenAI GPT-5.2SpaceXAI Grok 4.7
MakerOpenAIxAI
WeightsNo public weights listedNo public weights listed
ReleasedDecember 10, 2025September 21, 2026
Context window400K500K
Input $/Mtok$1.75$2.00
Output $/Mtok$14.00$6.00
Coding index55.966.7
Independent measurements behind it35
Cost per solved task$1.91Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-5.2 better than Grok 4.7 for coding?

Pick Grok 4.7 for coding: it scores 66.7 on the Vibe Coding Index against 55.9 for GPT-5.2. Pick GPT-5.2 for work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.

Which is cheaper, GPT-5.2 or Grok 4.7?

GPT-5.2 costs $1.75 per million input tokens and $14.00 per million output tokens; Grok 4.7 costs $2.00 and $6.00. Cost per solved task is known for GPT-5.2 only.

Which has the bigger context window, GPT-5.2 or Grok 4.7?

Grok 4.7, with 500K tokens against 400K.

How many benchmarks is the GPT-5.2 vs Grok 4.7 comparison based on?

66 benchmarks have a result for both models as of October 22, 2026. GPT-5.2's coding index rests on 3 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.

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