Enter opens the highlighted result. Esc closes.

GPT-5 Nano vs Grok 4.3

Grok 4.3 leads GPT-5 Nano on the Artificial Analysis Intelligence Index, 24.9 to 13.0, and GPT-5 Nano costs 6.3x less per output token ($0.40 vs $2.50 per million). Updated October 22, 2026; 68 benchmarks have a result for both.

GPT-5 NanoOpenAI
Coding
No data
Overall
35.6
Output $/M
$0.40
4
Grok 4.3xAI
Coding
37.4
Overall
53.1
Output $/M
$2.50
64
vs boards won of 68 shared

GPT-5 Nano or Grok 4.3: which to pick

Only Grok 4.3 has a coding score, 37.4 on the Vibe Coding Index; GPT-5 Nano has none, so the two cannot be ranked on coding.

Pick GPT-5 Nano for output tokens 6.3x cheaper ($0.40 vs $2.50 per million) and a lower cost per solved task ($0.109 against $0.31).

Shared results
68
GPT-5 Nano only
19
Grok 4.3 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
GPT-5 Nano not benchmarked, Grok 4.3 37.4 from 3 benchmarks.
Widest category
Agentic work, 61.1 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: GPT-5 Nano 36.5% against Grok 4.3 97.7%, 61.1 points apart on a 0 to 100 scale.
Output price
GPT-5 Nano $0.40 against Grok 4.3 $2.50 per million output tokens, 6.3x apart.
Context window
GPT-5 Nano takes 400K tokens, Grok 4.3 1M.

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 NanoBarsGrok 4.3Ties
Coding05
Intelligence01
Agentic work01
Reasoning110
Math15
Knowledge110
Web design06
General126
All shared boards464
CodingLike-for-likeSix of these boards feed the Vibe Coding Index05
Coding benchmarks for GPT-5 Nano and Grok 4.3
BenchmarkGPT-5 NanoGrok 4.3Leader
SWE-bench Verified34.8%Not measuredOnly GPT-5 Nano measured
LMArena WebDevNot measured1358Only Grok 4.3 measured
AA Coding IndexNot measured42.2Only Grok 4.3 measured
LiveCodeBench (Vals AI)70.2%84.5%Grok 4.3
SWE-bench Verified (Vals AI)Not measured71.4%Only Grok 4.3 measured
Vibe Code Bench (Vals AI)Not measured19.4%Only Grok 4.3 measured
Code migration (Vals AI)Not measured6.8%Only Grok 4.3 measured
ProgramBench (Vals AI)Not measured0.0%Only Grok 4.3 measured
Terminal-Bench 2.1 (Vals AI)Not measured42.0%Only Grok 4.3 measured
SkillsBench (Vals AI)Not measured40.6%Only Grok 4.3 measured
LiveCodeBench (Artificial Analysis run)78.9%Not measuredOnly GPT-5 Nano measured
SciCodeNot measured48.3%Only Grok 4.3 measured
Terminal-Bench Hard (Artificial Analysis run)17.4%37.9%Grok 4.3
Terminal-Bench21.8%Not measuredOnly GPT-5 Nano measured
WeirdML38.1%49.9%Grok 4.3
ALE-BenchNot measured944.2Only Grok 4.3 measured
LMArena Text: coding13511415Grok 4.3
LMArena Text: software and IT13451416Grok 4.3
LMArena WebDev: ReactNot measured1353Only Grok 4.3 measured
LMArena WebDev: HTMLNot measured1367Only Grok 4.3 measured
LMArena WebDev: from an imageNot measured1375Only Grok 4.3 measured
Neither model has a result on the other 18 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GPT-5 Nano and Grok 4.3
BenchmarkGPT-5 NanoGrok 4.3Leader
AA Intelligence Index13.024.9Grok 4.3
Epoch Capabilities Index139.2Not measuredOnly GPT-5 Nano measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GPT-5 Nano and Grok 4.3
BenchmarkGPT-5 NanoGrok 4.3Leader
AA Agentic IndexNot measured15.5Only Grok 4.3 measured
Design Arena: agents building web appsNot measured1126Only Grok 4.3 measured
Design Arena: agents building full-stack appsNot measured1006Only Grok 4.3 measured
Design Arena: agents building mobile appsNot measured1058Only Grok 4.3 measured
Design Arena: agents building Android appsNot measured962Only Grok 4.3 measured
Design Arena: agents building Godot gamesNot measured914Only Grok 4.3 measured
Design Arena: agents building gamesNot measured1007Only Grok 4.3 measured
Design Arena: HTML slidesNot measured959Only Grok 4.3 measured
Design Arena: PowerPoint slidesNot measured988Only Grok 4.3 measured
Design Arena: spreadsheetsNot measured879Only Grok 4.3 measured
Finance Agent (Vals AI)Not measured37.7%Only Grok 4.3 measured
Tax Agent Bench (Vals AI)Not measured6.2%Only Grok 4.3 measured
Legal Research Bench (Vals AI)Not measured15.4%Only Grok 4.3 measured
Excel Modeling Benchmark (Vals AI)Not measured18.4%Only Grok 4.3 measured
τ²-Bench Telecom36.5%97.7%Grok 4.3
Vending-Bench 2Not measured$35Only Grok 4.3 measured
LMArena SearchNot measured1166Only Grok 4.3 measured
Berkeley Function Calling Leaderboard (BFCL)51.4%Not measuredOnly GPT-5 Nano measured
Neither model has a result on the other 23 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index110
Reasoning benchmarks for GPT-5 Nano and Grok 4.3
BenchmarkGPT-5 NanoGrok 4.3Leader
GPQA Diamond63.4%91.4%Grok 4.3
MMMU Pro (Vals AI)70.9%83.1%Grok 4.3
GPQA Diamond (Artificial Analysis run)67.6%90.1%Grok 4.3
Humanity's Last Exam (Artificial Analysis run)9.5%37.2%Grok 4.3
GPQA Diamond (Epoch AI run)69.4%88.8%Grok 4.3
ARC-AGI20.7%Not measuredOnly GPT-5 Nano measured
ARC-AGI-22.6%Not measuredOnly GPT-5 Nano measured
Chess puzzles22.0%18.0%GPT-5 Nano
Mystery game puzzles9.0%Not measuredOnly GPT-5 Nano measured
VPCT37.2%Not measuredOnly GPT-5 Nano measured
LMArena Text: hard prompts13281396Grok 4.3
LMArena Text: expert13211384Grok 4.3
LMArena Text: hard prompts in English13381402Grok 4.3
LMArena Vision: diagrams11411241Grok 4.3
LMArena Vision: homework11571241Grok 4.3
Kagi LLM Benchmark62.2%Not measuredOnly GPT-5 Nano measured
Neither model has a result on the other 6 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index15
Math benchmarks for GPT-5 Nano and Grok 4.3
BenchmarkGPT-5 NanoGrok 4.3Leader
AIME81.2%Not measuredOnly GPT-5 Nano measured
MATH 500 (Vals AI)93.8%Not measuredOnly GPT-5 Nano measured
MGSM (Vals AI)89.3%Not measuredOnly GPT-5 Nano measured
AA Math Index83.7Not measuredOnly GPT-5 Nano measured
AIME 2025 (Artificial Analysis run)83.7%Not measuredOnly GPT-5 Nano measured
OTIS Mock AIME81.1%93.3%Grok 4.3
FrontierMath, tiers 1 to 320.0%42.8%Grok 4.3
FrontierMath, tier 42.4%14.6%Grok 4.3
ProofBench12.0%11.0%GPT-5 Nano
MATH level 595.2%Not measuredOnly GPT-5 Nano measured
LMArena Text: math13171387Grok 4.3
LMArena Text: mathematical work13261396Grok 4.3
Neither model has a result on the other 1 board here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index110
Knowledge benchmarks for GPT-5 Nano and Grok 4.3
BenchmarkGPT-5 NanoGrok 4.3Leader
MMLU Pro76.1%85.8%Grok 4.3
LegalBench (Vals AI)50.1%84.5%Grok 4.3
CaseLaw (Vals AI)52.6%79.3%Grok 4.3
CorpFin (Vals AI)Not measured68.5%Only Grok 4.3 measured
TaxEval (Vals AI)67.4%70.8%Grok 4.3
MortgageTax (Vals AI)53.6%48.3%GPT-5 Nano
MedQA (Vals AI)93.3%Not measuredOnly GPT-5 Nano measured
MedCode (Vals AI)30.4%38.1%Grok 4.3
MMLU Pro (Artificial Analysis run)78.0%Not measuredOnly GPT-5 Nano measured
SimpleQA Verified11.7%33.2%Grok 4.3
LMArena Text: science13181399Grok 4.3
LMArena Text: medicine and healthcare13351384Grok 4.3
LMArena Text: legal and government12981386Grok 4.3
LMArena Text: business and finance13111381Grok 4.3
Neither model has a result on the other 4 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index06
Web design benchmarks for GPT-5 Nano and Grok 4.3
BenchmarkGPT-5 NanoGrok 4.3Leader
Design Arena: websites11011196Grok 4.3
Design Arena: UI components10641190Grok 4.3
Design Arena: game dev10441173Grok 4.3
Design Arena: data viz10561179Grok 4.3
Design Arena: 3D9651124Grok 4.3
Design Arena: all code categories10841186Grok 4.3
Design Arena: SVGNot measured1080Only Grok 4.3 measured
Design Arena: ASCII artNot measured1125Only Grok 4.3 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 GPT-5 Nano and Grok 4.3
BenchmarkGPT-5 NanoGrok 4.3Leader
Public Benefits Bench (Vals AI)Not measured51.7%Only Grok 4.3 measured
SAGE (Vals AI)30.4%19.7%GPT-5 Nano
MedScribe (Vals AI)72.9%74.4%Grok 4.3
Vals Multimodal IndexNot measured43.3%Only Grok 4.3 measured
IFBench67.6%83.3%Grok 4.3
Long context reasoning (AA-LCR)45.0%75.0%Grok 4.3
DTBench62.7%90.7%Grok 4.3
LMCA7.9%38.3%Grok 4.3
Fiction.LiveBench44.4%Not measuredOnly GPT-5 Nano measured
LMArena Text13201397Grok 4.3
LMArena Text: instruction following13061366Grok 4.3
LMArena Text: creative writing12491380Grok 4.3
LMArena Vision11591229Grok 4.3
LMArena Text: English13211404Grok 4.3
LMArena Text: non-English13131385Grok 4.3
LMArena Text: Chinese13561422Grok 4.3
LMArena Text: German13271395Grok 4.3
LMArena Text: Japanese12271379Grok 4.3
LMArena Text: Korean12691354Grok 4.3
LMArena Text: FrenchNot measured1411Only Grok 4.3 measured
LMArena Text: Spanish13601399Grok 4.3
LMArena Text: Russian12961399Grok 4.3
LMArena Text: Polish13141398Grok 4.3
LMArena Text: multi-turn13111406Grok 4.3
LMArena Text: longer queries13121393Grok 4.3
LMArena Text: writing and language12831385Grok 4.3
LMArena Text: entertainment and media12681370Grok 4.3
LMArena Vision: English11701235Grok 4.3
LMArena Vision: Chinese11761257Grok 4.3
LMArena Vision: reading text11521235Grok 4.3
LMArena Vision: humor11101222Grok 4.3
LMArena Vision: recognizing thingsNot measured1217Only Grok 4.3 measured
LMArena Vision: creative writingNot measured1237Only Grok 4.3 measured
Neither model has a result on the other 13 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-5 Nano and Grok 4.3
BenchmarkGPT-5 NanoGrok 4.3Leader
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 Nano$0.0003Fits in one request
Grok 4.3$0.0025Fits in one request

GPT-5 Nano costs less

Repository review

50K input + 3K output tokens

GPT-5 Nano$0.0037Fits in one request
Grok 4.3$0.070Fits in one request

GPT-5 Nano costs less

Agent loop

200K input + 10K output tokens

GPT-5 Nano$0.014Fits in one request
Grok 4.3$0.275Fits in one request

GPT-5 Nano 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 Nano and Grok 4.3
SpecificationOpenAI GPT-5 NanoSpaceXAI Grok 4.3
MakerOpenAIxAI
WeightsNo public weights listedNo public weights listed
ReleasedAugust 7, 2025April 30, 2026
Context window400K1M
Input $/Mtok$0.05$1.25
Output $/Mtok$0.40$2.50
Coding indexNot benchmarked37.4
Independent measurements behind it03
Cost per solved task$0.109$0.31
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-5 Nano better than Grok 4.3 for coding?

Only Grok 4.3 has a coding score, 37.4 on the Vibe Coding Index; GPT-5 Nano has none, so the two cannot be ranked on coding. Pick GPT-5 Nano for output tokens 6.3x cheaper ($0.40 vs $2.50 per million) and a lower cost per solved task ($0.109 against $0.31).

Which is cheaper, GPT-5 Nano or Grok 4.3?

GPT-5 Nano costs $0.05 per million input tokens and $0.40 per million output tokens; Grok 4.3 costs $1.25 and $2.50. Per solved SWE-bench Verified task, GPT-5 Nano costs $0.109 and Grok 4.3 $0.31.

Which has the bigger context window, GPT-5 Nano or Grok 4.3?

Grok 4.3, with 1M tokens against 400K.

How many benchmarks is the GPT-5 Nano vs Grok 4.3 comparison based on?

68 benchmarks have a result for both models as of October 22, 2026. GPT-5 Nano's coding index rests on 0 independent benchmarks and Grok 4.3's on 3. Where only one model has a result, the page says so instead of naming a winner.

Compare next