Grok 4.3

Grok 4.3 is SpaceXAI's reasoning model for text and image inputs, built for agentic workflows, instruction following, factual accuracy, long-document analysis, and deep research. Reasoning is always active and requests above 200k total tokens are charged at the higher long-context rate. Content policy rejections can still be charged: SpaceXAI may pass through a $0.05 moderation-failure fee, or a $0.055 usage-guidelines violation fee, depending on which rejection upstream returns.

  • Reasoning
  • Vision
  • Tool Calling
  • Structured Output

Added Apr 30, 2026

Pricing

Auto routing · per 1M tokens
Input
$1.25
Output
$2.50
Cache read
$0.20
Compare provider prices

Specifications

Context window
1M
Max output
1M
Avg output (7d)
998 tokens
Longer than 70% of models

Benchmarks

Sourced from Artificial Analysis.

Intelligence Index

14.0

Better than 58% of models compared

Coding Index

35.2

Better than 41% of models compared

Agentic Index

14.0

Better than 45% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

0.9%

Better than 13% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

722 Elo

Better than 31% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

945 Elo

Better than 41% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

2.8%

Better than 16% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

32.3%

Better than 31% of models compared

Reasoning

HLE

Humanity's Last Exam

6.8%

Better than 41% of models compared

IFBench

Instruction-following benchmark

47.6%

Better than 56% of models compared

CritPt

Research-level physics reasoning

0.0%

Coding

Terminal-Bench v4.0

Practical coding and terminal tasks

0.0%

Better than 16% of models compared

SciCode

Python programming for scientific computing

39.4%

Better than 23% of models compared

Knowledge

AA-Omniscience Accuracy

Proportion of correctly answered questions

23.7%

AA-Omniscience Hallucination Rate

Rate of incorrect answers among non-correct responses

74.5%

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

65.8%

Better than 41% of models compared

Terminal-Bench Hard (legacy)

Agentic coding and terminal use

18.9%

Better than 59% of models compared

T²-Bench Telecom (legacy)

Conversational AI agents in dual-control scenarios

65.8%

Better than 60% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

32.3%

Better than 31% of models compared

GDPval-AA (unversioned / legacy)

Economically valuable tasks

22.3%

Last updated Oct 11, 2026

Artificial Analysis

Providers

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