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
Specifications
- Context window
- 1M
- Max output
- 1M
- Avg output (7d)
- 998 tokens
- Longer than 70% of models
Benchmarks
Benchmarks
Sourced from Artificial Analysis.
Intelligence Index
14.0
Coding Index
35.2
Agentic Index
14.0
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
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