xAI's most capable model for coding and knowledge work (September 2026): a larger base model, longer RL training, a 500K-token context window and $2/$6 per million tokens.
Context window
500k
tokens
Release date
21 September 2026
Access:APIHostedDeployment:☁ Cloud
Overview
Access & deployment
APIHosted
Cloud
Weights: Closed
Key parameters
📏 Context: 500k
✓ Tools
📥 Input: text, image, documents
Technical specification
Context window
500k
tokens
Knowledge cutoff
1 May 2026
Knowledge boundary
Features:✓ Tool use
Modalities
⬇ Input
textimagedocuments
⬆ Output
textcodestructured_data
Capabilities and applications
Native model capabilities
Reasoning
The model's ability to reason logically and solve complex problems.
Category: reasoning
Advanced reasoning
The ability to perform multi-step, structured reasoning: analysing problems, planning steps, and drawing conclusions from hypotheses. Reasoning-first models (e.g. GPT-5.1 Thinking) dedicate a portion of inference to chains of thought before responding.
Category: reasoning
Multi-step reasoning
Carrying out multi-step chains of reasoning across long, complex tasks.
Category: reasoning
Long context
Support for large context windows — tens to hundreds of thousands (or millions) of input tokens. Enables analysis of entire codebases, long documents, and many parallel conversations without losing earlier information. GPT-5.1 supports 400,000 tokens.
Category: language
Coding
Generating, analysing and modifying code in many programming languages. Covers writing functions, debugging, refactoring, code review, and creating tests. Measured by benchmarks such as HumanEval and SWE-bench.
Category: coding
Agentic coding
Multi-hour, multi-step programming tasks performed autonomously by the model: cloning a repository, running tests, iterating on fixes, integrating with CLI tools. Characteristic of Codex variants (GPT-5.1-Codex-Mini, Codex-Max).
Category: coding
Agentic capability
The model's ability to autonomously plan and execute multi-step tasks by sequentially using tools, maintaining context, and adapting to intermediate results.
Category: planning
Tool use
The model's ability to call external functions, APIs and tools during a conversation: calculator, search engine, code editor, database. The model decides when and how to use a tool and interprets its result.
Category: planning
Function Calling
Category: planning
Structured output
Producing data in structured formats such as JSON.
Category: structured_generation
Image understanding
Analysing and interpreting the content of images.
Category: vision
Web browsing
Ability of the model to autonomously search and browse web pages to retrieve up-to-date information.
Category: other
Multi-step project execution
The ability to autonomously drive multi-hour, multi-step projects: decomposing the task, planning the sequence of actions, iteratively delivering results, and adjusting based on feedback. Key for enterprise and knowledge-work agents.
Category: planning
Planning
Forming and executing action plans for complex tasks.
Category: planning
Benchmark results
5 benchmarks
EEBench
accuracy
64.0%%
📄 xAI — ogłoszenie Grok 4.7
Electrical engineering benchmark. Grok 4.7 scores 64.0% versus 53.0% for the previous version.
CursorBench 4.0
accuracy
46.3%%
📄 xAI — ogłoszenie Grok 4.7
Agentic coding benchmark in Cursor. Grok 4.7 scores 46.3% versus 40.4%.
Terminal-Bench 4.0
accuracy
38.0%%
📄 xAI — ogłoszenie Grok 4.7
Terminal agentic tasks. Grok 4.7 scores 38.0% versus 20.3%.
Harvey Legal Agent Benchmark
accuracy
19.6%%
📄 xAI — ogłoszenie Grok 4.7
Benchmark of agentic legal tasks. Grok 4.7 scores 19.6% versus 15.8%.
Benchmark biobezpieczeństwa (LatchBio)
accuracy
62.4%%
📄 xAI — ogłoszenie Grok 4.7
Assessment of capability in the biosafety domain.
