What is Grok 4.7?

Grok 4.7 is a frontier artificial intelligence model from SpaceXAI built for demanding coding, knowledge work, and long-running agent workflows. The model uses a larger base architecture than Grok 4.6 and was trained with an extended reinforcement learning process focused on more difficult and longer-duration tasks. Its training emphasizes problems that may require hours of work, making it suitable for workflows that involve planning, execution, verification, and repeated tool use. Grok 4.7 improves self-checking behavior and long-context management so it can maintain task state more effectively across complex operations. The model also natively understands the Grok Bot harness, which improves conversational performance and general knowledge capabilities. Its use cases include software engineering, terminal tasks, document and presentation creation, legal analysis, electrical engineering, clinical reasoning, and other professional knowledge work. SpaceXAI reports benchmark gains over Grok 4.6 across coding, terminal, engineering, legal, and multi-hour office-task evaluations. Grok 4.7 includes a newly developed safeguard stack designed to strengthen jailbreak resistance and improve handling of risky cybersecurity, biological, and other dual-use requests. The company states that the model is designed to maintain strong utility for legitimate cybersecurity and research tasks while refusing more dangerous requests. Grok 4.7 is available through Grok Build, Cursor, the Grok API, coding harnesses, model routers, and supported cloud platforms, with a faster serving option also available. Pricing starts at $2 per million input tokens and $6 per million output tokens, positioning the model for developers and organizations running high-volume coding and professional AI workloads.

Pricing

Price Starts At:
$2 per 1M tokens (input)
Price Overview:
$2 per million input tokens and $6 per million output tokens

Integrations

Offers API?:
Yes, Grok 4.7 provides an API

Screenshots and Video

Grok 4.7 Screenshot 1

Company Facts

Company Name:
SpaceXSI
Date Founded:
2023
Company Location:
United States
Company Website:
grok.com

Product Details

Deployment
SaaS
Training Options
Documentation Hub
Support
Web-Based Support

Product Details

Target Company Sizes
Individual
1-10
11-50
51-200
201-500
501-1000
1001-5000
5001-10000
10001+
Target Organization Types
Mid Size Business
Small Business
Enterprise
Freelance
Nonprofit
Government
Startup
Supported Languages
English

Grok 4.7 Categories and Features

Grok 4.7 Customer Reviews

Write a Review
  • Reviewer Name: A Verified Reviewer
    Position: CTO
    Has used product for: Less than 6 months
    Uses the product: Daily
    Org Size (# of Employees): 100 - 499
    Feature Set
    Layout
    Ease Of Use
    Cost
    Customer Service
    Would you Recommend to Others?
    1 2 3 4 5 6 7 8 9 10

    Frontier level AI

    Date: Sep 21 2026
    Summary

    Overall, Grok 4.7 has become one of the more compelling models for serious coding and long-running agent work. It feels noticeably better at staying organized, checking its work, and finishing complex tasks without needing as much hand-holding.

    Positive

    The biggest improvement is how much better it is at sticking with longer tasks. It feels more deliberate than earlier Grok versions, especially when I am working through debugging, repo-level changes, or something that takes multiple rounds of planning and verification. The coding performance is strong too. xAI reports 46.3% on CursorBench 4.0 versus 40.4% for Grok 4.6, and Terminal-Bench 4.0 jumps from 20.3% to 38.0%. That is the kind of improvement that actually matters for agentic coding rather than just short code-generation prompts. I also like the pricing. At $2 per million input tokens and $6 per million output tokens, it is much easier to justify for daily agent workflows than some of the more expensive frontier models.

    Negative

    The downside is that the higher reasoning modes can use a lot of tokens, so I would not automatically run xhigh on every task. For quick edits or simple coding questions, a faster model can still make more sense.

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