Grok 4.7 API

Technical Specifications of Grok 4.7

Grok 4.7 is xAI's frontier model for coding, agentic tasks, and knowledge work. The official API model ID is grok-4.7. xAI documents a 500K-token context window, text and image inputs with text output, configurable reasoning effort, and support for both Responses API and Chat Completions.

What is Grok 4.7?

Grok 4.7 is a new xAI model released on September 21, 2026, positioned around long-running coding, agentic workflows, and professional knowledge work. xAI says it uses a larger base model than Grok 4.6 and a longer reinforcement-learning run focused on tasks that can take hours to complete. The model is also trained to understand the Grok Bot harness natively, with an emphasis on checking its own work and managing longer contexts.

For developers, the important change is not simply a higher benchmark score. Grok 4.7 combines a 500K context window, reasoning controls through reasoning_effort, tool support, and multimodal input in a model intended for extended agent loops.

Main Features of Grok 4.7

• 500K-token context: The large context window is designed for long repositories, extended agent sessions, large technical documents, and multi-step knowledge-work tasks.
• Configurable reasoning: Developers can select low, medium, high, or xhigh reasoning effort, with high as the default. This allows compute allocation to be adjusted according to task complexity.
• Coding and agentic optimization: xAI specifically positions Grok 4.7 for software engineering, agentic tasks, and long-running professional workflows.
• Multimodal input: The API accepts text and image inputs and produces text output, making the model applicable to screenshot analysis, visual debugging, and document-understanding workflows.
• Native tool support: The documented capability set includes function calling, web search, X search, and code execution.
• Long-horizon reliability focus: xAI reports improvements in self-verification and longer-context management compared with Grok 4.6, reflecting its emphasis on multi-hour tasks. citeturn0search0

Benchmark Performance of Grok 4.7

xAI's launch evaluation reports the following results. These are provider-reported benchmark measurements, so they should be interpreted in the context of each benchmark's methodology and configuration rather than as a universal ranking.

*The reported Grok 4.7 DeepSWE result uses high effort. xAI's table reports these measurements as part of its launch comparison.

The benchmark profile is task-dependent. Grok 4.7 shows substantial gains over Grok 4.6 on the reported coding, terminal, legal, clinical, and electrical-engineering evaluations, while the same table shows other frontier models ahead on selected benchmarks. This makes benchmark selection important when evaluating Grok 4.7 for a particular workload.

Grok 4.7 vs Grok 4.6 vs GPT-5.6 Sol

Grok 4.7 and Grok 4.6 share the same 500K context class and reasoning-effort options, but xAI reports improved results for Grok 4.7 on its launch benchmark suite. Compared with GPT-5.6 Sol, the practical distinction depends on the workload: xAI's published table shows different relative performance across coding, office work, terminal work, legal work, clinical reasoning, and electrical engineering.

Limitations and Implementation Considerations

Grok 4.7 has a large context window, but a larger context does not guarantee better results for every long-document or agent workflow. Long-running applications should still manage context deliberately, especially when conversations accumulate tool outputs. xAI recommends prompt-cache routing and context compaction for long agent loops.

The model also has no public Batch API support according to the current model documentation. Developers building batch-oriented workloads therefore need to use the supported synchronous API patterns or another deployment route.

Its benchmark results are provider-reported and vary by benchmark configuration and reasoning effort. They should be treated as task-specific evidence rather than a single universal measure of model quality.

Representative Use Cases

1. Repository-scale coding: Analyze large codebases, implement changes across multiple files, debug failures, and reason over long issue histories.
2. Agentic software engineering: Combine reasoning with function calling, code execution, and external tools for multi-step engineering workflows.
3. Technical document analysis: Process long specifications, architecture documents, research material, or project records within a 500K-token context.
4. Visual debugging: Analyze screenshots, diagrams, and other image inputs alongside textual engineering context.
5. Professional knowledge work: Draft, analyze, and revise documents or structured deliverables that require extended multi-step reasoning.
6. Research and information workflows: Use web search and X search tools where current external information is required and the application's tool permissions allow it.

How to Access Grok 4.7 API Through CometAPI

CometAPI currently has a dedicated Grok 4.7 model page with the model ID grok-4.7 and documented /v1/chat/completions and /v1/responses routes. The current CometAPI page confirms that Grok 4.7 is available through its API layer.

Step 1: Get a CometAPI API Key

Create or sign in to a CometAPI account and obtain an API key from the API console. CometAPI provides a unified API layer for accessing multiple model providers through one account.

Step 2: Select grok-4.7

Use grok-4.7 as the model ID and select the documented CometAPI route that matches the application. The current model page lists both /v1/chat/completions and /v1/responses. citeturn1search2

Step 3: Send and Process the Response

Send the user's prompt through the selected endpoint, then process the returned response in the same application workflow used for other CometAPI-supported language models. For production deployments, verify the live CometAPI model catalog and route documentation before pinning implementation details.