SpaceXAI released Grok 4.7 on the xAI API on September 21, targeting coding, agent tasks and knowledge work. The model identifier is grok-4.7. For a team considering a switch, the useful comparison is the cost of a verified result, including the attempts and tool calls required to get there. Official release notes
A model upgrade can change how much work an agent attempts, how long it reasons and how often it recovers from mistakes. Matching token prices alone would not settle those questions.
What the API release specifies
The release notes list a 500K context window, text and image inputs, and text output. Reasoning effort supports low, medium, high and xhigh, with high as the default. They also describe Grok 4.7 Fast as available through Cursor and Grok Build, rather than the public xAI API. API release details
Use the exact product surface when planning a comparison. A setting available in a coding application is not automatically a parameter or model slug you can use in a direct API integration.
Read the price threshold with the rate
The September 21 API notes give these rates per million tokens:
| Prompt size | Input | Cached input | Output |
|---|---|---|---|
| Below 200K tokens | $2 | $0.50 | $6 |
| Above 200K tokens | $4 | $1 | $12 |
The same notes describe Fast as charging twice the token rates. Confirm the current billing rules for the surface you use, including treatment at the exact threshold, before relying on an estimate. Published rates
A useful migration trial records total input, cache usage, output and task completion for each run. Include failed attempts. If one configuration needs more retries or human cleanup, its lower apparent request cost may not survive the full comparison.
Check response handling before the workload
The release notes flag one integration detail: on the Responses API, Grok 4.7 returns reasoning.encrypted_content even when it was not requested in include. Audit strict response parsers and logging around that behavior. Response change
Then replay a small set of representative tasks against the current model and the candidate. Keep tool permissions, repository state and acceptance checks consistent. Compare reasoning settings explicitly instead of assuming their defaults represent equal effort.
For coding, the result should include tests and a review of the actual change. For research, it should include source checks. For an external action, verify authorization and the destination before execution. These are evaluation criteria, not claims that Grok 4.7 passes them.
S5 Labs has not benchmarked this release. We would use it as a candidate in a controlled workload comparison before changing a default agent. The prompt engineering guide explains why prompt and model settings should be evaluated together.