Short answer
For a new production deployment, don’t choose between Grok Code Fast 1 and GPT-5 as though both were current, unchanged options. As of November 10, 2026, xAI’s retirement notice says Grok Code Fast 1 was retired from its API on May 15, 2026, and names Grok Build 0.1 as its replacement. OpenAI’s GPT-5 model page labels GPT-5 deprecated and recommends a newer model. Treat the older comparison as historical context, not a current model-selection verdict. (xAI retirement notice; OpenAI GPT-5 model page)
If you maintain an existing integration, confirm through the provider’s migration documentation which model serves requests using your existing identifier. Don’t assume that a retired identifier still pins the same model, behavior, or price. For a new xAI integration, evaluate the documented successor rather than assuming the old model’s characteristics carry over. (xAI retirement notice)
What the API documentation says now
| Model or API identifier | Documented status | Pricing information for selection | What that means |
|---|---|---|---|
grok-code-fast-1 |
xAI’s retirement notice lists it as retired on May 15, 2026, and names grok-build-0.1 as its replacement. |
The Grok Build 0.1 model page lists base rates of $1.00 per million input tokens, $0.20 per million cached input tokens, and $2.00 per million output tokens. For prompts that reach 200K tokens, xAI’s pricing page lists long-context rates of $2.00 input, $0.40 cached input, and $4.00 output per million tokens; those rates apply to all tokens in the request. | Use the successor’s documented identifier for evaluation, and confirm the applicable pricing tier for your prompt. Don’t assume a retired identifier still serves the same model. (xAI retirement notice; Grok Build 0.1 model page; xAI API pricing) |
gpt-5 |
OpenAI’s model page labels GPT-5 deprecated and recommends a newer model. | Confirm current input, cached-input, and output rates on OpenAI’s model page before budgeting; this comparison does not treat a GPT-5 rate as confirmed. | Its status and migration implications matter more than a historical price comparison when planning a new deployment. (OpenAI GPT-5 model page) |
These are API pricing details, not IDE or ChatGPT subscription prices. Token rates alone do not establish cost per completed coding task: task success, output volume, caching eligibility, and long-context pricing can affect the bill. (Grok Build 0.1 model page; xAI API pricing)
A small token-cost example
For 10,000 input tokens and 1,000 output tokens, using the listed Grok Build 0.1 global base rates and a prompt below 200K tokens:
- Input: 10,000 × $1.00 per million = $0.01.
- Output: 1,000 × $2.00 per million = $0.002.
- Total: $0.012 at those rates, before any other applicable charges. The listed US regional endpoint rate is 1.1× the global token rates.
This is arithmetic from the listed base rates, not a measured bill or a comparison of completed coding tasks. At a prompt of 200K tokens or more, the long-context rates apply to all tokens in the request; confirm the applicable tier and endpoint rate before budgeting. This example does not include cached input; check eligibility and the applicable rate before estimating cached usage. (xAI API pricing; Grok Build 0.1 model page)
What older Grok Code Fast 1 vs GPT-5 claims can tell you
A 2025 DEV Community comparison described Grok Code Fast 1 as a fit for rapid prototyping and repetitive coding, and GPT-5 High as a fit for complex reasoning and architecture. It also reported SWE-Bench Verified scores of 70.8% for Grok and 74.9% for GPT-5 High, plus context figures of 256K and 400K tokens. Those are claims from that article, not verified like-for-like results: it provides no benchmark configuration, test protocol, or supporting source links. Its GPT-5 High comparison should not be assumed to describe every GPT-5 API setting. (DEV Community comparison)
Anecdotal reports from a Cursor forum discussion are weaker evidence for a general model choice: one poster preferred “Grok Code” for perceived speed and concise replies, but did not identify the exact model version or run a controlled comparison. That experience may suggest something to test; it does not establish that Grok Code Fast 1 beats GPT-5 in API latency, coding quality, or total cost. (Cursor forum discussion)
Practical reading: the older reports suggest hypotheses—fast iteration may matter for small coding changes, while more deliberate reasoning may help on difficult multi-step work. They do not establish a current winner, particularly now that the relevant API models and statuses have changed.
Which should you choose for production coding?
- Maintaining Grok Code Fast 1 calls? Check the provider’s migration documentation to confirm which model serves your requests and what you are billed. For a planned migration, evaluate
grok-build-0.1under that explicit identifier rather than assuming the old model’s behavior or price still applies. (xAI retirement notice) - Considering GPT-5 for a new system? OpenAI marks it deprecated and recommends a newer model. Choose a currently supported model deliberately, and include migration risk in the decision rather than relying on an old price or benchmark comparison. (OpenAI GPT-5 model page)
- Choosing between current coding options? The cited sources do not provide a controlled, current head-to-head API evaluation of Grok Build 0.1 and OpenAI’s recommended successor. Don’t infer a quality, speed, or cost-per-successful-task winner from the 2025 GPT-5 comparison or token prices alone.
A compact evaluation before switching
Run a small, repeatable test on tasks that resemble your own workload:
- Choose representative tickets—for example, a bug fix with tests, a multi-file change, and a code-review or refactor task.
- Record the exact API model identifiers, prompts, tools, repository context, and reasoning settings. Keep the setup as comparable as the APIs allow.
- Define success before running the test: tests pass, review criteria are met, or a human accepts the patch without specified classes of defects.
- For each task, record completion or acceptance, end-to-end time, input and output token usage, and billed API cost. Include retries and failed attempts.
- Pick a primary model only if its task results meet your quality, latency, and cost requirements. If different models perform better on different tasks, test a fallback arrangement rather than assuming it will help.
This is proposed evaluation guidance, not a benchmark result. It addresses the main gap in the older comparison: the cited material does not establish matched-task performance or cost per successful coding task.
Bottom line
Grok Code Fast 1 vs GPT-5 is now primarily a legacy-model comparison. xAI’s retirement notice names Grok Build 0.1 as the replacement for Grok Code Fast 1; OpenAI marks GPT-5 deprecated and recommends a newer model. Use historical comparisons only to form workload-specific hypotheses. For a production decision, select currently supported API model identifiers and compare them on your own coding tasks, including actual cost and migration behavior. (xAI retirement notice; OpenAI GPT-5 model page)
Sources
May 15, 2026 Model Retirement — xAI API
The Ultimate AI Coding Grok Code Fast 1 vs GPT-5 High vs Claude Sonnet 4 — DEV Community