# Grok 4.7's $2/$6 Price Leaves Out the Cost per Passing Patch **Summary:** Grok 4.7 keeps Grok 4.6's $2 input and $6 output rates while improving published coding scores. I would route production work only after measuring cost per accepted patch, retries included. - Canonical: https://markhuang.ai/news/grok-4-7-price-needs-accepted-patch - Language: en - Author: [Mark Huang](https://markhuang.ai/about) - Published: 2026-09-21 - Section: News - Tags: Grok 4.7, AI Coding, Coding Agents, Model Evaluation, AI Pricing - Source: [SpaceXAI](https://x.ai/news/grok-4-7) - License: https://creativecommons.org/licenses/by-nc/4.0/ --- ![A software factory sends luminous modules through a test chamber while rejected pieces return for another attempt](https://cdn.markhuang.ai/news/grok-4-7-price-needs-accepted-patch/hero.webp) *A low rate at the entrance helps. The economic result is decided after testing, retries, and acceptance.* [SpaceXAI's Grok 4.7 announcement](https://x.ai/news/grok-4-7) puts the model at $2 per million input tokens and $6 per million output tokens, the same rates as Grok 4.6. It also reports a jump from 40.4% to 46.3% on CursorBench 4.0 and from 20.3% to 38.0% on Terminal-Bench 4.0. This could be a very good price for long coding jobs, but the launch does not show the number I would use to route production work. I want cost per accepted patch, including the tokens spent on failed attempts, repair turns, tests, and review. The low token rate helps only when the model turns those tokens into work I can accept. That distinction carries more weight with Grok 4.7 because SpaceXAI says it trained the model on harder problems weighted toward tasks that take many hours. Longer runs magnify small differences in retry rate and verbosity. They also reward a model that can finish without intervention. ## The improvement is real inside SpaceXAI's test frame The release describes a larger base model, a longer reinforcement-learning run, better self-verification, and better management of long context. SpaceXAI also says Grok 4.7 was trained to understand its Grok Bot harness natively. This model is meant to keep working inside an agent loop rather than stop at a short coding answer. Beyond the two coding scores, SpaceXAI reports gains over Grok 4.6 on office work, legal work, clinical reasoning, and electrical engineering. The model is available through Cursor, Grok Build, the Grok API, third-party coding harnesses, routers, and cloud platforms. A fast variant doubles output speed and price. I would still label these provider-reported results. The comparison table pits Grok 4.7 at `xhigh` effort against Grok 4.6 at `high`, so the version and reasoning budget change together. That does not erase the improvement, but it makes the size of the model-only gain harder to isolate. ## The rate card stops before the expensive part At $2 in and $6 out, Grok 4.7 undercuts the comparison models in SpaceXAI's own table. Yet token prices cannot tell me whether one repository change costs less. The model might use more reasoning tokens, produce longer output, call more tools, or need another pass after tests fail. It might also avoid those costs by solving the job correctly on the first try. The rate card cannot distinguish the two outcomes. Early independent data reinforces the need to count full tasks. [Artificial Analysis measured Grok 4.7 high at 46 on its Intelligence Index](https://artificialanalysis.ai/models/grok-4-7-high). Its page says the run generated 200 million output tokens, compared with a 92 million median among the comparison set. At the time I inspected it, the [release page](https://artificialanalysis.ai/models/releases/grok-4-7) still left output speed and per-task cost blank. That is an early measurement, not a verdict, but it shows why output volume belongs in the cost calculation. Early public discussion is focused on this missing context. In [an r/cursor thread](https://www.reddit.com/r/cursor/comments/1wmgn3m/introducing_grok_47/), people questioned the mixed effort settings and asked how the model performs inside Cursor. In [a separate benchmark thread](https://www.reddit.com/r/singularity/comments/1wmh9rg/grok_47_benchmarks/), the positive case centered on price, while one skeptical comment noted that price without token efficiency says little. These are first-day reactions, so I treat them as questions rather than evidence of quality. > **Info:** > > For a coding agent, I would track total input and output tokens, elapsed time, tool calls, test passes, repair turns, and human review minutes. The denominator is accepted changes, not model calls. ## A fair trial needs the whole agent loop My trial would put Grok 4.7 against Grok 4.6 and one current alternative on a frozen set of real repository tasks. Each task needs an objective finish line: tests pass, the requested behavior exists, unrelated behavior remains intact, and a reviewer accepts the change. Every task also needs several runs because one lucky completion can hide an unstable workflow. The harness must stay fixed too. SpaceXAI trained Grok 4.7 to understand Grok Bot, while Cursor wraps the model in its own prompts, tools, context selection, and execution loop. A model can look better because its harness feeds it better evidence or lets it recover from errors. That is still useful product performance, but it should not be mistaken for a property of the model alone. This follows the purchasing mistake I examined in [my earlier analysis of token pricing](https://markhuang.ai/news/token-price-is-not-the-bill). Native token rates are inputs to a budget, not the final bill. Grok 4.7 puts more pressure on that distinction because its advertised advantage combines a low rate with longer autonomous work. ## The safety numbers need their own acceptance test SpaceXAI says Grok 4.7 has a new safeguard stack. The announcement reports a 62.4% result on LatchBio's biosafety benchmark and says the model allowed 3.3% of risky dual-use prompts through on HackerBench v0.3 while rarely refusing legitimate security work. Those are specific claims, but the announcement does not give enough linked methodology for me to translate them into a deployment policy. I would keep security-sensitive coding behind normal controls regardless of the score: least-privilege credentials, isolated execution, review for dangerous changes, and logs that make the agent's actions reconstructable. A better refusal benchmark can reduce risk. It cannot replace the boundary around the model. ## What would make the price convincing Grok 4.7 does not need to win every benchmark to be useful. If it delivers more accepted changes than Grok 4.6 at the same $2/$6 rates, the economics improve even when another model posts a higher headline score. The Terminal-Bench gain is large enough to justify a controlled trial. I would start with a reversible queue of well-tested issues and cap both spend and repair attempts. After enough runs, the useful comparison is mundane: accepted tasks per dollar, time to acceptance, and how often a human has to rescue the loop. If Grok 4.7 wins there, its price is a product advantage. For now, the $2/$6 rate has earned a trial, not an automatic place in the production router.