Grok 4.7 vs GPT-6 Astra: Which AI Wins Your Task?

Introduction

Three new AI models claim to be the best. Grok 4.7. GPT-6 Astra. DeepSeek V4.1 Flash. But a score on a chart does not tell you which one to open for your code, your hard problems, or your writing. So we will turn the published numbers into one clear answer for each job.

You're watching Latent Layer. AI, explained simply — new tools, new research, how it works. By the end, you will know how to read AI benchmark scores and pick the right model for each job.

Context

Here is who is who. GPT-6 Astra is the new model from OpenAI. Grok 4.7 comes from xAI, built on a new, larger base model than Grok 4.6. DeepSeek V4.1 Flash is a cheaper variant, and it appears in one key comparison. We will use results published by the makers, and by Artificial Analysis, a benchmarking site.

First, one idea. A benchmark is a fixed set of test questions. Every model gets the same test, and a program marks the answers. That makes scores comparable. But each test measures one narrow skill. So the useful question is not which model scored highest. It is which test looks like the job you actually do.

How it works

Let us read the scores one skill at a time. We start with reasoning, then coding-style work, then computer use. After that, we look at speed and cost. Each step shows one way a chart can mislead you, and one way to read it properly.

Start with reasoning. GPT-6 Astra scores ninety-eight percent on FrontierMath Tier four, a hard maths test, according to OpenAI. OpenAI describes that as saturated: ceiling reached. In our view, near full marks cannot separate strong models any more. And our sources show no matching scores for Grok 4.7, so no head-to-head exists yet.

Now coding-style work. Terminal-Bench version four gives a model a command line and a job to finish. In my reading, that is close to what a coding assistant does. Astra scores fifty-nine percent. Claude Fable 5.1 scores fifty-two. DeepSeek V4.1 Flash scores twenty-seven, just ahead of Grok 4.7 at twenty-six. One report rounds Astra to sixty. The gap is wide either way.

Next, computer use. This means a model clicking through apps for you, such as filling in a form. On OSWorld two point zero, Astra scores seventy-two point six percent, in about forty minutes per task. Its predecessor, GPT-5.6 Sol, scores sixty-five point seven percent in about seventy-five minutes. That is higher accuracy in about forty-seven percent less time. Speed and accuracy improved together.

Now, how was Grok 4.7 built? It starts from a new, larger base model. Then comes reinforcement learning. The model attempts tasks, gets scored, and adjusts. Grok's run was longer, on a harder mix of tasks, weighted toward problems that take many hours to complete. The aim is long, hard jobs. The published scores show how far that aim still has to go.

Why can a fast-starting AI still feel slow? Because speed has two parts. Time to first token is the wait before words appear. For Grok 4.7 that is point nine zero seconds, which is quick. Then comes output speed. Grok 4.7 writes forty tokens per second. A token is a small piece of a word. Artificial Analysis calls that notably slow.

Also, Grok 4.7 is talkative. Running the same Intelligence Index, it produced two hundred forty million tokens. The median model produced eighty-eight million. Talkative models take longer, and can cost more per job. GPT-6 Astra goes the other way. At maximum effort it uses twenty-seven thousand output tokens per task, about a third of the seventy-eight thousand used by Claude Fable 5.1.

Now cost, which is easy to misread. List prices are per million tokens. Grok 4.7 costs two dollars for input and six for output. Astra costs ten and fifty. But you pay for every token used, and Grok uses many. On the same index, a task costs three dollars seventy-four with Grok, and three dollars twenty-six with Astra. Claude Fable 5.1 costs seven dollars sixty-three. These were measured on different dates, so treat them as a guide.

What it means

Now the practical part. Here is what these numbers change for you. We sort by job: coding, hard reasoning, computer tasks, and lighter everyday work. This is my reading of the published scores, so treat it as opinion, not fact. Your own tests always come first.

Coding help: choose GPT-6 Astra. It leads Terminal-Bench by a wide margin. Hard reasoning: Astra again, since it is the only one with published scores in our sources. Clicking through apps: Astra, faster and more accurate than its predecessor. For light, cheaper jobs, test DeepSeek V4.1 Flash first. It edges past Grok 4.7 on Terminal-Bench, and it is the cheaper variant.

One number sums up the overall gap. On the Artificial Analysis Intelligence Index, GPT-6 Astra scores fifty-three, level with Claude Fable 5.1. Grok 4.7 scores forty-six. On coding-style tests, Grok 4.7 and DeepSeek Flash lag far behind. But Grok 4.7 has a context window of five hundred thousand tokens, room for very long documents. No published test shows how well it uses that space.

What about writing? Emails, essays, stories. Here the numbers go quiet. No independent source we found publishes writing rankings yet. Run the same prompt on each model and judge yourself. That is your data. Use a real task, the same instructions every time, and compare the answers side by side.

The other side

One number deserves a pause. GPT-6 Astra's hallucination rate fell from ninety-two percent to fifty-one percent at maximum effort. That is roughly one in two. Better than before, but check facts that matter. A hallucination is a confident answer that is wrong.

One more limit. On GDPval-AA version two, a test of professional knowledge work, Astra drops about forty-five Elo points against GPT-5.6 Sol. Elo is a rating built from head-to-head comparisons, as in chess. In professional knowledge tasks like legal writing, a forty-five point drop means Astra may not be better. Test your domain. Also, only the Flash variant of DeepSeek is covered here.

Takeaway

Here is the whole method. Read benchmarks right. Check the source. Find the ceiling. Pick your tool. A score tells you where to start testing. Your own task tells you where to finish. More plain guides to new AI tools are waiting on this channel.