Grok 4.7 vs GPT-6 Astra: Full Comparison

Grok 4.7 and GPT-6 Astra arrived within weeks of each other, and both are aimed at work that goes far beyond simple chat.

Grok 4.7 with xAI positioning it around coding, long-running agent tasks, and professional knowledge work. GPT-6 Astra launched earlier in September as OpenAI's flagship for complex reasoning, software engineering, computer use, research, and end-to-end professional workflows.

That overlap makes the comparison useful—but the two models take noticeably different approaches.

If you want the full specifications and benchmark results for xAI's new model first, see our Grok 4.7 review.

Grok 4.7 vs GPT-6 Astra at a Glance

Grok 4.7's official API documentation lists a 500K context window, four reasoning levels, function calling, web search, X search, and code execution. GPT-6 Astra supports a 1.05M context window, up to 128K output tokens, five reasoning levels, function calling, web search, file search, and computer use.

Coding: Both Are Built for Agentic Work

Coding is one of the clearest areas of overlap.

xAI trained Grok 4.7 on a harder mix of long-running tasks and says the model is better at managing context and checking its own work. On xAI's evaluations, Grok 4.7 reached 46.3% on CursorBench 4.0 and 71.0% on DeepSWE v1.1.

OpenAI describes GPT-6 Astra as its strongest software engineering model to date. In OpenAI's evaluations, Astra scored 57.9% on Terminal-Bench 4.0, 74.1% on DeepSWE v1.1, and 64.5 on FrontierCode 1.1 Extended.

The important caveat is that vendor benchmark settings can differ. Scores published by xAI and OpenAI should not automatically be treated as a perfectly controlled head-to-head test.

For real projects, the more useful comparison is often whether the model can inspect a codebase, use tools, run tests, catch its own mistakes, and stay consistent across a long session.

Context: 500K vs 1.05M Tokens

Context is one of the easiest differences to quantify.

Grok 4.7 supports 500,000 tokens.

GPT-6 Astra supports 1,050,000 tokens.

A larger context window can matter when working with very large repositories, many documents, long conversations, or multi-stage agent sessions.

But context size alone does not tell you how effectively a model uses that information. Long workflows also depend on retrieval, memory management, compaction, tool use, and the model's ability to recover details from earlier steps.

OpenAI has introduced additional context-preservation features for Astra in Codex, while xAI specifically highlights improved long-context management in Grok 4.7.

Agents and Tool Use

Both models are clearly being designed around AI agents rather than isolated prompts.

Grok 4.7 supports function calling, web search, X search, and code execution through xAI's platform. xAI also trained the model to work more naturally with its Grok Bot harness.

GPT-6 Astra supports function calling, web search, file search, and computer use. OpenAI places particularly heavy emphasis on Astra's ability to work across browsers, code, and professional software.

That creates a practical distinction.

Grok has strong integration with xAI's search ecosystem and coding workflows, while Astra puts more emphasis on general computer use and end-to-end software interaction.

API Pricing Is Very Different

The pricing gap is substantial.

For shorter prompts, Grok 4.7 starts at $2 per million input tokens and $6 per million output tokens.

GPT-6 Astra's standard API pricing is $10 per million input tokens and $50 per million output tokens.

That does not mean Grok will always produce a lower total cost per completed task. A more expensive model can sometimes finish a task with fewer attempts, fewer tokens, or less human correction.

Still, for teams running high-volume workloads, the per-token difference is large enough to matter.

Research and Knowledge Work

Neither model is only for programmers.

Grok 4.7 is explicitly positioned for professional knowledge work and shows improvements on benchmarks involving office tasks, legal work, healthcare reasoning, and electrical engineering.

GPT-6 Astra is designed for research, science, document creation, browsing, and professional software workflows. OpenAI reports strong results across academic, professional, science, computer-use, and coding evaluations.

For research-heavy workflows, the practical test should include source discovery, long-document understanding, factual verification, and how well the model preserves evidence across several steps.

Which One Fits Your Workflow?

The answer depends less on a single leaderboard number and more on what you actually need.

A useful evaluation is to give both models the same repository, research question, or multi-document task and compare the result from start to finish.

If Claude is also on your shortlist, see our Grok 4.7 vs Claude Fable 5.1 comparison.

Grok 4.7 and GPT-6 Astra represent two similar trends in frontier AI: longer tasks, deeper reasoning, more tool use, and less emphasis on one-shot chatbot answers.

Grok 4.7 stands out for its much lower starting API token price and xAI-centered search and coding ecosystem.

GPT-6 Astra offers roughly twice the context window and puts more emphasis on computer use, browsing, complex reasoning, and end-to-end professional workflows.

For benchmark-heavy comparisons, the numbers are useful. For an actual buying or development decision, testing the models on your own task is much more informative.

Already decided to try xAI's model? Our guide to using Grok 4.7 covers the API, Cursor, Grok Build, reasoning levels, and practical use cases.