Grok 4.6 vs GPT-5.6 Sol

Update: OpenAI has since released GPT-6 Astra, a new frontier model that sits above GPT-5.6 Sol in OpenAI’s own lineup. The comparison below reflects the two models as released and is still useful for understanding GPT-5.6 Sol’s positioning and pricing, but Sol is no longer OpenAI’s top model.

Quick Answer

Grok 4.6 and GPT-5.6 Sol are close competitors on paper, tied at 61 on the Artificial Analysis Intelligence Index, an independent benchmark, and both explicitly built for long-running agentic work rather than just single-turn chat. Grok 4.6 is cheaper per token and has broader third-party access (Cursor, OpenRouter, Vercel, Cloudflare); GPT-5.6 Sol has a larger context window and OpenAI’s much larger existing ecosystem. Neither benchmark tie nor lower list price settles which one actually costs less to get a real, multi-step job done.

At a Glance

What Is Grok 4.6?

Grok 4.6 is xAI’s flagship model, released August 12, 2026, built specifically for tasks that stay open across many steps: researching a topic, working across a codebase, or turning an idea into a finished artifact. It offers configurable reasoning effort (low through xhigh) and is available through a notably broad set of third-party channels, including Cursor, OpenRouter, Vercel, and Cloudflare, alongside xAI’s own API and Grok Build.

What Is GPT-5.6 Sol?

GPT-5.6 Sol is the flagship tier of OpenAI’s GPT-5.6 family, alongside the lower-cost Terra and fastest Luna. It reached general availability across ChatGPT, Codex, and the API on July 9, 2026, after a restricted preview that began June 26, gated in part by a High cybersecurity capability rating in OpenAI’s own system card. It’s positioned for complex reasoning, coding, and long-horizon, multi-step problem solving, with particular strength documented in command-line and multi-step coding tasks. See the full ChatGPT guide for how Sol fits alongside Terra and Luna.

A Benchmark Tie Isn’t the Whole Story

Both models scored 61 on the Artificial Analysis Intelligence Index, an independent third-party benchmark, at the time of Grok 4.6’s release. That tie is a genuinely useful data point, it’s not vendor-reported, but a single aggregate score doesn’t capture how a model performs across the many different sub-tasks a real agent workflow touches: planning, tool use, code execution, error recovery, and staying coherent across a long session. Don’t treat the tie as proof the two models are interchangeable for your specific workflow.

Context Window and Reasoning Effort

GPT-5.6 Sol’s 1.1M-token context window gives it more headroom than Grok 4.6’s 500K for tasks that need to hold an unusually large amount of context at once, a very large codebase or a long document set, for example. Both models support configurable reasoning effort, letting you dial up deeper reasoning for hard steps and dial it down for routine ones, which matters directly for effort routing within a single agent workflow rather than switching models entirely.

Number of Turns, Retries, and the Real Cost of Finishing a Task

This is where per-token pricing stops being the useful number. An agent working through a long task doesn’t make one call, it makes many: planning steps, tool calls, verification checks, and retries when something doesn’t work the first time. A few things determine what a task actually costs to finish, regardless of which model you’re using:

• Number of turns. More steps means more calls, and cost scales with call count as much as with per-call price.
• Retries. A model that needs to redo a step effectively pays for that step more than once.
• Context growth. As an agent’s history grows across a long session, later calls carry more context and cost more, even at a fixed per-token price.
• Finished-work cost, not attempt cost. Cost per completed task is the number that matters: total spend across every attempt needed to get one accepted result, not the listed price of a single call.

Grok 4.6’s lower input price gives it a real head start on cost per attempt. Whether that translates into a lower cost per completed task depends on how many turns and retries each model needs on your specific workload, something neither vendor’s pricing page can tell you.

Access and Ecosystem

Grok 4.6 is notably available through a wider set of third-party platforms at launch, Cursor, Grok Build, OpenRouter, Vercel, and Cloudflare, alongside xAI’s own API. GPT-5.6 Sol’s access runs through OpenAI’s own surfaces, ChatGPT, Codex, and the API, which benefit from OpenAI’s much larger existing user base and integration ecosystem, but with fewer third-party access points at this stage.

Which Should You Choose?

Choose Grok 4.6 if per-token cost and broad third-party platform access (especially inside tools like Cursor) matter most, or if you’re already working in xAI’s or a partner platform’s ecosystem. Choose GPT-5.6 Sol if you need the larger context window, you’re already working inside ChatGPT or Codex, or you want the backing of OpenAI’s broader ecosystem and tooling. For agent-heavy workloads specifically, run your actual multi-step task on both and compare total cost to completion, not just the headline benchmark tie or per-token price.

Keep Exploring

• Tools: Grok, ChatGPT
• Related: Grok 4.6 vs Claude Fable 5, Grok vs ChatGPT
• Glossary: Cost per Completed Task, Effort Routing, Long-Running Agent
• Guide: How to Estimate an AI Agent’s Cost