GPT-6 vs GPT-6.1: Astra reasoning effort and model selection

For most coding and document work, start with GPT-6.1 Sol at medium effort; choose GPT-6 Astra when difficult reasoning and judgment justify more usage, and Luna for focused repeatable tasks. This guide uses OpenAI documentation checked on October 3, 2026, distinguishes API effort from Codex’s Ultra mode, and offers starting points to test rather than measured speed or accuracy rankings.

Recommendation: where to start with Astra, Sol, and Luna

Start with GPT-6.1 Sol at medium for a feature, document draft, or analysis you expect to revise. OpenAI positions it close to Astra for complex work at a lower cost than Astra. Choose Astra when the hardest part is resolving conflicting evidence, maintaining constraints across tools, or completing a demanding project. Choose Luna when the input, output, and acceptance check are clear. [1, 8]

The number alone can mislead: “GPT-6” names a family that includes Astra, Sol, and Luna. GPT-6.1 Sol updates the Sol branch. It does not mean every 6.1 result will exceed Astra, nor does “near-Astra” establish equal performance on your task. Compare the exact models you can select.

Decision criteria: separate model choice from reasoning effort

Model choice changes the model doing the work. Reasoning effort guides how much it thinks before answering. Higher effort usually increases latency and token use, while the model can still adapt to task difficulty. It does not prescribe a fixed thinking time or answer length. [6]

For Astra, the API lists low, medium, high, xhigh, and max. Codex calls low Light in some interfaces and adds Ultra as a mode with task delegation. [2, 7]

The situations are editorial starting points based on OpenAI’s guidance. High, Extra High, and Max are candidates to compare; their labels do not guarantee a better result on every input. [6, 8]

Max and Ultra solve different problems. Max gives the selected model more time on one task. Ultra introduces subagents for divisible work. A single difficult calculation may benefit from deeper reasoning; a project with independent modules may benefit from delegation. Ultra is a Codex/Work control, not a listed reasoning.effort: "ultra" value for the Astra API. It also differs from Ultrafast, which is a speed option. [2, 7]

Important differences: GPT-6 Sol versus GPT-6.1 Sol

Compare Sol with Sol before interpreting a generation change. The shared context and output limits do not establish equal reasoning quality; the practical migration differences include supported settings and tool calling. [3, 4]

The Astra and Luna roles come from their model pages; the two Sol rows describe the documented branch comparison. [2-5]

Three details matter when moving from GPT-6 Sol to GPT-6.1 Sol:

• Effort compatibility: 6.1 Sol supports neither none nor minimal. A workflow using none needs a supported setting such as low, followed by a fresh quality and latency comparison.
• Tool calling: use the Responses API with 6.1 Sol. Its Chat Completions support excludes tool calling. GPT-6 Sol allows Chat Completions function calling only at none.
• Cache economics: cached input is listed at 5% of the uncached input rate for 6.1 Sol, versus 10% for GPT-6 Sol. Those percentages use each model’s own base rate; they do not establish which complete task is cheaper. [3, 4]

Both Sol pages list a 1,050,000-token API context window and a 128,000-token maximum output. App context limits, default efforts, available controls, and plan access can differ from API specifications. Check the product you use before carrying an API assumption into Codex. [3, 4, 7]

Choose by your situation: practical model and effort starting points

These recommendations combine OpenAI’s model-selection guidance with the difficulty and review needs of each task. They are editorial judgments, not results of a new benchmark. [8]

For repeated work, judge cost per accepted result, including retries and correction time. A lower token rate can lose its advantage if you need several attempts. For a single urgent task, waiting time may matter more than a small usage difference.

A fair comparison keeps the input, sources, tools, permissions, and acceptance criteria the same. Record missing requirements, incorrect claims, elapsed time, and usage. Keep the least costly configuration that consistently meets the quality bar; an occasional task may justify Astra even when a frequent automation does not.

Limits and evidence: what these settings cannot promise

This article checks documentation; it does not report a controlled speed, accuracy, or credit-use test. OpenAI’s “near-Astra” description is a vendor claim, not a universal equivalence. No fixed percentage improvement from GPT-6 Sol to 6.1 Sol, or from High to Max, is established here.

Plan, client, rollout, and workspace settings determine what appears in your picker. API defaults and app defaults can differ. More effort also cannot retrieve a missing source, grant permission, or verify an external write without a result check. Treat a correct-looking answer and a completed action as separate outcomes.

For a familiar task, the useful next check is whether another setting removes a specific failure at an acceptable cost.

Sources

Official documentation checked on October 3, 2026:

1. OpenAI: using the GPT-6 family
2. OpenAI: GPT-6 Astra model
3. OpenAI: GPT-6.1 Sol model
4. OpenAI: GPT-6 Sol model
5. OpenAI: GPT-6 Luna model
6. OpenAI: reasoning effort
7. OpenAI: models and Max/Ultra in Codex and Work
8. OpenAI: model-selection guidance

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