6.1 Sol vs 6 Astra: choosing a model for creative planning

For 6.1 Sol vs 6 Astra, begin with the job you need finished. OpenAI positions GPT-6.1 Sol as a lower-cost choice approaching Astra's performance on complex work, and recommends comparing them on your own tasks. For a creator, those tasks might be a campaign brief, a critique of a reference image or a prompt that must preserve several product details.

Compare accepted results and revision time alongside the price. The test below is a suggested method; Yix has not benchmarked either model or measured its latency for this guide.

Documented differences and shared capabilities

The table uses the official GPT-6.1 Sol model reference and GPT-6 Astra model reference, checked October 8, 2026.

These are standard short-context text rates, not a quote for a complete agent or image-generation job. Requests above 272K input tokens carry different rates for the full request. Caching, processing mode, tools and other applicable charges also affect the bill; check the linked references for your setup.

What the price difference means for one brief

Consider a hypothetical request billed for 10,000 uncached input tokens and 2,000 total output tokens at standard rates. Sol's text cost is $0.02 for input plus $0.02 for output, or $0.04. Astra's is $0.10 plus $0.10, or $0.20.

This is arithmetic, not a measurement. It assumes the same billed token counts and excludes tools and other charges. In actual reasoning requests, include all billed output tokens, including reasoning tokens, rather than only the visible answer. A different effort setting can change usage substantially.

The fivefold difference in those uncached text rates does not guarantee fivefold savings per finished brief. Compare the cost of all attempts divided by the number of accepted results. Record editing time separately, especially when an answer needs someone to check facts or rewrite a complicated layout instruction.

When to evaluate Sol first

For routine creative planning, start your comparison with tasks whose correctness is easy to inspect: converting notes into a fixed brief, producing several headline options or extracting visual constraints from a reference. Sol's documented lower rates make it a reasonable candidate to evaluate for these jobs; they do not establish its acceptance rate for your brand.

Give the model a defined output format and facts it may use. For example, require separate sections for subject, composition, lighting, text and exclusions. A clear format reduces the time spent locating a missing requirement, regardless of which model you select.

For bulk work, measure whether the same instruction survives different products. A successful brief for a candle does not prove the model handles transparent glass, reflective metal or a product with several mandatory warnings equally well.

When to compare Astra on the same task

Include Astra when the brief involves competing requirements or when a wrong interpretation causes expensive rework. Examples include several linked campaign assets, a dense source document with important exceptions, or a composition that must satisfy both brand and production constraints.

Use the same acceptance sheet as Sol. Ask whether the extra cost reduces errors or review time enough to matter. Do not automatically send every long prompt to the more expensive model; remove irrelevant material first and test whether the remaining brief already meets your quality target.

OpenAI's current-model guide explains the intended roles of each model. Use your own briefs to check whether those roles fit the work you repeat.

Image understanding and image generation are separate steps

Both model references list image input. That supports a planning task in which you provide a photograph and ask for a description or composition critique. You still need to inspect whether the model identified the relevant material, camera angle or label correctly.

Both also support an image-generation tool in the Responses API. Native image input and calling an image tool are different capabilities: the planning model can help direct a tool, while the generated image needs its own quality and cost checks. Do not treat the text-token table above as the price of that image.

For an existing photograph, keep a written preservation list: identity, product shape, text, pose or background. A capable planner can write a useful edit instruction without guaranteeing that the subsequent image model preserves each detail.

A ten-brief comparison sheet

Select ten briefs you are allowed to reuse. Include straightforward prompts, briefs with several restrictions and examples with conflicting instructions. Decide in advance which conflicts should trigger a clarifying question. Remove identifying or confidential information that the task does not need.

Use identical inputs, tool access and output requirements. Record effort separately; the same setting name is not evidence of equal compute or elapsed time. Randomize the output labels before review if you want to reduce preference for a model's name.

An original test instruction is: “Plan a 4:5 advertisement for a cobalt-blue glass bottle on pale stone. Keep the white label blank, leave the upper quarter for a headline and use left-side window light. Return one image prompt and a checklist. If any requested detail conflicts, state the conflict before proposing a change.” This example has not been run on either model for this guide.

Put the chosen brief to work

Use Yix's prompt generator to organize a subject, scene, camera and constraints into a working prompt. It is a Yix prompt-preparation tool, not a selector for Sol or Astra. Save the same brief for comparisons in the services where you use those models.

Then open image editing for a source-photo change, or use the product-photography guide to develop a new scene. Judge the final image separately from the planning text. For interpreting another model's published scores, see Haiku 5.5 benchmark.

Common questions

Is Sol always the better value?

The documented standard rates are lower. Whether it is better value depends on accepted outputs, retries and human review time for your task.

Can a larger context window replace a clear brief?

No. More input can include more irrelevant or contradictory instructions. Keep essential facts and restrictions easy to identify, even when the model can accept a much longer request.

What should decide the winner?

Set a quality threshold before testing, then compare cost per accepted brief and review time. If both meet the threshold, the cheaper workflow may be sufficient. If one repeatedly misses an important constraint, that failure should remain visible in the decision.