GPT-6.1 Sol vs GPT-6 Astra: which model should you choose?

What actually differs between Sol and Astra?

OpenAI positions GPT-6.1 Sol as an option for complex work with performance approaching Astra at a lower cost, and Astra as its most capable option. Those are the developer’s descriptions. They help frame a trial but do not establish a winner for your own documents or code.

Both list the same context and maximum output sizes. A context window describes capacity, not a guarantee that every fact will be recalled correctly. Put important constraints in the brief and check the answer against the source.

OpenAI: GPT-6.1 Sol · OpenAI: GPT-6 Astra

Writing: compare the editing you still need to do

For email and everyday writing, give each model the same facts, recipient and desired tone. A polished answer is not enough: it must preserve names, dates and commitments. Prefer the draft that needs fewer corrections to become something you would actually send.

Use the example below as a small controlled task. A successful answer keeps Tuesday provisional, preserves the £480 amount and asks for approval. It must not turn a suggested date into a confirmed booking. No model responses are presented here.

Write a concise email to a client using only these facts: the proposed workshop is Tuesday; the date is not confirmed; the agreed quote is £480; we need written approval before booking. Ask for approval in a friendly, professional tone. Include a subject line. Do not add a deadline or promise availability.

Coding: judge the fix and its checks together

Choose a contained bug before comparing models on a large project. Provide the same code, expected behaviour and constraints. Check that the proposed change fixes the problem without introducing another one. A claim that tests pass only counts if the tests were actually executed.

This example has a known issue: sorted() returns a new list, while the function returns the original list. A useful answer should explain that distinction, return sorted values and preserve the caller’s list. Run any proposed code in your own test environment.

Review this Python function:

def ordered_copy(values):
    sorted(values)
    return values

It should return ascending values without modifying the input list. Explain the bug, propose the smallest fix and provide tests for duplicates, an empty list and input preservation. State whether you actually executed the tests.

Document analysis: test what the answer can prove

Use a document with an uncertain date or an unresolved decision. Ask both models to separate explicit facts, proposals and missing information. Check every claimed decision against the text rather than judging the length or confidence of the answer.

In the fictional notes below, 12 November is a proposal, not an approved launch. The budget is a ceiling, not actual spending. These distinctions make a short test useful even before you compare longer documents.

Use only these fictional meeting notes: “Mina proposed launching on 12 November. Finance has not approved the plan. The budget ceiling is 8,000 units. Leo will prepare a draft; no deadline was agreed.” Return a table of confirmed facts, proposals and missing decisions, with a supporting quote for every row. Do not infer approval, actual spending or a deadline.

Do Sol and Astra generate images?

Their model pages list image input and text output. Understanding an uploaded image is different from generating a new one. Image generation is listed separately as a supported tool in the Responses API; an application must provide the corresponding workflow.

For a photo task, first decide whether you need an explanation of an image or a new edited picture. Then check the available feature in the product you are using. A model name alone does not establish the app’s editing capabilities.

How to make your final choice

Pick three tasks you perform regularly and define a pass condition for each before running either model. Keep the prompt, source material, tools and settings consistent. Start separate sessions and record the exact model version and date.

For each run, record whether the result passed, how long it took and how many corrections you made. Repeat the tasks before drawing conclusions. Choose based on accepted results in your workflow; do not turn a small personal trial into a universal ranking.

The prompts above can also help you evaluate other models. In GO AI, choose from the models actually shown in the selector. This article does not unlock Sol or Astra or confirm their availability there.

Common questions

Sources and scope

• OpenAI: GPT-6.1 Sol
• OpenAI: GPT-6 Astra