GPT-6.1 Sol: Choose a Model With a Test, Not a Name

Before you begin

No previous experience required unless you choose a coding exercise. Use an account you are allowed to access; features vary by product and region.

What you will learn

• Explain why a model name is not a buying decision.
• Identify input, output, tools, quality and budget requirements.
• Calculate a simple illustrative API cost.
• Compare Sol and Astra on the same representative task.

What changed on September 29

OpenAI's API changelog records the release of GPT-6.1 Sol on September 29, 2026. OpenAI positions it as a lower-cost model than GPT-6 Astra and recommends comparing both on the same task. The current model page lists text and image input, text output, a 1,050,000-token context window and a 128,000-token maximum output. Audio and video are not supported directly by this model page.

The same documentation says tool calling is available through the Responses API. Those are API details for developers, not a promise that every consumer app exposes identical controls. Model access, regional options, rate limits and prices can change, so recheck the official page before building or paying.

Sources: OpenAI API changelogOpenAI GPT-6.1 Sol model pageOpenAI model selection guide

Start with five requirements

Do not begin with 'Sol or Astra?' Begin with the job. Write five lines: input, output, tools, quality and budget. For example: input is a one-page club announcement; output is a 120-word beginner summary; no external tools are needed; every date and number must remain exact; the result should require no more than five minutes of review.

This checklist exposes mismatches early. A huge context window is irrelevant if your input is one page. A tool-enabled model is unnecessary if the task only rewrites supplied text. A cheaper response is not useful if it repeatedly drops dates and costs extra review time.

For high-stakes medical, legal, financial or safety decisions, a model comparison is not a substitute for a qualified professional or authoritative source. The exercise below uses harmless fictional text and requires no account or API key.

Understand the listed price with a small example

At the October 1 check, OpenAI lists standard GPT-6.1 Sol pricing for prompts up to 272,000 tokens as $2 per million input tokens and $10 per million output tokens. Cached input and cache writing have separate prices. Prompts above that threshold use higher rates, and tool use or other services may add costs.

Suppose one API request uses 10,000 uncached input tokens and produces 2,000 output tokens. The illustrative input cost is 10,000 divided by 1,000,000, then multiplied by $2: $0.02. The output cost is 2,000 divided by 1,000,000, then multiplied by $10: $0.02. Together, that is $0.04 under those simplified assumptions.

This is arithmetic, not a quote or bill estimate for your project. It excludes cache writes, tools, taxes, discounts, failed retries and longer-context rates. Consumer subscriptions are also different from API usage pricing. Record the assumptions next to every estimate so a reader knows what is missing.

Sources: OpenAI GPT-6.1 Sol model page

Compare quality before scaling

OpenAI's model-selection guide recommends evaluating models on your own tasks. Use three representative examples, not a single favorite prompt. Hide the model name while scoring if possible. Check required facts, instruction following, clarity and review time. Record failures, not just attractive outputs.

Choose the least expensive option that consistently clears your quality threshold and meets your speed needs. If Sol fails an important case that Astra passes, document that case. If both pass, the lower-cost option may be sensible. If neither passes, improve the task design or use a different workflow instead of forcing a winner.

A model comparison expires. Re-run it after major model, prompt, dataset or product changes. Save the date and exact model identifier. Labels such as 'latest' can move over time, while an exact identifier makes your notes more reproducible.

Sources: OpenAI model selection guide

Terms and next step

Token: a unit used to process text and other model inputs. Context window: the maximum material a model can consider in one request. Cached input: previously processed input reused under provider rules. Quality threshold: the minimum score a result must reach. Evaluation set: examples used to test a system consistently.

Continue with 'AI Tokens, Context Windows, and Prompts Explained' for the foundations, then 'How to Verify AI Answers and Detect Mistakes' to build a repeatable checking habit.

Try this prompt

This is a suggested exercise, not a tested guarantee of any model’s output.

Summarize the fictional announcement below in exactly 120 words for a beginner. Preserve every date, number and limitation. Do not add benefits. End with a three-item checklist of facts you preserved. Announcement: The Robotics Club meets on 14 October in Room 204. Registration closes on 10 October. The workshop has 24 seats, costs nothing, and covers safe 5-volt sensor projects. It does not include motor wiring or take-home hardware.

Mini project & challenge

1. Write the five requirements: input, output, tools, quality and budget.
2. Create a four-point rubric covering dates, numbers, limitations and requested format.
3. If you have permitted access, run the same prompt on Sol and Astra; otherwise compare two sample outputs from a teacher or partner.
4. Score without using model reputation as a criterion and record correction time.
5. Calculate the cost of the illustrative 10,000-input and 2,000-output token request, showing every assumption.
6. Challenge: change one requirement and predict whether it affects model choice, prompt design or both.

Common mistakes

• Choosing from the model name alone: define the task and threshold first.
• Confusing API usage prices with consumer subscriptions: they are separate products.
• Ignoring output tokens or tool charges: list every included and excluded cost.
• Testing only one easy prompt: use several representative examples and save failures.

Check your understanding

1. When was GPT-6.1 Sol added to the checked OpenAI changelog?
2. How does OpenAI position Sol relative to Astra?
3. What five requirements does the lesson ask you to write?
4. What is the listed context window on the checked model page?
5. Does the checked model page list direct audio or video support?
6. What is the illustrative input cost for 10,000 tokens at $2 per million?
7. What is the illustrative output cost for 2,000 tokens at $10 per million?
8. What is the simplified total in that example?
9. Why save the exact model identifier and date?
10. When may the lower-cost model be the sensible choice?

In short

Choose GPT-6.1 Sol, Astra or another model by testing the real job. Define requirements, compare identical examples, calculate costs with explicit assumptions and count human review as part of the workflow.

Continue the Understand AI course →

Sources & review notes

1. OpenAI API changelog

   Release entry dated September 29, 2026; checked October 1.

2. OpenAI GPT-6.1 Sol model page

   Model capabilities, limits and standard pricing checked October 1. Prices and availability can change.

3. OpenAI model selection guide

   Evaluation guidance checked October 1. The cost example and teaching exercise in this article are original.

Product information was checked on 2026-10-01. This is a selected beginner guide, not an exhaustive archive of every announcement. Recheck access, pricing and compatibility before relying on a current product detail. Supplied screenshots remain unreplicated claims.