GPT-6 Astra vs GPT-6.1 Sol vs GPT-5.6 Sol (2026)

We tested GPT-6 Astra, GPT-6.1 Sol and GPT-5.6 Sol on four coding tasks. All three passed; see the cost, latency and task-by-task recommendations.

Which GPT model should you use for coding? The short answer: GPT-6.1 Sol for everyday work, GPT-6 Astra for the hardest tasks, and GPT-5.6 Sol when you need to preserve an existing GPT-5.6 workflow. That answer is not based only on official positioning. We ran the same four coding tasks through all three models. All three passed 4/4. The difference was mostly on the bill.

This is not a laboratory benchmark. It is a small test with four representative tasks, one run per task and automated assertions. The goal is not to crown "the most powerful model" but to show the cost/performance tradeoff per task.

The 30-second decision

Model	Best for	Codex4Less input / output	Mini test
GPT-6 Astra	Hardest reasoning, large refactors, complex debugging	$1.20 / $5.00	4/4 · $0.030
GPT-6.1 Sol	Everyday coding, agentic work, cost/performance balance	$0.10 / $1.00	4/4 · $0.003
GPT-5.6 Sol	Existing GPT-5.6 workflows, compatibility	$1.50 / $1.50	4/4 · $0.024

Prices are USD per 1 million tokens as of October 21, 2026. The "Mini test" cost is the total for all four tasks.

What the official positioning says

OpenAI's model pages draw clear lines between the three:

GPT-6 Astra: The company's most capable model. Positioned for complex reasoning, coding, computer use, research and document creation.
GPT-6.1 Sol: "Near-Astra performance at a lower cost" for complex coding, computer use and professional work. OpenAI recommends comparing it with Astra on your own tasks.
GPT-5.6 Sol: The flagship of the GPT-5.6 family. The gpt-5.6 alias routes to this model, making it the natural choice for existing GPT-5.6 prompts and workflows.

All three support a large context window, up to 128K output tokens and reasoning. Codex4Less's model catalog lists them with a 900K context; if you plan to use long context, verify the current limit and price on the pricing page. Knowledge cutoff is April 30, 2026 for Astra and 6.1 Sol, and February 16, 2026 for 5.6 Sol.

Model	Position	Knowledge cutoff	Codex4Less input / output
GPT-6 Astra	Most capable	April 30, 2026	$1.20 / $5.00
GPT-6.1 Sol	Near-Astra, more economical	April 30, 2026	$0.10 / $1.00
GPT-5.6 Sol	GPT-5.6 flagship	February 16, 2026	$1.50 / $1.50

Mini test: four tasks, three models

We ran the test through the Codex4Less API on October 21, 2026. Every model received the same four tasks:

Bug fix: Fix an interval-merging function that failed to merge touching intervals.
Refactor: Simplify an order-summary function without changing its behavior.
From scratch: Write a function that returns the k most frequent integers, breaking ties by smaller value first.
Dependency order: Write a build-order function with cycle detection and lexicographic tie-breaking.

Conditions: identical prompts, temperature: 0, max 1,200 output tokens, one run per model per task. Generated code was executed against automated tests; a task passed only if every assertion passed.

Model	Pass rate	Total tokens	Total cost	Average latency
GPT-6 Astra	4/4	23.2K	$0.030	3.6s
GPT-6.1 Sol	4/4	21.8K	$0.003	4.5s
GPT-5.6 Sol	4/4	15.8K	$0.024	3.6s

What the results show

All three models passed all four tasks. On this task set, Astra's higher price did not buy a better result.
GPT-6.1 Sol was about 91% cheaper than Astra and 89% cheaper than GPT-5.6 Sol. It passed the same tests, making it the strongest default for everyday coding.
GPT-5.6 Sol was about 20% cheaper than Astra. It is not the cheapest option, but it makes sense when you need compatibility with existing GPT-5.6 prompts.
Latency was similar. GPT-6.1 Sol took 8.1 seconds on the hardest task; with one run per task, do not read that as a general speed difference.

Token counts come from the API's usage field. Even small prompts can consume extra context at the gateway, so focus on relative cost under identical conditions rather than absolute token counts. This is a four-task sample, not a statistical benchmark.

Task-by-task recommendations

Task	Recommendation	Why
Small bug fixes, refactors, test generation	GPT-6.1 Sol	Passed all four tasks at a much lower cost
Large refactors, architecture, hard debugging	GPT-6 Astra	Highest reasoning capacity; the gap may appear here
Existing GPT-5.6 prompts and compatibility	GPT-5.6 Sol	Minimizes behavior changes
Long-context code review	Any of the three, subject to limits	Check the context limit and long-context pricing
Agent loops	Start with 6.1 Sol, escalate to Astra when needed	Avoid wasting tokens on routine steps

Cost/performance: when is Astra worth it?

Based on the four-task average, the cost per task is roughly:

GPT-6 Astra: $0.0074
GPT-6.1 Sol: $0.0007
GPT-5.6 Sol: $0.0060

At those averages, 100 routine coding tasks would cost about $0.74 on Astra, $0.07 on 6.1 Sol and $0.60 on 5.6 Sol. The absolute gap looks small, but it compounds when agents run hundreds of tasks.

That does not mean you should drop Astra. On a large refactor, a complex bug or a multi-step agent task, fewer retries can recover the price difference quickly. The practical rule: default to 6.1 Sol, escalate to Astra for hard work, keep 5.6 Sol for existing GPT-5.6 workflows. Route by task complexity; do not send every request to the most expensive model.

Switching models in Codex CLI, Cline and Aider

You do not need a new API key or base URL. Change the model ID:

Codex CLI: Set model = "gpt-6.1-sol" in ~/.codex/config.toml and keep wire_api = "responses". Full setup: Codex CLI guide.
Cline: Enter gpt-6.1-sol in the Model ID field. Setup: Cline guide.
Aider: Start with --model openai/gpt-6.1-sol. Setup: Aider guide.

To move to Astra, change the model ID to gpt-6-astra with the same key.

FAQ

Is GPT-6 Astra always better?

No. Astra is the most capable model, but not every task needs its reasoning capacity. In our four-task test, all three models produced passing code, so Astra's extra cost bought no measurable quality gain. Harder and longer tasks may tell a different story; test on your own workload.

Can GPT-6.1 Sol replace Astra?

For everyday coding, small refactors and routine agent work, yes for most users. For the hardest reasoning, major architecture decisions and complex debugging, Astra is still worth testing. OpenAI itself recommends comparing 6.1 Sol with Astra on your tasks.

Why would anyone still use GPT-5.6 Sol?

For existing GPT-5.6 prompts, production workflows and compatibility requirements. If you are starting fresh and optimizing cost/performance, 6.1 Sol is more attractive. If you want to avoid behavior changes in an existing system, keep 5.6 Sol.

Why is the test so small?

It is four tasks, one run each, with automated assertions. The goal is not a comprehensive benchmark; it is a check on whether the most expensive model is necessary for routine work. A larger test set could produce different results.

Which model is cheapest?

GPT-6.1 Sol on these four tasks. Prices and promotions change; check the pricing comparison and the pricing page for current rates.

Which model should I use for long context?

All three support large context windows; the difference is usually price and task complexity. Long-context pricing can vary by model and provider, so verify the current limits and rates before you rely on them.

How can I run the same test?

Pick 3-5 tasks from your own workload, run the same prompt through all three models, and compare the output with tests or blind review. Watch tokens and cost in the dashboard. A small balance is enough to start.

Conclusion

Start with GPT-6.1 Sol for everyday coding: it matched Astra on our four-task test and cost about 91% less. Save Astra for large refactors, hard debugging and the most demanding reasoning. Keep GPT-5.6 Sol if you need to preserve an existing GPT-5.6 workflow.

Run the same prompt through all three models on your own tasks. If quality is close, make 6.1 Sol your default. Check current models and prices on the pricing page, or create a free account and test with a small balance.

Compare prices → Create a free account →

Sources

Test results were collected through the Codex4Less API on October 21, 2026. Codex4Less is an independent platform and is not affiliated with OpenAI. GPT and Codex are trademarks of OpenAI.