6.1 Sol vs 6 Astra: Price, Coding & Which to Choose

Choose GPT-6.1 Sol as a starting point for complex work with a tight budget. Evaluate GPT-6 Astra when difficult reasoning, missed details or repeated corrections cost you more than the model. This is a practical recommendation based on published evidence; we have not run our own head-to-head benchmark.

Searching for “6.1 sol vs 6 astra”? These are OpenAI models with different capability and cost tradeoffs. A higher version number does not put Sol above Astra. GPT-6.1 Sol is also a different version from GPT-6 Sol, so check the exact model name in every comparison.

GPT-6.1 Sol vs GPT-6 Astra at a glance

Sources: official Sol model specifications, Astra model specifications and API pricing. Rates above use Standard processing with up to 272,000 input tokens. API charges are separate from ChatGPT or Codex subscription allowances.

Price: what does “five times cheaper” mean?

At equal uncached input and billable output volumes, Sol’s Standard token charges are 80% lower. Cached input has a different ratio. This does not guarantee 80% savings per completed task: retries, reasoning, tool charges and review time change the result.

For example, 20,000 uncached input tokens and 5,000 billable output tokens cost $0.09 on Sol or $0.45 on Astra. At 1,000 identical runs, that is $90 versus $450 before additional charges. Change those assumptions below.

Estimate Standard API token costs

Input tokens per run

Billable output tokens per run

Number of runs

GPT-6.1 Sol

$90.00

Estimated total · USD

GPT-6 Astra

$450.00

Estimated total · USD

Illustrative estimate at rates checked October 25, 2026. Output includes billable reasoning tokens. Excludes caching, tools, retries, regional surcharges and subscription fees; assumes identical usage on every run. Inputs are processed in your browser.

For prompts above 272,000 input tokens, both model pages specify higher rates for the whole request: 2× input/cache rates and 1.5× output rates. The calculator applies those input/output multipliers automatically. Fast, Ultrafast, Batch, Flex, regional processing and third-party providers have different pricing; use matching tiers when comparing.

Benchmarks: close on some measures, different in practice

Artificial Analysis reports these figures across its evaluation suite. The index is a composite score, not a percentage accuracy rate. Its task costs and output speeds describe that test setup, not your application’s bill or end-to-end latency. Results can change as the evaluator updates its data.

For a concrete coding example, OpenAI’s software-maintenance case study reports 131 accepted stale-experiment findings for Sol Ultra at $15.12, versus 142 for Astra Ultra at $109.06. Sol Medium found 86 at $5.27. The author reviewed the findings and identified dependency-related cases Sol missed. This supports testing quality against cost on a specific workflow; it does not establish a universal coding winner. “Ultra” is the study’s Codex setting, not an API reasoning.effort value.

Which model should you choose?

The following starting points interpret the official model-selection guidance. Confirm them using your own acceptance criteria.

Is Sol faster than Astra?

There is no universal “30% faster” rule. Output tokens per second, time to first response and time to an accepted result are different measurements. Reasoning effort, processing tier, tools and retries all matter. The Artificial Analysis snapshot above even shows higher output throughput for Astra at Max effort. Measure elapsed time for the same task on your actual service.

For another perspective, Endor Labs’ October 2 Codex security evaluation reports median task times of 7.9 minutes for Sol and 11.6 for Astra, with functional pass rates of 77.7% and 82.1%, respectively. That supports a speed advantage on those tasks and that harness; it does not establish one across products or workloads.

Does Sol use less Codex allowance?

API prices do not establish a fixed Codex allowance multiplier. Product access and limits vary; consult the current Codex pricing and usage documentation and your account’s usage display. Avoid treating an individual user’s reported savings as a guaranteed conversion rate.

Run a fair comparison on your own tasks

1. Pick one routine task, one difficult task and one previous failure. Define what counts as success before running either model.
2. Give each model the same starting files, prompt, tools and completion criteria in a fresh session. Set a retry limit.
3. Start with the same available effort label and processing tier, then tune each model separately. Matching labels do not guarantee equal compute.
4. Record accepted results, total elapsed time, charges for all attempts, and minutes of human correction.
5. Compare total charges ÷ accepted results. With zero accepted results, report failure rather than a cost per success.

Use this task brief with both models:

Complete [task] using [inputs]. Preserve [constraints]. Success means [checks and deliverable]. State assumptions, identify unresolved issues, and report only checks you actually performed.

Keep Sol where it meets your quality bar. Escalate to Astra when a detectable failure or a more demanding task justifies it. Include the first attempt’s cost when evaluating that workflow; escalation is not automatically cheaper.

Need a clearer task brief? Use our prompt-writing guide or free prompt builder. For a first introduction to the assistant, start with the ChatGPT beginner’s guide.

Sources and corrections

Prices, model specifications and cited measurements were checked on October 25, 2026. This page combines official documentation with the evaluator’s own published measurements, and labels our recommendations separately. Follow the linked sources for changes. Corrections: jimmysenior360@gmail.com.