GPT-6.1 Sol costs one fifth of GPT-6 Astra per token, and on most business work it delivers the same result. On OpenAI’s own published numbers, Sol beats Astra’s best coding score at about one seventh of the cost per task, ties it on document analysis at under one fifth, and lands within about 2 points on computer use at roughly one seventh. Astra’s lead is real in one place: the hardest multi-step technical work, where it is 11 points ahead.
So the efficient default is Sol, with Astra kept for the tasks Sol measurably fails. The rest of this post shows the numbers behind that, why per-token price is the wrong way to compare, and how to set up routing so you pay Astra prices only when you need Astra results.
What Is GPT-6.1 Sol?
OpenAI announced GPT-6.1 Sol at DevDay on September 29, 2026, one week after GPT-6 Sol and Luna. It replaces GPT-6 Sol at the same price and is better on every evaluation OpenAI published. If you are on GPT-6 Sol today, the upgrade costs nothing per token. Test it before you switch, because it is a different model and some request settings may need changing.
| GPT-6 Astra | GPT-6.1 Sol | GPT-6 Luna | |
|---|---|---|---|
| Input / 1M tokens | $10.00 | $2.00 | $0.10 |
| Cached input / 1M | $1.00 | $0.10 | $0.01 |
| Output / 1M tokens | $50.00 | $10.00 | $0.50 |
| API model ID | gpt-6-astra | gpt-6.1-sol | gpt-6-luna |
Note the cached input line. Sol’s cached input is 95% off its standard input rate, against 90% for Astra. If your workload reuses a long system prompt or reference document on every call, Sol’s advantage is slightly larger than five to one.
Why Per-Token Price Is the Wrong Comparison
A model that costs five times more per token can still be cheaper per task if it uses fewer tokens, takes fewer attempts, or gets it right the first time. That was the case for Astra against the previous generation: Artificial Analysis measured it using about a third of the tokens of GPT-5.6 Sol per task.
Against GPT-6.1 Sol, that efficiency advantage mostly disappears. Because the per-token price ratio is exactly five on both input and output, the cost-per-task ratio tells you directly how many tokens each model used:
- Coding and computer use: Astra costs 5x to 7x more per task, so it is using the same or more tokens than Sol, not fewer.
- Document work and business workflows: about 5.5x more per task, so token use is roughly even.
- Hard scientific and technical work: about 4.4x more per task at maximum effort, so Astra uses around 13% fewer tokens. That is the only place token efficiency claws back any of the price gap.
In other words, the five to one sticker gap is close to the real gap. Astra does not earn its premium by being thrifty. It has to earn it by being better.
GPT-6.1 Sol vs Astra: The Cost Per Task Numbers
These come from the charts in OpenAI’s GPT-6.1 Sol announcement, which report cost per task at every reasoning effort setting. We picked each model’s best-scoring setting, plus a cheaper setting where it changes the conclusion.
| Work type (benchmark) | GPT-6.1 Sol | GPT-6 Astra | Astra costs | Score gap |
|---|---|---|---|---|
| Software engineering (DeepSWE 1.1) | 75.2% at $0.65 (high) | 74.1% at $4.43 (xhigh) | 6.8x | Sol +1.1 |
| Document analysis (GDP.pdf) | 32.0% at $0.35 (high) | 32.2% at $1.91 (xhigh) | 5.5x | Astra +0.2 |
| Business workflows (AutomationBench) | 36.1% at $0.30 (max) | 41.4% at $1.73 (max) | 5.8x | Astra +5.3 |
| Computer use (OSWorld 2.0) | 71.4% at $1.27 (max) | 73.5% at $9.44 (max) | 7.4x | Astra +2.1 |
| Scientific and technical (Terminal-Bench Science) | 57.0% at $5.47 (max) | 68.1% at $23.80 (max) | 4.4x | Astra +11.1 |
Read across the rows and the pattern is clear:
- Coding: Sol is ahead, outright. There is no reason to pay for Astra on software engineering work of this kind.
- Documents: a statistical tie. Astra’s extra 0.2 points cost 5.5 times as much.
- Computer use: Astra is better by about 2 points, at more than seven times the price. Sol at medium effort (66.8% at $0.77) already beats Astra at low effort (62.2% at $2.72).
- Business workflows: Astra’s 5 point lead is real. Whether it is worth almost six times the cost depends on what a failed workflow costs you.
- Hard technical work: Astra’s 11 point lead is the one gap that clearly justifies the price. Even Astra at its lowest setting ($11.41) scores below Sol at maximum ($5.47).
Cost per successful task
For benchmarks scored as a pass rate, dividing cost by success rate gives the price of one completed result, which is the number that matters for a budget.
| Work type | GPT-6.1 Sol | GPT-6 Astra |
|---|---|---|
| Software engineering | $0.86 | $5.98 |
| Document analysis | $1.09 | $5.93 |
| Business workflows | $0.83 | $4.18 |
On the business workflow benchmark, where Astra scores best, a successful run still costs five times as much with Astra.
Higher Effort Is Not Always Better
One detail in OpenAI’s data matters for anyone setting up Sol: on the coding benchmark, Sol at high effort scored 75.2% for $0.65, and at max it scored 71.9% for $1.57. Maximum effort cost 2.4 times as much and did worse. The same flattening shows up for Astra on coding (xhigh beat max) and on document analysis for both models.
The practical rule: test each workload at medium and high before reaching for max. Reasoning tokens are billed as output, so effort is the setting that moves your bill the most, and past a point it stops buying accuracy.
What This Means for a Monthly Bill
Scaled to plausible monthly volumes for a small or midsize team, using OpenAI’s per-task figures:
| Monthly workload | GPT-6.1 Sol | GPT-6 Astra |
|---|---|---|
| 1,000 business workflow runs (medium effort) | $190 | $1,270 |
| 200 coding tasks (each model’s best setting) | $130 | $886 |
| 100 computer-use tasks (medium effort) | $77 | $536 |
Benchmark tasks are not your tasks, so treat these as order-of-magnitude figures. The ratio is what transfers: for the same workload, expect an Astra bill five to seven times larger.
When Is Astra Worth the Premium?
Pay for Astra when at least one of these is true:
- The work is genuinely hard and technical. Multi-step scientific, data, or infrastructure work in a terminal is where Astra’s 11 point lead lives.
- A failure is expensive. If a wrong answer means a bad deploy, a misfiled financial record, or a customer-facing mistake, five extra points on a workflow benchmark can be worth far more than $1.40 a run.
- You cannot check the output cheaply. If nobody and nothing can verify the result, you want the model with the higher success rate on the first try.
- Sol has already failed at it. The best evidence for Astra is a task where Sol measurably falls short on your own data.
For everything else, including most coding, document, and back-office automation, Sol gives the same result for a fraction of the price.
The Efficient Setup: Sol First, Astra on Failure
The cheapest way to get Astra-level results is often not to use Astra by default. Run Sol first, check the result, and send only the failures to Astra.
On the business workflow numbers, that looks like this: run every task on Sol at max effort ($0.30), and escalate the 64% that fail to Astra at max effort ($1.73 each). The average cost is about $1.41 per task, against $1.73 for running everything on Astra, and the success rate is at least as high as Sol alone. On coding, where Sol already wins, the escalation rarely fires at all.
Two honest caveats. First, this only works where you can detect failure cheaply: tests that pass or fail, a form that validates, a record that reconciles. Second, the tasks Sol fails are probably harder than average, so Astra will succeed on fewer of them than its headline rate suggests. Measure it on your own work before you rely on the numbers.
A simpler version for teams without the engineering time: route by task type. Coding, documents, drafting, and routine automation go to Sol. Hard technical investigations and anything Sol has failed at before go to Astra. High-volume classification and extraction go to GPT-6 Luna, at one twentieth of Sol’s price.
A Caveat on the Numbers
Every figure in this post comes from OpenAI’s own evaluations, run in its research environment. Independent results may differ, and OpenAI chose which benchmarks to publish. The pattern (Sol near or above Astra on mainstream work, Astra ahead on the hardest tasks) is consistent across all six evaluations OpenAI published, which makes it more credible than any single number. Still, run a dozen of your own real tasks through both models before you commit a budget.
If you are also comparing against Anthropic, our GPT-6 Astra vs Claude comparison covers price and capability across the Claude range. GPT-6.1 Sol is priced the same as Claude Sonnet 5.
How Exodata Helps
We help small and midsize businesses cut AI spend without cutting results: measuring cost per completed task on your real workloads, setting up model routing and escalation, and tuning effort settings so you stop paying for reasoning that does not improve the answer. If your AI bill is growing faster than its value, talk to our team.
Frequently Asked Questions
Is GPT-6.1 Sol cheaper than GPT-6 Astra?
Yes. GPT-6.1 Sol costs $2 per million input tokens and $10 per million output tokens, one fifth of Astra’s $10 and $50. On OpenAI’s published benchmarks, Sol’s cost per task runs between about one fourth and one seventh of Astra’s, depending on the type of work.
Is GPT-6.1 Sol as good as GPT-6 Astra?
On many tasks, yes. OpenAI’s numbers show Sol scoring higher than Astra on software engineering, tying it on document analysis, and landing about 2 points behind on computer use. Astra is clearly better on the hardest scientific and technical work, by about 11 points, and leads by about 5 points on multi-step business workflows.
Does Astra use fewer tokens than Sol?
Not by much. Against the older GPT-5.6 Sol, Astra used about a third of the tokens. Against GPT-6.1 Sol, Astra uses the same or more tokens on coding and computer use, and only about 13% fewer on the hardest technical tasks. The five to one price gap is close to the real cost gap.
Should I upgrade from GPT-6 Sol to GPT-6.1 Sol?
In most cases, yes. The price per token is the same, cached input is half the price, and GPT-6.1 Sol scored higher than GPT-6 Sol on every evaluation OpenAI published. Test your prompts and request settings on the new model before switching production traffic.
What reasoning effort should I use with GPT-6.1 Sol?
Start at medium and try high. On OpenAI’s coding benchmark, high effort scored better than max at less than half the cost. Reasoning tokens are billed as output tokens, so maximum effort can multiply your bill without improving the result.
When should a business pay for GPT-6 Astra?
Use Astra for hard multi-step technical work, for tasks where a failure is expensive and hard to catch, and for anything GPT-6.1 Sol has already failed at on your own data. For most coding, document, and routine automation work, Sol delivers the same result at a fraction of the cost.