GPT-6 Model Cost Tool
GPT-6 Astra vs GPT-6.1 Sol
Compare capability, model specifications and API token pricing, then estimate cost per run, day and month. Use this Loop Engineering tool to choose a model for a verifiable, budget-aware workflow.
Calculate your cost difference ↓Compare your token cost
USD estimates · instant browser calculation · no login or data upload
Both choices use Standard processing. Up to 272,000 input tokens: short-context rates. Above that: long-context rates apply to the full request, including output. Combined input includes cached tokens and writes; cumulative usage across separate requests does not set this band.
Advanced
Writes create reusable context. Enter zero if none were billed; do not also count these as uncached input.
Applied rates: Standard / short context
GPT-6 Astra
GPT-6.1 Sol
You save $108.30/month with GPT-6.1 Sol
82.0% lower estimated token cost at identical token usage
Which should I choose?
Your input is mostly reused cached context, where Sol’s cached-input discount is particularly useful. Sol has 82.0% lower estimated token cost at the same usage. Prefer Astra when maximum capability on difficult reasoning, coding or professional work matters more than these savings, or a failed result would cost more. This is a cost scenario, not a quality score.
Pricing last verified: . Verify current provider pricing before making budget decisions. Same-token estimates exclude tools, taxes, subscription fees, negotiated or regional rates, and Fast, Ultrafast, Batch and Flex processing.
GPT-6 Astra vs GPT-6.1 Sol: Quick Answer
- Astra prioritizes the highest capability for difficult work; Sol offers near-Astra capability at lower token prices.
- Sol’s uncached input and output rates are 20% of Astra’s; the cached-input ratio is 10%. Your token mix determines the savings.
- Both have a 1,050,000-token context window and a 128,000-token output limit. Long-context billing begins above 272,000 input tokens.
- For coding agents, compare cost per accepted result, including retries and checker runs. Lower list prices do not establish equal task quality.
Sources: OpenAI’s Sol release and the official Astra / Sol model pages.
Pricing comparison
USD per million tokens (MTok), Standard processing. The context band applies to the full request. Cache writes are charged separately from uncached input and cache reads.
| Model / context band | Uncached input | Cached input | Cache write | Output |
|---|---|---|---|---|
| GPT-6 AstraStandard / short context | $10.00 | $1.00 | $12.50 | $50.00 |
| GPT-6 AstraLong context | $20.00 | $2.00 | $25.00 | $75.00 |
| GPT-6.1 SolStandard / short context | $2.00 | $0.10 | $2.50 | $10.00 |
| GPT-6.1 SolLong context | $4.00 | $0.20 | $5.00 | $15.00 |
At most 272,000 input tokens uses short-context pricing. Above it, input and cache rates double and output costs 1.5 times the short-context rate. See official API pricing and the API changelog. Verified .
Cost example: the calculator’s default usage
One representative request contains 10,000 uncached input tokens, 90,000 cached input tokens, 5,000 output tokens and 0 cache-write tokens. At 10 runs per day for 30 days, Astra costs $0.44 per run and $132.00 per month. Sol costs $0.079 per run and $23.70 per month: $108.30 saved, or 82.0% lower estimated token cost.
The example is generated by the same calculation function as the interactive results. Both models use identical token counts; real runs may produce different output volumes and require different numbers of attempts.
perRun = (uncachedInput × inputRate + cachedInput × cachedRate
+ cacheWrites × writeRate + output × outputRate) / 1,000,000
daily = perRun × runsPerDay
monthly = daily × daysPerMonth
monthlyDifference = astraMonthly - solMonthly
solSavingsPercent = astraMonthly > 0
? monthlyDifference / astraMonthly × 100 : 0Performance and positioning
OpenAI positions Astra for the most demanding end-to-end reasoning, coding and professional work, and Sol for similar practical work at lower cost. Those descriptions are a starting point for evaluation. This page does not translate benchmark scores into an intelligence percentage or promise that one model always wins. Use the official GPT-6 model guidance to select reasoning settings and test representative tasks.
| Attribute | GPT-6 Astra | GPT-6.1 Sol |
|---|---|---|
| Positioning | Highest capability for the hardest end-to-end work | Near-Astra capability at lower token prices |
| Model ID | gpt-6-astra | gpt-6.1-sol |
| Context window | 1,050,000 tokens | 1,050,000 tokens |
| Max input | 922,000 tokens | 922,000 tokens |
| Max output | 128,000 tokens | 128,000 tokens |
| Knowledge cutoff | April 30, 2026 | April 30, 2026 |
| Reasoning levels | low, medium, high, xhigh, max | low, medium, high, xhigh, max |
| Standard input / MTok | $10 | $2 |
| Standard cached input / MTok | $1 | $0.1 |
| Standard output / MTok | $50 | $10 |
| Best for | Difficult reasoning, coding and professional tasks where failure is costly | Repeated coding and agent work after task-specific validation |
Which model for coding agents?
Start by testing Sol on repeated tasks with clear acceptance checks: a passing test suite, a reviewed patch or a reproducible analysis. Stable context can make cache reuse valuable, but include the writes that created it. Count planner, worker and checker requests separately; a loop is usually more than one API call.
Escalate difficult or repeatedly failing work to Astra when evaluation supports it. Compare total spend until validation succeeds, retain the failure evidence and set an iteration cap. Use the Loop Engineering Goal Generator to define acceptance criteria, a fallback plan and a human approval gate.
Which model for expensive high-stakes runs?
Model cost is one component of a decision. If a wrong result would require hours of expert review or cause an expensive downstream change, Astra’s additional capability may justify its price. Evaluate accuracy and recovery effort on your workload; keep human review before consequential actions. Paying more never substitutes for verification.
How to reduce agent cost with Loop Engineering
- Send relevant files and bounded logs; keep reusable prompt prefixes stable.
- Measure actual cached-input reuse and include cache-write charges.
- Limit output and reasoning effort where task validation supports it.
- Set retry limits, stop rules and a budget cap before scheduling repeated work.
Use the Loop Budget Calculator for arbitrary models, iterations and retry budgets. For Claude prompt-cache billing, use the Claude Code Cost Calculator. Browse Loop Engineering Tools for related workflows.
GPT-6 Astra vs GPT-6.1 Sol FAQ
Is GPT-6.1 Sol cheaper than GPT-6 Astra?
Yes, at the published rates and identical token usage. Standard short-context uncached input and output cost 20% of Astra’s rates; cached input costs 10%. Actual cost per successful task also depends on token usage, retries and verification.
How much does GPT-6 Astra cost?
Standard short-context prices per million tokens are $10 uncached input, $1 cached input, $12.5 cache writes and $50 output. Longer prompts use a separate context band. The calculator projects token charges, not a subscription bill.
How much does GPT-6.1 Sol cost?
Standard short-context prices per million tokens are $2 uncached input, $0.1 cached input, $2.5 cache writes and $10 output. Enter your usage to estimate daily and monthly costs.
Is GPT-6.1 Sol as good as GPT-6 Astra?
OpenAI positions Sol as delivering near-Astra capability at lower cost. That does not guarantee equal results on every task. Astra remains the highest-capability option for difficult work; compare both against the same acceptance criteria.
Which model is better for coding?
Test Sol first for repeated coding tasks where cost matters and your checks establish adequate quality. Consider Astra for unusually difficult debugging, architecture or multi-step work when its additional capability justifies the cost. Neither choice removes the need for tests and review.
Which model is better for AI agents?
Sol’s lower token prices suit repeated agent and checker runs. Astra can be appropriate for difficult planning or escalations. In Loop Engineering, choose using validated outcomes, context reuse, a stop rule and a budget cap.
Does cached input change the comparison?
Yes. Sol’s cached-input rate is 5% of its uncached rate; Astra’s is 10%. Cache writes have their own charge. Treat all input categories as disjoint and include writes when measuring the net benefit of reuse.
When should I use Astra instead of Sol?
Use Astra when maximum capability on the hardest reasoning, coding or professional task matters more than token savings, or when a failed attempt has a high downstream cost. Validate that benefit on representative work rather than assuming a fixed intelligence advantage.
What happens with long-context pricing?
Prompts above 272,000 input tokens use twice the short-context input and cache rates and 1.5 times the output rate for the full request. The calculator applies this automatically. Total tokens across multiple independent requests do not establish a single request’s context band.
Does this calculator upload or save my usage?
No. Calculator inputs stay in browser memory and are not sent to an API or saved by the calculator. Copy the comparison if you want to retain it; reloading restores the example. No account or API key is needed.
Independent comparison by Loop Engineering, not affiliated with OpenAI. Published API list prices are estimates, not an invoice or a measure of subscription capacity. Verify current provider pricing and validate results before making budget decisions.