GPT-5.6 Sol
Hard reasoning and coding
The high-capability choice for difficult professional work, complex reasoning, and demanding coding tasks where answer quality matters more than the lowest unit cost.
1.05M context / 128K output
OpenAI model guide
OpenAI Models and Token Cost Calculator
Compare the current GPT-5.6 family, estimate one request or a month of API traffic, and see which parts of a prompt actually reach the bill. The calculator is for text API planning, not ChatGPT subscription pricing.
Choosing within the GPT-5.6 family
OpenAI describes its latest models as supporting text and image input with text output. The three choices below share a large context window, but they serve different budgets and levels of difficulty. The useful question is not simply which model is best. It is which model clears your quality bar at the lowest total cost.
GPT-5.6 Terra
Balanced production work
A middle option for applications that need strong intelligence without paying the highest rate on every request. It is a practical place to start for mixed workloads.
1.05M context / 128K output
GPT-5.6 Luna
High-volume, cost-sensitive tasks
The lower-cost member of the family for routing, extraction, classification, and other repeatable jobs that do not need the most capable model each time.
1.05M context / 128K output
Interactive cost estimate
Estimate OpenAI API cost
Enter the tokens in one request and how often it runs. The comparison uses the current text rates stored on this site for OpenAI models.
Request pattern
Example workloads
Highest monthly estimate
$14,400
GPT-5.4 Pro
Lowest monthly estimate
$83.6000
GPT-5.4 Nano
Estimated standard text cost
Rates are per 1M tokens. Results are sorted from highest to lowest monthly estimate.
GPT-5.4 Pro
Context 1,050K / 128K
$14,400
per month
Input
$30
Cache
not listed
Output
$180
$1.4400 per request
GPT-5.5 Pro
Context 1,050K / 128K
$14,400
per month
Input
$30
Cache
not listed
Output
$180
$1.4400 per request
GPT-5.5
Context 1,050K / 128K
$2,040
per month
Input
$5
Cache
$0.5
Output
$30
$0.2040 per request
GPT-5.6 Sol
Context 1,050K / 128K
$2,040
per month
Input
$5
Cache
$0.5
Output
$30
$0.2040 per request
GPT-5.4
Context 1,050K / 128K
$1,020
per month
Input
$2.5
Cache
$0.25
Output
$15
$0.1020 per request
GPT-5.6 Terra
Context 1,050K / 128K
$1,020
per month
Input
$2.5
Cache
$0.25
Output
$15
$0.1020 per request
GPT-5.6 Luna
Context 1,050K / 128K
$408.0000
per month
Input
$1
Cache
$0.1
Output
$6
$0.0408 per request
GPT-5.4 Mini
Context 400K / 128K
$306.0000
per month
Input
$0.75
Cache
$0.075
Output
$4.5
$0.0306 per request
GPT-5.4 Nano
Context 400K / 128K
$83.6000
per month
Input
$0.2
Cache
$0.02
Output
$1.25
$0.008360 per request
| Model | Rates per 1M | Per request | Per month |
|---|---|---|---|
GPT-5.4 Pro Context 1,050K / 128K | Input $30 Cache read not listed Output $180 | $1.4400 | $14,400 |
GPT-5.5 Pro Context 1,050K / 128K | Input $30 Cache read not listed Output $180 | $1.4400 | $14,400 |
GPT-5.5 Context 1,050K / 128K | Input $5 Cache read $0.5 Output $30 | $0.2040 | $2,040 |
GPT-5.6 Sol Context 1,050K / 128K | Input $5 Cache read $0.5 Output $30 | $0.2040 | $2,040 |
GPT-5.4 Context 1,050K / 128K | Input $2.5 Cache read $0.25 Output $15 | $0.1020 | $1,020 |
GPT-5.6 Terra Context 1,050K / 128K | Input $2.5 Cache read $0.25 Output $15 | $0.1020 | $1,020 |
GPT-5.6 Luna Context 1,050K / 128K | Input $1 Cache read $0.1 Output $6 | $0.0408 | $408.0000 |
GPT-5.4 Mini Context 400K / 128K | Input $0.75 Cache read $0.075 Output $4.5 | $0.0306 | $306.0000 |
GPT-5.4 Nano Context 400K / 128K | Input $0.2 Cache read $0.02 Output $1.25 | $0.008360 | $83.6000 |
This estimate covers text input, eligible cached reads, and generated text. It does not include cache writes, built-in tools, web search, containers, images, audio, or other separately billed features.
What goes into an OpenAI request
The visible user message is often only a fraction of the input. System and developer instructions, examples, retrieved documents, prior messages, tool definitions, and structured output schemas can all add tokens. If you are estimating a production feature, count the assembled request rather than the text box the user sees.
Normal input
This is new prompt content processed at the standard input rate. Retrieval-heavy applications can spend more here than expected because every attached passage becomes part of the request.
Cached input
Stable prompt prefixes may qualify for a discounted cached-read rate. Keep reusable instructions and examples consistent, but do not assume every repeated-looking prompt will be billed as a cache hit.
Generated output
Output is usually priced above input. A tight response format, a sensible maximum output, and early stopping can matter more than shaving a few lines from a small prompt.
Long context and extras
Some model records use higher rates after a long-context threshold. Tools, web search, containers, images, audio, cache writes, and other features can add charges that a text-only estimate does not capture.
Counting tokens before and after a request
A browser counter is useful while writing a prompt, setting limits, or comparing rough cost. It cannot see every model-specific detail in a fully assembled API call. OpenAI's token-counting guide recommends counting with the model and request shape you plan to use, while production responses provide the usage data you should rely on for billing reports.
That difference matters with chat history, tools, images, and structured messages. Use this page to make decisions early; use provider usage fields to reconcile what actually ran.
Useful checks before launch
- Count the final system and developer instructions.
- Include retrieved context and tool definitions.
- Set a realistic output budget from observed responses.
- Track cache hits separately from ordinary input.
- Compare estimated totals with API usage in staging.
OpenAI token calculator FAQ
Which GPT-5.6 model should I start with?
Start with Terra when you need a sensible quality and cost baseline. Test Luna for simpler, high-volume work and move difficult requests to Sol only when evaluations show a clear benefit.
Does cached input reduce every OpenAI request?
No. Cached pricing applies when a request reuses eligible prompt content under OpenAI's caching rules. A changing prompt or a short request may not receive the same benefit.
Is ChatGPT subscription pricing included here?
No. This page estimates API usage from token rates. ChatGPT plans are separate products and are not calculated from the token fields on this page.
Will this browser estimate exactly match my OpenAI bill?
Treat it as a planning estimate. Production usage can include model-specific tokenization, tools, cache writes, media, and other billable items. Use OpenAI's request usage data for final accounting.