OpenLuxLLM API cost calculator: estimate monthly spend
Estimate coding-agent or application spend from the inputs that actually move the bill: model rate, sessions per day, input and output tokens, retry overhead, working days and team size. The calculator uses the current published OpenLux rate and never sends workload data to a server.
Reviewed by OpenLux Engineering Team · Last verified
What changes an LLM API bill?
The headline rate is only one input. These are the fields used by the calculator.
| Cost driver | Input | Why it matters |
|---|---|---|
| Model | Published USD rate | Every model has a different per-token rate |
| Usage | Sessions or requests per day | Converts one workload into monthly volume |
| Prompt size | Input tokens per session | Long repositories and documents increase repeated context |
| Response size | Output tokens per session | Agent plans and code explanations can dominate output |
| Reliability | Retry overhead | Failed or repeated calls consume additional capacity |
| Team | Developers and working days | Turns one-user estimates into a monthly team budget |
How do you estimate LLM API cost?
Multiply the model rate by input and output tokens, add retry overhead, then multiply by daily sessions, working days and users. The result is an API-usage estimate, not a vendor subscription price.
The calculator deliberately keeps all workload data in the browser. Only aggregate event fields such as model, workload profile and estimated monthly amount are sent to GA4 when the calculate button is pressed.
Why can two coding agents cost different amounts?
Coding tools differ in how much repository context they resend, how often they call tools and how long their plans and explanations become. A tool that looks cheaper per token can become more expensive if it repeats large prompts or requires more retries.
Measure cost per successful task instead of cost per request. Use the calculator for an initial budget, then replace its assumptions with one week of actual request logs.
Should you compare token prices or monthly estimates?
Use token prices to rank models, but use monthly estimates to make a purchase decision. Plans, credits and retry behaviour can change which option is cheaper at actual volume.
The model table below uses identical workload assumptions. It is useful for sensitivity analysis, not a promise that every coding agent generates the same tokens.
What happens when usage outgrows the estimate?
Top up pay-as-you-go credit or move to a monthly plan when the allowance fits stable usage. Keep request logs segmented by application and model so a rising bill can be traced to a workload rather than averaged across the whole account.
Use separate keys for experiments and production, and set spend limits where the application supports them.
Which option fits?
Use the estimate for
- Initial API budgets and team forecasts.
- Comparing models at the same token assumptions.
- Testing how retries or larger prompts change monthly cost.
Replace assumptions after
- One week of request-log data is available.
- The coding tool changes its context strategy.
- A model or plan price changes.
Estimate your coding-agent API cost
Adjust the workload to match one developer, then scale by team size. The calculation uses the current OpenLux catalogue rate and a blended token assumption.
Assumptions: one blended per-million-token rate, all input and output tokens counted once, and failed attempts equal to the retry percentage. Your console invoice is authoritative. Compare OpenLux plans.
Same assumptions across text models
These rows apply the same token, session and retry assumptions to each published model rate.
| Model | Monthly per developer | Monthly team cost |
|---|---|---|
| gpt-6-astra | $2.38 | $2.38 |
| gpt-5.6-sol | $1.19 | $1.19 |
| gemini-3.8-flash | $0.3564 | $0.3564 |
| gemini-3-flash-preview | $0.238 | $0.238 |
| claude-sonnet-4-5-20250929 | $1.71 | $1.71 |
| claude-opus-5 | $2.85 | $2.85 |
| claude-fable-5-1 | $11.42 | $11.42 |
| gemini-3.1-pro-preview | $0.9514 | $0.9514 |
| deepseek-v4 | $0.3965 | $0.3965 |
| qwen3-max | $0.5071 | $0.5071 |
Related models and live rates
| Model | Vendor | Price | Vs. official |
|---|---|---|---|
| gpt-5.6-sol | OpenAI | $0.1838 / 1M tokens | -96% |
| claude-sonnet-4-5-20250929 | Anthropic | $0.2647 / 1M tokens | -91% |
| gemini-3.8-flash | $0.0551 / 1M tokens | -92% | |
| deepseek-v4 | DeepSeek | $0.0613 / 1M tokens | -90% |
Key takeaways
- Budget from model rate, token direction, retries and users together.
- Compare models with the same workload assumptions before switching.
- Use real request logs after week one to replace the calculator defaults.
Frequently asked questions
How accurate is an LLM API cost calculator?
It is accurate as a planning tool when its token and retry assumptions match the workload. Replace defaults with actual request logs as soon as they are available.
Does the calculator estimate subscription pricing?
No. It estimates usage charges from published model rates. Compare any monthly plan allowance and terms on the pricing page before subscribing.
Why include retries in the estimate?
Failed or repeated calls can consume additional tokens. A low retry assumption understates production cost.
Is workload data uploaded anywhere?
No. Inputs stay in the browser. Only privacy-safe aggregate event fields are sent to GA4 when the estimate button is pressed.
Sources and methodology
Competitor claims were checked against the linked public pages on the verification date. Pricing and product terms can change; confirm them before purchase.
- OpenLux model catalogue and current ratesRates were read from the live catalogue on 2026-10-31.
- OpenLux pricingPay-as-you-go and plan terms.
- Requesty LLM cost calculatorCompetitor example of a workload-based calculator; checked for page-format context.
- Requesty AI cost optimizationCompetitor cost-optimization positioning; no claims were copied.
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Run the estimate on real request logs
Start with calculator defaults, then use one non-critical key to validate cost and latency.