In development · launching soon
Get the maximum out of every AI model.
Compare LLMs side by side, turn token counts into real API costs, and use prompt techniques that actually help — so you pick the right model for the job and pay only for what you need.
| Model | Context | $ / 1M in | $ / 1M out | $ / request |
|---|---|---|---|---|
| Model A | 128K | 1.00 | 4.00 | 0.0060 |
| Model B | 200K | 3.00 | 15.00 | 0.0210 |
| Model C | 32K | 0.20 | 0.60 | 0.0010 |
HOW IT WORKS
From "which model?" to a number you can defend
- 01
Describe the job
Pick the task you care about — support replies, code review, extraction, long-document Q&A — and the constraints that matter: budget, latency, context size.
- 02
Compare models side by side
Line up candidate models in one table: capabilities, context window, input and output pricing, and the date each figure was last checked.
- 03
Calculate, then test
Plug in your real token volumes to see monthly cost, then run a small eval on your own examples before you commit.
FEATURES
Everything you need to choose and budget an LLM
- COMPARE
Side-by-side model table
Capabilities, modalities, context window and per-token pricing in one sortable grid, with a source and checked-on date for every number.
- CALC
API cost calculator
Tokens in × input price + tokens out × output price, scaled to requests per day. See per-request, daily and monthly cost at a glance.
- CONTEXT
Context window planner
Check whether your system prompt, retrieved documents, history and expected answer actually fit — with headroom to spare.
- SAVE
Cost levers
Model the effect of prompt caching, batching, shorter prompts and routing easy requests to smaller models before you change code.
- PROMPTS
Prompt tips that hold up
Practical techniques — clear instructions, examples, structure, output formats — explained with before/after examples, not hype.
- EVAL
Bring-your-own eval
A simple workflow for testing models on your own tasks, so decisions rest on your data instead of someone else's leaderboard.
calculator.formula
cost_per_request = (input_tokens / 1,000,000) × input_price
+ (output_tokens / 1,000,000) × output_price
monthly_cost = cost_per_request × requests_per_day × 30USE CASES
Built for people who ship with AI
- Developers
- Pick a model for a new feature and estimate what it will cost at launch volume — before writing the integration.
- Product teams
- Compare quality, speed and price trade-offs in one place and document why a model was chosen.
- Founders & finance
- Turn "tokens" into a monthly budget line and see which levers move it the most.
- Prompt writers
- Tighten prompts so they cost less and produce more consistent output across models.
FAQ
Questions, answered
What is MaximumAI?
A toolkit for getting the most out of AI language models: a side-by-side LLM comparison, an API cost calculator, a context window planner, and practical prompt guidance. It is in development and not available yet.
When does it launch?
We are building it now. There is no sign-up form yet — the blog is live in the meantime, and this page will be updated when the tools open.
Will you rank the "best" model?
No. The best model depends on your task, budget and latency needs, and rankings go stale quickly. MaximumAI shows the trade-offs and helps you test on your own examples instead of handing you a single winner.
How will you keep prices accurate?
Model prices change often. Every figure in the comparison will show its source and the date it was last checked, and the calculator lets you override any price with the number from your provider's current pricing page.
Do I need to know what a token is?
No, but it helps. Our guide on tokens and API cost walks through it with a worked example. In short: providers bill by tokens, which are chunks of text, priced separately for input and output.
Are the numbers on this site real prices?
No. Any prices or benchmark figures in our articles and previews are hypothetical examples used to show the method. Always check the provider's official pricing page for current rates.
FROM THE BLOG
Latest guides
- prompting
Prompt Writing Techniques That Actually Help (With Examples)
Practical prompt engineering techniques that improve LLM output: clear task framing, context, examples, structure, output formats, step-by-step reasoning and testing.
- evaluation
How to Evaluate LLMs on Your Own Tasks: Build a Small Eval Set
Leaderboards can't tell you how a model handles your data. Learn to build a 30–100 example eval set, choose scoring methods, and compare models fairly.
- cost
How to Cut LLM API Costs: 8 Levers That Actually Work
Reduce LLM API spend with shorter prompts, output caps, prompt caching, batch processing, smaller models and routing — plus how to measure the savings before you ship.
STATUS: IN DEVELOPMENT
MaximumAI is launching soon
The comparison table, cost calculator and prompt toolkit are being built now. There's no waitlist form yet — check back at maximumai.xyz for the launch. Until then, our guides cover the same methods you'll find in the product.