How does NumStack calculate AI token costs?

NumStack uses real published pricing from OpenAI, Anthropic, Google, and other AI providers. Enter your typical usage (input tokens, output tokens, requests per day) to see projected monthly costs across all models.

Why do different AI models have different token prices?

Different models have different computational costs. Smaller models like GPT-4o mini or Haiku are cheaper but less capable. Larger models like GPT-4o or Claude Sonnet are more powerful but cost more per token.

How can I reduce my AI API costs?

Use smaller models for simple tasks, cache repeated prompts, trim output length (output tokens cost 3-5x more than input), and batch requests instead of real-time when latency allows.

Model Selection Advisor

Find your perfect AI model in 2 minutes

Answer 5 questions about your use case and we'll rank the best models for your specific needs.

Tell us about your project

This takes 60 seconds

How to choose the right AI model

There are now 30+ AI models to choose from, each optimized for different tasks. Some flagship models are great for open-ended chat, but overkill (and expensive) for simple classification. Reasoning-focused models excel at multi-step analysis, but can be slower and pricier for tasks that don't need it. Budget models are cost-friendly but may sacrifice quality on harder prompts.

Answer a few quick questions about your use case — task type, latency needs, budget, quality threshold, and context length — and this tool ranks models specifically for your problem, using the current prices and specs shown in the calculator. See the tradeoffs upfront before you commit.

Model choice impacts everything

•Latency: some models respond in well under a second, others take several seconds — critical for real-time apps
•Cost: budget and premium models can differ by 10x or more per token
•Quality: reasoning-oriented models tend to beat smaller general models on complex, multi-step tasks
•Context window: ranges from a few thousand tokens up to 200K+ depending on the model
•Availability: newer or specialized models sometimes have rate limits or restricted access

🎯 Our recommendation: Start with a fast, budget-tier model for prototypes. Upgrade to a larger or reasoning-focused model once you hit a latency or quality wall the smaller model can't clear.

How this calculator works

This tool scores every model in the database against your answers to five questions: task type, latency requirement, monthly budget, required output quality, and context length needed. Each model starts from a base score and gains or loses points based on how well it fits — for example, a model gets bonus points for matching your task type, more points if its price fits comfortably under a tight budget, and a penalty if it can't meet your latency requirement.

The top 5 highest-scoring models are shown ranked, each with a short explanation of why it scored well for your specific answers (fast inference, budget fit, strong reasoning capability, and so on). The scoring is a heuristic based on published price and capability signals for each model, using the current prices shown in the calculator — it's meant to narrow the field quickly, not replace testing a couple of finalists on your actual prompts.

How to use it

1.Answer what you're building: classification, generation, reasoning, extraction, or summarization.
2.Pick your latency requirement — real-time, fast, or flexible.
3.Set your approximate monthly budget tier.
4.Choose the output quality/accuracy bar your use case needs.
5.Select how much text you typically process at once, then review your top 5 ranked models with reasoning.

Common mistakes

⚠Picking the #1 result without checking cost — the top-ranked model balances several factors — always glance at its estimated monthly cost before committing, since a close #2 or #3 might be meaningfully cheaper.
⚠Answering "premium" budget out of habit — a higher budget tier removes cost penalties from scoring, which can surface models you don't actually need to pay for — answer with your real budget.
⚠Ignoring context length until it breaks — if your prompts include long documents or history, under-selecting context length can recommend a model that will truncate your input in production.
⚠Treating the ranking as final — the score is based on published specs and price, not your specific prompts — validate the top 2-3 candidates against real examples before locking in a choice.

Frequently asked questions

How does the advisor rank models?▾

It scores every model in the database against your five answers — task type, latency, budget, quality, and context length — giving bonus points for good fits and penalties for mismatches (like exceeding your budget or not meeting your latency need), then ranks the top 5 by score.

Does the ranking use real-time pricing?▾

It uses the same model database and prices shown elsewhere on NumStack, which we check regularly against provider pricing pages. Always confirm current rates on the provider's site before committing to a model for production.

What if two models score almost the same?▾

A close score usually means either would work well — at that point, other factors like your team's familiarity with a provider, existing infrastructure, or a quick head-to-head test on your real prompts should break the tie.

Can I change my answers and get new recommendations?▾

Yes — restart the questionnaire any time your requirements change (e.g. your budget shifts or you need a longer context window) and it will re-rank the full model database against the new answers.

Does a "Best Value" or "Top Quality" badge mean anything beyond the score?▾

Badges are a quick visual flag for models that stand out in one dimension — lowest cost for a budget priority, or top capability scores for a quality priority — layered on top of the underlying ranked score, not a separate rating system.