Model Choice
Different models, different price points. Compare and choose the right one.
T9 Token Budget helps engineering and finance teams estimate, compare, and optimize AI costs across models, providers, and use cases.
Define your workload, understand where the cost comes from, and compare available estimates before you scale.
Choose a use case, model, message sizes, and volume. The chatbot calculator walks you through it.
Get a Low / Likely / High monthly range, split into the assumptions that drove it.
Check the numbers against the model pricing reference before you commit to a plan.
Covering 15 leading AI providers
Provider names are shown for comparison only. T9 Token Budget is independent and is not affiliated with, endorsed by, or officially connected to any AI provider.
Different models, different price points. Compare and choose the right one.
Input, output, and total tokens drive the majority of your spend.
Longer context = more tokens. Optimize prompts and history.
More requests mean higher costs. Batch and cache where possible.
Prompt design, caching, and model routing reduce waste and cost.
Adjust the inputs to see how the estimate changes.
How many times this workload runs.
$2.10
$0.001615 / 1K tokens
All prices per 1M tokens (USD).
Prices manually verified against official provider pages as of 2026-09-08. See the methodology and pricing pages for sources and exclusions.
| Model | Input (1M) | Output (1M) |
|---|---|---|
| GPT-5.4 | $2.50 | $15.00 |
| Claude Sonnet 5 | $2.00 | $10.00 |
| Gemini 2.5 Pro | $1.25 | $10.00 |
| GPT-5.4 Mini | $0.75 | $4.50 |
| Mistral Large 3 | $0.50 | $1.50 |
| DeepSeek V4-Flash | $0.22 | $0.66 |
| Claude Haiku 4.5 | $1.00 | $5.00 |
| GPT-5.5 | $5.00 | $30.00 |
Every workload has its own token pattern. Pick the one closest to yours.
Customer-support or sales chatbot conversations
Summarizing meeting transcripts into notes
Summarizing long documents and reports
Answering questions over your own documents
Multi-step AI agents completing tasks with tool calls
Extracting structured data from invoices, forms, and documents
Translating documents and content with general-purpose LLMs
Sentiment, intent, topic labeling, moderation, and safety review
Using an LLM to score, compare, or benchmark AI responses
Generating synthetic training examples and structured datasets
We source pricing directly from each provider’s official page and normalize it across models so estimates stay comparable. Prices are manually re-checked against those official sources, not pulled automatically.
A quick primer on tokens, context windows, and cost.
Read guide GuideThe common reasons a real bill drifts above your estimate.
Read guide MethodologyHow we source, normalize, and verify pricing data.
Read guideJoin teams making smarter AI cost decisions.