Why use Together AI instead of OpenAI?

Together AI hosts open-source models at lower per-token prices and lets you run specific open weights. For workloads where an open model meets your quality bar, it can cut inference costs substantially versus proprietary APIs.

How is the monthly cost calculated?

The tool computes cost per request from your token profile and the model's input and output prices, multiplies by daily requests, then by 30 for the monthly figure.

Is the proprietary comparison fair?

It applies a typical proprietary model price (GPT-4o class) to the identical workload, so the dollar gap reflects pricing only. It does not judge quality differences, which you must weigh for your use case.

Are the prices exact?

They are editable presets based on published list prices and clearly labelled as estimates. Together AI updates pricing, so confirm the current rate in their dashboard before budgeting.

Is my data sent anywhere?

No. The calculator runs entirely in your browser. Nothing you enter is uploaded, stored or logged.

Together AI Cost Calculator

Together AI cost calculator

Together AI hosts open-weight models — Llama 3, Mixtral, DBRX and more — at per-token prices well below proprietary APIs. Pick a model, enter your prompt and completion tokens and daily request volume, and this tool returns the cost per request, per day and per month, plus a head-to-head against an equivalent proprietary option.

How it works

cost_per_request = (prompt_tokens / 1,000,000) × input_price
                 + (completion_tokens / 1,000,000) × output_price
monthly_cost     = cost_per_request × daily_requests × 30

Open models on Together AI typically charge a single blended rate or modest input/output prices, often a fraction of frontier-model pricing. The comparison column applies a GPT-4o-class price to the same workload so the savings — or the premium you would pay for proprietary quality — are explicit in dollars.

Tips

Match model to task. Mixtral and Llama 3 70B handle most production chat and RAG workloads; reserve the largest models for genuinely hard prompts.
Cap completion length. Output tokens drive most of the bill, so a tight max_tokens is the cheapest optimization.
Benchmark quality first. The savings only count if the open model meets your accuracy bar — test on real prompts before switching.