Forecast Your AI Costs Before They Break Your Margin

Most product teams discover their true AI costs only after the cloud bill arrives. Model selection isn't just a technical decision; it's a structural unit economics problem. Map your expenses now.

Trusted by 500+ product teams to forecast AI costs

Estimate Your AI Costs

AI Token Cost Calculator

Token count will be estimated based on this text

2026 LLM Model Comparison

ModelInput CostOutput CostContextTierBest For
GPT-5.4
OpenAI
$5/1M$15/1M200KflagshipComplex reasoning, AI agents, coding, multi-step workflows
GPT-4o
OpenAI
$2.5/1M$10/1M128Kmid-rangeBalanced performance, general tasks, multimodal
GPT-4o-mini
OpenAI
$0.15/1M$0.6/1M128KbudgetSimple tasks, high volume, cost optimization
Claude Opus 4.6
Anthropic
$15/1M$75/1M200KflagshipMost capable, complex analysis, research, creative writing
Claude Sonnet 4.6
Anthropic
$3/1M$15/1M200Kmid-rangeBalanced, long documents, coding, analysis
Claude Haiku 3.5
Anthropic
$0.25/1M$1.25/1M200KbudgetFast responses, cost-sensitive, simple tasks
Gemini 2.0 Pro
Google
$1.25/1M$5/1M2000KflagshipMassive context, multimodal, research
Gemini 1.5 Flash
Google
$0.075/1M$0.3/1M1000KbudgetSpeed, cost efficiency, large context
Llama 3.3 70B
Meta
$0.59/1M$0.79/1M128Kmid-rangeSelf-hosted, data privacy, customization
Mistral Large 2
Mistral
$2/1M$6/1M128Kmid-rangeEuropean deployment, multilingual, coding
DeepSeek V3
DeepSeek
$0.27/1M$1.1/1M128KbudgetCost-effective reasoning, coding, math

Pricing as of March 2026. Costs are per 1 million tokens. Context window shown in thousands (K).

Why AI Costs Compound Fast

AI costs don't scale linearly. They compound in ways that catch teams off guard.

A product with 10,000 users making 5 requests/day at $0.03/1K tokens costs **$4,500/month**. Add in retries, monitoring, and infrastructure, and you're looking at **$6,000-7,000** before you know it.

Most teams discover this after the bill arrives.

The Hidden Costs

  • Beyond token pricing: Infrastructure (GPU hosting, API gateways)
  • Monitoring and observability
  • Customer support for AI errors
  • Engineering time for prompt optimization
  • Failures and retries (LLMs aren't 100% reliable)

The cost of getting it wrong

  • Over-provisioning: Wasting budget on expensive models
  • Under-provisioning: Poor user experience, churn
  • No monitoring: Silent cost explosions
  • Wrong model choice: 10-100x cost difference

Compare Models Side-by-Side

ModelInput CostOutput CostContext WindowBest For
Execute instant pricing comparisons across 20+ production models$2.50/1M$10.00/1M128KComplex reasoning, coding
Project monthly operations expenses based on exact token volume$2.50/1M$10.00/1M128KComplex reasoning, coding
Receive concrete model selection recommendations$2.50/1M$10.00/1M128KComplex reasoning, coding
Implement token optimization and dynamic routing strategies$2.50/1M$10.00/1M128KComplex reasoning, coding
Model real-world costs directly from live production systems$2.50/1M$10.00/1M128KComplex reasoning, coding

Who Should Use This Calculator

Product Managers

Forecast costs for roadmap planning. Make informed decisions about which AI features to build.

"I needed to justify the AI feature to my CEO. This calculator gave me realistic numbers to work with." — PM at Series B startup

Engineers

Compare models for specific use cases. Understand the cost implications of your architecture choices.

"We were using GPT-4 for simple classification. Switched to Haiku and cut costs by 95%." — Lead Engineer

Founders

Build investor pitches with realistic AI economics. Show you understand unit economics.

"Investors asked about AI costs. I showed them the calculator output and they nodded." — Founder, AI SaaS

Finance Teams

Budget for AI initiatives. Understand cost drivers and scaling implications.

"Finally, a tool that speaks my language. No more guessing AI budgets." — CFO, Tech Startup

Quick Wins to Reduce Your AI Costs

1. Use Smaller Models for Simple Tasks

Classification, extraction, and simple Q&A don't need GPT-4. Try mini or Haiku first.

Savings: 80-95%

2. Implement Caching

Cache responses to repeated queries. Most users ask similar questions.

Savings: 30-50% for chat applications

3. Batch Requests When Possible

Process multiple items in a single API call instead of sequential calls.

Savings: 20-40% for batch processing

4. Consider Hybrid Approaches

Use a cheap model for 80% of cases, expensive model for the remaining 20%.

Savings: 50-70% with minimal quality loss

5. Optimize Your Prompts

Shorter prompts = fewer tokens. Be specific and concise.

Savings: 10-30% depending on prompt length

6. Monitor and Alert

Set up cost monitoring. Catch anomalies before they become problems.

Savings: Prevents 10x+ cost explosions

Frequently Asked Questions

How accurate are these base estimates?

We map directly to published API pricing endpoints. Be aware that regional pricing, scale volume discounts, and enterprise tier provisioning will alter your final outlay.

What hidden costs aren't included?

We strictly calculate pure token inference. You must separately project your vector infrastructure, eval monitoring tools, and dedicated engineering hours.

Should I self-host open-source models to save money?

Open-source creates leverage only at massive scale. At low to medium volume, your dedicated cloud GPU provisioning will completely dwarf standard proprietary API costs.

Get Your Detailed AI Cost Analysis Report

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