LLM Cost Calculator: 300+ Models Compared in Real-Time

LLM Cost Calculator: Compare 300+ Models in Real-Time

Building a production LLM application without accurate cost modeling is like flying a plane without fuel gauges. You might take off, but you’ll never know when you’ll run out. A single model selection decision can make or break your unit economics, turning a profitable feature into a money pit overnight.

This guide provides the definitive framework for comparing LLM costs across 300+ models, understanding hidden pricing factors, and building accurate TCO projections for your specific use case.

Key Takeaway

Model sticker price is only 20-30% of your true cost. Context management, retry logic, and infrastructure overhead typically dominate your bill—but only if you don’t plan for them.

Why LLM Cost Comparison Matters

The LLM market has fragmented into hundreds of models across dozens of providers, each with pricing that looks simple until you scale. A model that costs 3x less per token can cost 10x more in production due to context window limitations, latency-driven retries, or missing batch API support.

The Real Cost Equation

Your actual cost per request is:

Total Cost = (Input Tokens × Input Rate) + (Output Tokens × Output Rate) + (Context Overhead) + (Retry Costs) + (Infrastructure)

Where:

• Context Overhead: System prompts, RAG context, conversation history (often 2-5x your prompt tokens)
• Retry Costs: 5-15% of requests fail or timeout, requiring full reprocessing
• Infrastructure: Compute, storage, monitoring, and engineering time

A model with 200K context at $3/1M input tokens might seem expensive compared to 128K context at $2.50/1M. But if your RAG pipeline needs 150K context, the cheaper model forces you to chunk and retrieve differently, increasing engineering complexity and potentially degrading quality.

Current Pricing Landscape

Based on verified pricing data from major providers (last updated Q4 2024), here’s how flagship models compare:

Model	Provider	Input Cost	Output Cost	Context Window	Source
Claude 3.5 Sonnet	Anthropic	$3.00/1M	$15.00/1M	200K	Anthropic Docs
Claude Haiku 3.5	Anthropic	$1.25/1M	$5.00/1M	200K	Anthropic Docs
GPT-4o	OpenAI	$5.00/1M	$15.00/1M	128K	OpenAI Pricing
GPT-4o Mini	OpenAI	$0.15/1M	$0.60/1M	128K	OpenAI Pricing

Pricing Freshness

LLM pricing changes frequently. The data above was verified on 2024-11-15 for Anthropic and 2024-10-10 for OpenAI. Always verify current pricing before making production decisions.

Hidden Pricing Factors

The table above shows only the base rate. Real-world costs include:

Context Window Efficiency

• Models with larger context windows often have higher base rates
• But they can eliminate expensive retrieval logic or multi-step processing
• Break-even analysis: If you need greater than 80% of a model’s context, the larger window usually pays for itself

Batch API Availability

• Batch processing reduces costs by 50% for async workloads
• Not all models support batch APIs
• Claude 3.5 Sonnet: No batch API (as of Nov 2024)
• GPT-4o: Batch API available at 50% discount

Rate Limits & Priority Tiers

• Standard tier requests can face “server overloaded” errors during peak times
• Priority tier adds 20-30% cost but guarantees availability
• For production systems, priority tier is often non-negotiable

Building Your Cost Model

Step 1: Profile Your Traffic

Before comparing models, quantify your actual usage patterns:

1. Measure average tokens per request

   • Input: System prompt + user query + context (RAG, history)
   • Output: Expected response length
   • Run 100+ production-like requests and measure actual token counts

2. Calculate request volume

   • Daily/monthly requests
   • Peak vs average load
   • Growth projections (3x in 12 months is common)

3. Identify retry patterns

   • Measure your current error rate
   • Typical production systems see 5-15% retries
   • Each retry costs a full request

4. Account for context growth

   • RAG retrieval can 5-10x your input tokens
   • Conversation history compounds over multi-turn interactions
   • System prompts often add 500-2000 tokens

Step 2: Model Comparison Framework

Use this formula to calculate true cost per successful request:

Widget

Full-featured pricing calculator with TCO breakdowns (3-year ROI analysis)

Interactive widget derived from “LLM Cost Calculator: 300+ Models Compared in Real-Time” that lets readers explore full-featured pricing calculator with tco breakdowns (3-year roi analysis).

Key models to cover:

• Anthropic claude-3-5-sonnet (tier: general) — refreshed 2024-11-15
• OpenAI gpt-4o-mini (tier: balanced) — refreshed 2024-10-10
• Anthropic haiku-3.5 (tier: throughput) — refreshed 2024-11-15

Widget metrics to capture: user_selections, calculated_monthly_cost, comparison_delta.

Data sources: model-catalog.json, retrieved-pricing.