Agent Framework Cost Comparison
Compare real token costs across Claude Code, LangChain, AutoGen, CrewAI, DSPy, and custom API. See how framework overhead compounds at your task volume.
| Framework | Per-task cost | Monthly cost | Overhead tok/step | Key strength |
|---|---|---|---|---|
| Custom / direct API | $0.05 | $162.00 | 100 | Minimal overhead |
| DSPy (Stanford) | $0.07 | $199.80 | 200 | Prompt optimization |
| Claude Code (Anthropic) | $0.10 | $329.60 | 800 | Deep codebase understanding |
| LangChain / LangGraph | $0.12 | $351.00 | 400 | Model agnostic |
| CrewAI | $0.12 | $360.00 | 500 | Role-based agents |
| AutoGen (Microsoft) | $0.15 | $442.80 | 600 | Multi-agent coordination |
Framework overhead estimates based on community benchmarks and published research. Actual overhead varies with configuration. Pricing verified 2026-08-20. · Full agent cost calculator
How to Use
Choose your model
Select the LLM powering your agents. Frontier models amplify framework overhead costs; smaller models make overhead proportionally smaller.
Set task parameters
Enter tasks per day, base input tokens per step, and output tokens per step. These determine the per-task cost baseline.
Compare frameworks
Results are ranked by monthly cost. The "overhead" column shows how many extra tokens each framework adds per step.
Factor in non-token costs
Claude Code has a subscription fee. Custom frameworks have dev costs. Monthly totals include platform fees where applicable.
Framework overhead compounds at scale
A 400-token overhead per step sounds minor. At 10 steps per task, 1,000 tasks/day, 30 days, that's 120 million extra input tokens per month. At $3/M: $360/month — just for framework overhead. At frontier model prices ($15/M), that's $1,800/month. At high volume, custom direct API code can pay for itself in weeks.
How to Cut This Cost
Use raw API instead of a framework for simple pipelines. LangChain and AutoGen add 500–2,000+ overhead tokens per call via internal prompts.
Switch to a lighter framework (DSPy, PydanticAI) for structured output tasks — they inject minimal overhead vs LangChain agents.
Combine framework routing with a cheap model for orchestration. Use GPT-5 mini or Haiku 4.5 as the planner, GPT-5.4 only for hard sub-tasks.
FAQ
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Last verified: 2026-08-20 · methodology · data sources