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Agentic AI

Compare cost for multi-step agent workflows with context accumulation, tool calls, and repeated inference loops.

What matters most

Agentic AI workloads have unique cost drivers: context accumulates across steps, tool calls add input/output overhead, and cache hit rates significantly impact total cost. Low input price and good cache support matter most.

Base example

This page starts with 6,000 input tokens (accumulated context), 3,000 output tokens (tool calls + final response), and 3,000 monthly agent sessions.

Low-cost agentic AI models in the base example

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Model Input Output Monthly cost
titan-embed-text-v2 $0.0200 / 1M tokens N/A $0.36
Qwen2.5-Coder-3B-Instruct $0.0100 / 1M tokens $0.0300 / 1M tokens $0.45
Qwen2.5-Coder-7B-Instruct $0.0100 / 1M tokens $0.0300 / 1M tokens $0.45
Qwen2.5-Coder-7B $0.0100 / 1M tokens $0.0300 / 1M tokens $0.45
llama3.2-11b-vision-instruct $0.0150 / 1M tokens $0.0250 / 1M tokens $0.49
llama3.2-3b-instruct $0.0150 / 1M tokens $0.0250 / 1M tokens $0.49
Llama-3.2-3B-Instruct $0.0200 / 1M tokens $0.0200 / 1M tokens $0.54
paddleocr-vl $0.0200 / 1M tokens $0.0200 / 1M tokens $0.54

Recommended cheaper models

Model Cost per session Monthly cost
titan-embed-text-v2 $0.000120 $0.36
Qwen2.5-Coder-3B-Instruct $0.000150 $0.45
Qwen2.5-Coder-7B-Instruct $0.000150 $0.45
Qwen2.5-Coder-7B $0.000150 $0.45
llama3.2-11b-vision-instruct $0.000165 $0.49
llama3.2-3b-instruct $0.000165 $0.49
Llama-3.2-3B-Instruct $0.000180 $0.54
paddleocr-vl $0.000180 $0.54

Open calculator to estimate your own workload cost with different token counts and model choices.

Browse providers to compare models from OpenAI, Anthropic, Google, Mistral, and other providers.

Pricing data from catalog last generated Aug 10, 2026. Verify before production decisions. Data sources.