Coding Agent Cost Optimization

The Cost Problem

A typical coding agent session burns through tokens fast:

Activity	Tokens per call	Calls per hour	Hourly tokens
Code generation	5,000–50,000	10–30	150K–1.5M
Codebase search	2,000–20,000	20–50	100K–1M
Code review	10,000–80,000	5–10	100K–800K
Autocomplete	500–3,000	50–200	50K–600K
Total			400K–4M+

At premium model rates, that’s

3–30/hour per developer. For a team of 10, that's

500–5,000/month.

Smart Model Selection

Not every coding task needs the most expensive model. Match the task to the right tier:

Task	Recommended	Cost Tier	Why
Architecture design	`claude-opus-4-6`, `gpt-5.4`	$$$$ Premium	Complex reasoning needed
Code generation	`claude-sonnet-4-6`, `gemini-3.1-pro-preview`	$$$ Standard	Best quality/cost balance
Code review	`claude-sonnet-4-6`, `deepseek-v4-pro`	$$–$$$	Pattern matching, less creativity
Bug fixing	`claude-sonnet-4-6`, `gpt-5-mini`	$$–$$$	Focused, well-defined tasks
Tab completion	`gpt-5-mini`, `gemini-3.5-flash`	$$ Budget	Speed matters more than depth
Boilerplate	`deepseek-v4-flash`, `gpt-5-mini`	$ Economy	Simple, repetitive patterns

See Model Selection Guide for detailed model comparisons and per-tool configuration.

Caching Strategies

Coding agents are ideal for caching because they repeat similar patterns constantly.

Prompt Cache (Provider-Level)

Provider-level prompt caching is automatic through AI Sonar. Long system prompts — which coding agents always include — get cached at the provider level:

Provider	Cache Discount	Min Tokens
Anthropic	90% off reads	1,024
OpenAI	50% off reads	1,024
DeepSeek	90% off reads	64

Since coding agents send the same system prompt + project context on every call, prompt cache hit rates are typically 70–90%.

Prompt Cache Savings Example

For a request with 50,000 input tokens (typical coding agent call):

Direct API (no caching):
  50,000 tokens × $3.00/1M = $0.150

With prompt cache (40,000 cached + 10,000 new):
  Cached:  40,000 × $0.30/1M = $0.012
  New:     10,000 × $3.00/1M = $0.030
  Total: $0.042 (72% savings)

Token Management Tips

Set max_tokens

Prevent runaway generation:

{
  "model": "claude-sonnet-4-6",
  "max_tokens": 4096,
  "messages": [...]
}

Most coding tasks need 1,000–4,000 output tokens. Setting a limit prevents the model from generating unnecessarily long responses.

Use Auto-Compact

Most coding agents support context compaction — summarizing old conversation turns to reduce token count. Enable it:

Claude Code: Built-in auto-compact triggers at context limits
Cursor: Automatic context management
Codex CLI: Use --max-context flag

Avoid Context Bloat

Don’t paste entire files when a function is enough
Use .gitignore-style patterns to exclude irrelevant files from agent context
Clear conversation history when switching tasks

Quick Configuration

Each tool needs just a few lines to connect through AI Sonar:

Claude Code

export ANTHROPIC_API_KEY="sk-your-api-key"
export ANTHROPIC_BASE_URL="https://api.aisonar.dev"

Full setup guide →

Cursor

Settings → Models → OpenAI API Key: sk-your-key, Base URL: https://api.aisonar.dev/v1Full setup guide →

Codex CLI

export OPENAI_API_KEY="sk-your-api-key"
export OPENAI_BASE_URL="https://api.aisonar.dev/v1"

Full setup guide →

Gemini CLI

export GEMINI_API_KEY="sk-your-api-key"
export GOOGLE_GEMINI_BASE_URL="https://api.aisonar.dev"

Full setup guide →

Compatibility Matrix

Coding Tools

Native SDKs

Frameworks & Platforms

Chat Apps

Coding Agent Cost Optimization

The Cost Problem

Smart Model Selection

Caching Strategies

Prompt Cache (Provider-Level)

Prompt Cache Savings Example

Token Management Tips

Set max_tokens

Use Auto-Compact

Avoid Context Bloat

Quick Configuration

​The Cost Problem

​Smart Model Selection

​Caching Strategies

​Prompt Cache (Provider-Level)

​Prompt Cache Savings Example

​Token Management Tips

​Set max_tokens

​Use Auto-Compact

​Avoid Context Bloat

​Quick Configuration

The Cost Problem

Smart Model Selection

Caching Strategies

Prompt Cache (Provider-Level)

Prompt Cache Savings Example

Token Management Tips

Set max_tokens

Use Auto-Compact

Avoid Context Bloat

Quick Configuration