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Coding Agent Cost Calculator

Calculate autonomous coding agent spend across multi-file diffs, test-runner retry loops, 75% prompt caching, and Frontier + Economy model routing.

Preset: Coding Agent (12 steps, routed) Reviewed 2026-11-12 Read How AI Agent Costs Work

Runs 100% in your browser. Zero inputs stored or sent.

Per Successful Task $0.331
Monthly Spend $795.17

Basics

Advanced (Caching, Retries, Routing & Variance)
Cost / Attempt $0.209 LLM $0.205 + API $0.0040
Cost / Successful Task $0.331 P90 at 85% success
Daily Budget $26.51 80 tasks/day
Monthly / Annual $795.17 $9,675 / yr
Peak context: 39,200 tokens Fits inside Claude Sonnet 5.5 (1,000,000)
Cheapest model that fits: GPT-5 nano ($53.76/mo)

Cumulative & Per-Step LLM Cost

Bars: step cost (incl. retries) · Line: cumulative LLM cost
$0.205$0.136$0.0682$0.0000Step 1: $0.0102 step / $0.0102 cumulativeStep 2: $0.0115 step / $0.0217 cumulativeStep 3: $0.0127 step / $0.0344 cumulativeStep 4: $0.0140 step / $0.0484 cumulativeStep 5: $0.0152 step / $0.0636 cumulativeStep 6: $0.0164 step / $0.0800 cumulativeStep 7: $0.0177 step / $0.0977 cumulativeStep 8: $0.0189 step / $0.117 cumulativeStep 9: $0.0202 step / $0.137 cumulativeStep 10: $0.0214 step / $0.158 cumulativeStep 11: $0.0226 step / $0.181 cumulativeStep 12: $0.0239 step / $0.205 cumulativeS1S2S3S4S5S6S7S8S9S10S11S12

Per-Step Token & Cost Breakdown

Step Input Cached Output Step Cost Cumulative
Step 1 7,500 5,625 900 $0.0102 $0.0102
Step 2 10,300 7,725 900 $0.0115 $0.0217
Step 3 13,100 9,825 900 $0.0127 $0.0344
Step 4 15,900 11,925 900 $0.0140 $0.0484
Step 5 18,700 14,025 900 $0.0152 $0.0636
Step 6 21,500 16,125 900 $0.0164 $0.0800
Step 7 24,300 18,225 900 $0.0177 $0.0977
Step 8 27,100 20,325 900 $0.0189 $0.117
Step 9 29,900 22,425 900 $0.0202 $0.137
Step 10 32,700 24,525 900 $0.0214 $0.158
Step 11 35,500 26,625 900 $0.0226 $0.181
Step 12 38,300 28,725 900 $0.0239 $0.205

All Verified Models Compared (Sorted by Monthly Cost)

Single-model comparison using your current token, caching, retry, and success assumptions. Prices verified 2026-11-12 · Methodology & formulas

Model Provider In / 1M Cached / 1M Out / 1M Context Fit Cost / Success Monthly Source
GPT-5 nano Cheapest fit OpenAI $0.05 $0.005 $0.40 400,000 Fits $0.0224 $53.76 Official pricing
Claude Haiku 5.5 Anthropic $0.10 $0.01 $0.50 200,000 Fits $0.0325 $78.07 Official pricing
GPT-4.1 nano OpenAI $0.10 $0.025 $0.40 1,047,576 Fits $0.0362 $86.88 Official pricing
GPT-4o mini OpenAI $0.15 $0.075 $0.60 128,000 Fits $0.0652 $156.58 Official pricing
DeepSeek V4.1 Flash (Together) Together AI $0.30 $0.006 $1.20 1,048,576 Fits $0.0699 $167.82 Official pricing
DeepSeek V4.1 Flash (Fireworks) Fireworks AI $0.30 $0.006 $1.20 1,048,576 Fits $0.0699 $167.82 Official pricing
GPT-5 mini OpenAI $0.25 $0.025 $2.00 400,000 Fits $0.0866 $207.80 Official pricing
GPT-OSS 120B (Groq) Groq $0.15 Not published $0.60 131,072 Fits $0.0935 $224.34 Official pricing
Gemini 3.5 Flash-Lite Google $0.30 $0.03 $2.50 1,048,576 Fits $0.105 $251.05 Official pricing
Gemini 2.5 Flash Google $0.30 $0.03 $2.50 1,048,576 Fits $0.105 $251.05 Official pricing
GPT-4.1 mini OpenAI $0.40 $0.1 $1.60 1,047,576 Fits $0.126 $301.80 Official pricing
Gemini 3.8 Flash Google $0.75 $0.075 $3.75 1,048,576 Fits $0.203 $486.40 Official pricing
Claude Haiku 4.5 Anthropic $1.00 $0.1 $5.00 200,000 Fits $0.268 $643.45 Official pricing
Llama 3.3 70B Versatile (Groq) Groq $0.59 Not published $0.79 128,000 Fits $0.318 $763.36 Official pricing
o4-mini (Reasoning) OpenAI $1.10 $0.275 $4.40 200,000 Fits $0.335 $803.26 Official pricing
Gemini 4 Hydrogen Google $1.50 $0.075 $7.50 1,048,576 Fits $0.371 $889.79 Official pricing
GPT-5 OpenAI $1.25 $0.125 $10.00 400,000 Fits $0.408 $978.03 Official pricing
Gemini 2.5 Pro Google $1.25 $0.125 $10.00 1,048,576 Fits $0.408 $978.03 Official pricing
Llama 3.3 70B Instruct Turbo (Together) Together AI $0.88 Not published $0.88 131,072 Fits $0.465 $1,117 Official pricing
Qwen 3.8 Max (Fireworks) Fireworks AI $2.00 $0.25 $6.00 1,000,000 Fits $0.470 $1,127 Official pricing
Claude Sonnet 5.5 Selected Anthropic $2.00 $0.1 $10.00 1,000,000 Fits $0.492 $1,181 Official pricing
GPT-4.1 OpenAI $2.00 $0.5 $8.00 1,047,576 Fits $0.603 $1,448 Official pricing
o3 (Reasoning) OpenAI $2.00 $0.5 $8.00 200,000 Fits $0.603 $1,448 Official pricing
Claude Sonnet 4.6 Anthropic $3.00 $0.3 $15.00 1,000,000 Fits $0.792 $1,900 Official pricing
Claude Opus 5.5 Anthropic $4.00 $0.2 $20.00 1,000,000 Fits $0.978 $2,347 Official pricing
GPT-4o OpenAI $2.50 $1.25 $10.00 128,000 Fits $0.988 $2,371 Official pricing
Claude Opus 4.6 Anthropic $5.00 $0.5 $25.00 1,000,000 Fits $1.315 $3,156 Official pricing
Need a model added or spotted a pricing update? Email contact@aiagentsarena.com View 11-step calculation logic →

Worked Example: Autonomous PR & Bug-Fix Engineering Agent

Autonomous software engineering agents consume far more tokens per task than customer-facing chatbots. Before writing a single patch, a coding agent ingests repository guidelines, bash/filesystem tool schemas, stack traces, test logs, and multi-file diffs—then iterates through build-test-fix loops until CI passes.

The pre-loaded Coding Agent preset models 80 engineering tasks per day using Claude Sonnet 5.5 ($2.00/1M input, $0.10/1M cached input, $10.00/1M output) with 35% of steps routed to Claude Haiku 5.5 ($0.10/1M input, $0.01/1M cached input, $0.50/1M output) for file discovery and log compression.

Base context starts at 7,500 tokens (3,500 coding instructions, 2,200 filesystem/shell tool schemas, 1,800 issue description and error trace). Across 12 steps, each file read or test log appends 2,800 tokens and each turn generates 900 output tokens of diffs and tool calls. By Step 12, peak context reaches 39,200 tokens, totaling 274,800 input tokens and 10,800 output tokens before retries. With 75% prompt caching, 15% retry overhead, 4 sandbox test runs at $0.001, an 85% autonomous pass rate, and P90 budgeting, the exact per-PR and monthly run rates update live above.

Why Coding Agents Hit Context Ceilings Quickly

  • Unfiltered Test & Build Output: A single verbose compiler trace or npm test dump can inject 15,000 tokens in one step. Truncating stdout to the failing assertions keeps step growth predictable.
  • Model Routing Gains: Offloading read-only repo search and lint log summarization to economy models cuts blended input and output rates without degrading the primary model's patch quality.
  • Framework Alignment: If you are building directly on Claude's filesystem and shell loop, check the Agent Framework Picker to compare the Claude Agent SDK against LangGraph and custom loops.

Next Steps for Engineering Teams

Compare coding economics against shorter operational workflows using the Customer Support Agent Cost Calculator or the Research Agent Cost Calculator. For concrete techniques to compact diffs and raise cache-hit ratios above 80%, read How to Reduce AI Agent Costs and Tokens and Context Windows Explained.

Coding Agent Cost FAQ

Why are coding agents so sensitive to prompt caching?

Autonomous coding loops resend large repository maps, coding standards, and file-edit tool schemas across 10 to 30 steps. Without prompt caching, re-reading 7,500+ base tokens plus growing diffs on every test iteration dominates the bill.

What drives the 15% retry overhead and 85% success rate in the coding preset?

Coding agents routinely hit syntax errors, failing unit tests, linter complaints, or patch-apply conflicts that require extra recovery steps—and roughly 10% to 20% of complex tickets still require human takeover.

Which models work best for coding agent routing?

Teams commonly pair a frontier coding model (such as Claude Sonnet 5.5, Claude Opus 5.5, or GPT-5) for architecture and patching with an economy model (Claude Haiku 5.5, GPT-5 nano, or DeepSeek V4.1 Flash) for log parsing, grep triage, and commit summaries.

How should I account for sandbox compute costs?

Enter your per-run container or microVM test-execution cost inside the External Tool / API Cost fields (for example, 4 test runs at $0.001 each) so both token and sandbox infrastructure spend appear in your monthly forecast.