Anthropic · Mid-tier · US$3.00 input/1M · US$15.00 output/1M · 200K context
Typical monthly cost
US$71.23
≈ per day
US$3.24
Blended cost/1M
US$3.65
Context window
200K
Claude Sonnet 3.7 from Anthropic is a mid-tier model priced at US$3.00 per 1M input tokens and US$15.00 per 1M output tokens. For a typical solo-developer workload (8 hours/day, 22 days/month — 1 medium feature, 5 small bug fixes, 4 PR reviews, 2 stack-trace debugs, ~1500 lines of TypeScript, 1 large-doc read, with prompt caching at the default mix) Claude Sonnet 3.7 costs about US$71/month. The 200K-token context window covers single-file workloads comfortably.
Move the slider or switch task mix — values update live.
Monthly budget
US$100 / month
≈ $23/wk · ≈ $4.55/day
on Claude Sonnet 3.7
Input tokens
51.5M
Output tokens
1.0M
Total tokens
52.5M
Per month this budget delivers
At the default coding-agent mix with 50% cache hits.
The 22-day month is based on the median working-day count across DE/US.
| Activity | Count | Per task | Daily | Monthly |
|---|---|---|---|---|
| Medium feature (10–15 files) | 1 | US$1.23 | US$1.23 | US$26.98 |
| Small bug fix | 5 | US$0.09 | US$0.43 | US$9.41 |
| PR review | 4 | US$0.09 | US$0.38 | US$8.32 |
| Debug from stack trace | 2 | US$0.21 | US$0.43 | US$9.44 |
| Read a large doc | 1 | US$0.12 | US$0.12 | US$2.64 |
| Micro-interaction (explain / lint fix) | 30 | US$0.00 | US$0.14 | US$3.07 |
| Lines of TypeScript | 1,500 | US$0.00 | US$0.52 | US$11.39 |
| Total | US$3.24 | US$71.23 | ||
The 1500-lines-of-TS row models ~1000 lines read (cache-hit) + ~500 lines written. Headline figures are precise to ~5% — see the FAQ.
What each monthly budget buys on this model (typical solo-developer day, 22 working days).
| Monthly budget | Medium features | PR reviews | Debug sessions | Lines of TS |
|---|---|---|---|---|
| Typical (≈ $71) | 58 | 753 | 332 | 206,465 |
| $50/month | 40 | 529 | 233 | 144,927 |
| $200/month | 163 | 2,116 | 932 | 579,710 |
| $500/month | 407 | 5,291 | 2,331 | 1,449,275 |
| $2000/month | 1,630 | 21,164 | 9,324 | 5,797,101 |
Typical mix: coding-agent (85% input, 50% cache hits). Values show the maximum count of each task type at that budget.
Coding
Trained or post-trained for code generation tasks.
Reasoning
Strong multi-step reasoning over complex prompts.
Multimodal
Accepts images alongside text.
Prompt cache
Cache reads billed at ~10% of input price — cuts agent costs sharply.
Batch API
50% off when you accept up to 24-hour turnaround.
Tool use
Native function-calling / tool-use API support.
Long context
Not supported
Extended thinking
Hidden reasoning tokens (Anthropic 'thinking' / OpenAI reasoning).
Total models
5
Median input/1M
US$3.00
Median output/1M
US$15.00
Input range
US$1.00–US$15.00
Verified: 2026-05-07
Running the realistic solo-developer day (1 medium feature + 5 small bug fixes + 4 PR reviews + 2 debug sessions + ~1500 lines of TypeScript + 1 large-doc read, 22 working days) on Claude Sonnet 3.7 costs about US$71/month. Heavier workloads scale proportionally; lighter workloads cost less.
200K tokens total, with up to 8K of output. That fits a few dozen source files in a single call.
Providers charge US$15.00 per 1M output tokens against US$3.00 per 1M input — output requires real compute, input comes mostly from cache. Coding agents read many files (input-heavy) and emit compact diffs (low output), so total spend is usually input-driven.
Cache reads typically cost only 10% of the regular input rate. On a coding-agent mix with 50% cache hits, that saves roughly 45% on input — which is about 38% off your total bill on input-heavy workloads. Anthropic models charge a one-time cache-write surcharge (25% over input) that pays for itself after 2–3 hits.
Extended-thinking / reasoning tokens are billed at the full output rate but never appear in your visible response. On hard agentic tasks they can double your output bill, lifting the monthly total by 20–30%. Enable thinking only when the standard response visibly fails.
Yes, if you can tolerate up to 24-hour turnaround: batch input/output are 50% cheaper than real-time rates. Perfect for nightly code reviews, bulk refactors or pre-merge analysis — wrong for inner-loop editing where you need an answer in seconds.
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