Q
What dominates my AI coding bill?
Input re-billing across turns, typically 90%+ of session tokens. Output is the receipt, input is the bill.
Glossary · AI Coding Token Costs (2026)
AI Coding Token Costs (2026).
Price the input, not the prose.
Direct answer
2026 reference pricing: Claude Sonnet-class input ~$2/M tokens, Opus-class ~$5/M; cached input ~10% of fresh. A 200-turn session re-bills its context every turn, so a 50K-window habit costs ~10M tokens per session before output. The bill equation is per-turn-context × turns, which is why packing (smaller context) beats prompting (cleverer words) by orders of magnitude. Refresh yearly; providers reprice.
In Anvaya
How We Implement It.
01AI Coding Token Costs (2026)Anvaya attacks both factors: 2–4K packed turns (vs ~50K raw) and cache-stable standing context, the 8.59M-token session becomes a ~1M-token one by construction.
Questions
Asked About AI Coding Token Costs (2026).
Q
Cache vs memory: which saves more?
Caching discounts price (~90% off stable tokens); memory shrinks quantity, the 12–25x per-node ratio is a design estimate, and the measured end-to-end lever today is −11.0% median uncached (micro-fold, n=5). Stack them.
Q
How do I estimate a session?
Per-turn packed context × expected turns ÷ cache-hit share. Measure one week with /cost-style accounting, then project.
Run Agents That Fit On Your Laptop.
25.6 MB median RSS. 25 agents ran in parallel on a Core 2 Duo with 4 GB RAM. Hundreds on your machine. Zero cloud required on the Ollama path.
Requires Rust/cargo to build from source. Linux and macOS today, Windows not yet supported. Pre-1.0, public beta. Pricing TBD.