Codex workflows on Amazon Bedrock with GPT‑5.6 Sol can see major cost spikes when explicit prompt cache controls aren’t sent. Cache writes cost 1.25×, while reads get a 90% discount—so missing cache breakpoints turns caching into churn instead of reuse.
OpenRouter lists GPT‑5.6 Sol at $2.50/1M input tokens and $15/1M output tokens during a 50% promotion, with 1M context and a Feb 2026 knowledge cutoff. ([openrouter.ai](https://openrouter.ai/openai/gpt-5.6-sol)) Real-world bills depend on provider routing and caching (fresh vs cached reads), which can shift your effective cost.
GPT‑5.6 Luna and Terra are cheaper, and GPT‑5.6 Sol adds Fast mode for lower latency. This post breaks down how token pricing, prompt caching, and inference/agent efficiency combine for better AI ROI in real workloads.
Kimi K3 and Claude Fable 5 aren’t just strong models by themselves. Per-task model routing lets an agent send the right work to the right model, hitting very high accuracy while cutting cost versus using Fable alone—largely thanks to how tokens, turns, and prompt caching behave in long loops.
Some coding agents arrive at the model already “loaded” with tens of thousands of tokens of system prompt and tool scaffolding. A side-by-side comparison found ~33k-token overhead for Claude Code versus ~7k for OpenCode, with prompt caching benefits depending on prefix stability.