First-Gen vs Second-Gen AI Coding Agents: The Token Math That Decides Who Wins

The AI coding agent market looks like a feature war. It’s actually an architecture war, and the battlefield is tokens. Two agents running the exact same model can differ by 4x in what a working day costs — not because of the model, but because of three design decisions made before your code ever reaches the LLM.

This guide maps the generation gap. The specific numbers below are directional estimates, clearly labeled — exact token counts vary by version and task. The structure is what matters, and the structure is measurable.

The three architectural decisions

1. System prompt weight: 20k tokens vs 2k

First-generation agents ship a system prompt stuffed with instructions, tool descriptions, and guardrails — commonly 15–25k tokens, re-sent as the conversation rolls. Second-generation agents compress to roughly 1–3k tokens and push the bulk of behavioral context into cheap, cacheable files (think AGENTS.md loaded once).

At scale that difference is a tax on every single turn: 20k × every message in a session that doesn’t cache.

2. File reading: paste vs parse

When a 1st-gen agent “reads” your codebase, it often stuffs whole files into context. A 2nd-gen agent parses the code first — AST-level tools (tree-sitter and friends) — and reads only the relevant symbols. Asking about one function shouldn’t cost you the whole file, and in 2nd-gen tools it doesn’t.

3. Prompt caching: the 10x lever

Prompt caching is the single biggest cost lever in agent economics. When the prefix of a request is byte-identical to a previous one, providers serve the cached portion at roughly 10% of list price. Native, disciplined caching turns the repeated-context problem into a non-problem.

The catch: the cache requires byte-identical prefixes. Which brings us to the trap most agents still fall into.

The summarization trap

When a session grows long, many agents “compact” it — summarize the history and continue with the summary. Every compaction changes the prefix, invalidating the entire cache. You pay full price to rebuild context that was, seconds ago, nearly free.

Second-generation architectures avoid this by not rewriting history in-band: state goes to external files, references stay stable, the prefix stays identical, the cache stays hot. It sounds like a bookkeeping detail. It’s frequently the difference between a $5 day and a $20 day on identical work.

What this means when you pick a tool

• Ask where the system prompt lives. Bulky prompt + no caching = 1st-gen economics, whatever the marketing says.
• Test with a real half-day task, not a demo prompt. Measure tokens spent (your provider dashboard shows it). That single number predicts your monthly bill better than any pricing page.
• BYOK multiplies the advantage. A 2nd-gen agent on your own key is the cheapest credible setup we measure: raw token prices, no subscription markup, cache discounts included.

The generation labels aren’t about age — a young tool can be 1st-gen under the hood, and the “2nd Gen Token Architecture” question is the first thing we check when scoring a new agent.

FAQ

How do I check an agent’s token architecture quickly? Open a provider dashboard, run the same 30-minute task on two agents with the same model, compare spend. A 2–4x gap answers the question faster than any documentation.

Does 2nd-gen mean lower quality? No — it means cheaper per unit of quality. The top of our quality rankings is architecture-agnostic; the top of the value rankings is dominated by 2nd-gen + BYOK.

Is prompt caching available everywhere? Major providers support it, with different discount depths and TTLs. It must also be used by the agent — architecture, not just API availability, decides.