Show HN: Does per-step reasoning effort save money in Claude Code?

Does letting Jev choose Claude Code's reasoning effort, step by step, save money? At high effort, barely: about 1%. At max effort, it cut the cost of a coding benchmark by 55% with every test still passing. In a long real session at max, the estimated saving was 4–9%, because re-reading the conversation's context is most of the bill. Changing effort on every step never reset the prompt cache.

A research project. The code works and the results are reproducible. Unofficial; not affiliated with Anthropic or TypeSafe.

Jev is TypeSafe's small, cheap decision model. After Astra-Ares used it to choose GPT-6 Astra's reasoning effort per step in Codex, several tools appeared doing the same for Claude. The pitch: most agent steps are routine, so run them at low effort and keep deep reasoning for the hard ones.

I built a proxy that does this for Claude Code without breaking the prompt cache, confirmed it works, then measured what it saves.

Effort changes how much the model thinks, and the saving follows thinking's share of the bill. At high, Claude Opus 5.5 thinks little on routine steps, so there's little to cut. At max, it thinks a lot even on routine steps, and Jev moves those down. But in a long session every step re-reads hundreds of thousands of tokens of context, which effort doesn't touch.

Claude Code caches the conversation so each step only pays full price for what's new. Changing effort mid-session used to throw that cache away. It no longer has to: Anthropic's per-message effort beta lets an effort-only system message change the level while leaving the cached prefix intact.

It held inside Claude Code. In the 470-step session with effort chosen on every step: 0 unexpected cache misses in 411 checked steps, and a 99.1% cache hit rate. With effort alternating low/high on every step of a test task, each request read the whole previous prefix from cache (26,992 → 34,285 → 35,017 → 35,270 → 35,617 tokens).

The catch: Claude Code already carries its own effort setting on a system message near the start of the conversation (beta per-turn-control-2026-07-01), and the latest setting in a request wins. An injected effort change has to be the last message, or it's silently ignored. Changing the request's top-level effort field is ignored the same way.

• Claude Code runs a whole session at one effort level (/effort : low, medium, high, xhigh, max).
• Opus 5.5, Fable 5.1, Mythos 5.1, and Opus 5 accept a per-message effort change : an effort-only system message insidemessages that leaves the cached prefix intact. So effort can change on every step without re-processing the conversation.
• Research on per-step effort selection (ARES ,TAB ) reports token savings of 35–53%. Both count tokens, not dollars. The open question was what per-step effort does to the total cost of real Claude Code sessions.

1. Mechanism. jev-effort runs claude behind a local proxy (ANTHROPIC_BASE_URL). Before each model request it sends Jev a trimmed view of the conversation (prompts, Claude's visible replies, the last six tool calls) and asks two questions: which effort the next step needs, and for how many steps to keep it. It applies the answer by appending an effort-only system message, and re-inserts earlier ones at their original positions so each request begins with the previous one. Jev may lower effort but never exceed the session's own setting.

2. Verification. On a hard counting problem, a low marker produced 438–548 output tokens and max produced 1,528–1,809, in line with Claude Code's own --effort (438 and 1,733). With effort alternating on every step, each request read the full previous prefix from cache. In the real session: 0 unexpected cache misses in 411 checked steps, 99.1% cache hit rate.

3. Controlled benchmark. Six small coding tasks, each with a visible test and hidden checks. Each task ran from identical files twice: once at a fixed effort, once with Jev choosing under that same effort as its ceiling. Two rounds at high and two at max, 48 headless Claude Code sessions in all. A warm-up run came first so neither arm paid to cache the other's system prompt.

4. Shadow mode on real work. One 92-minute session of my normal work at max effort (470 model steps, Opus 5.5, Claude Code 2.1.280). For every step, Jev's choice was logged and nothing was changed. Each request was priced at Claude API list prices from the usage the API reported: cache reads, cache writes, output, and hidden thinking.

Wall time fell from 913 s to 387 s. One task dominates: on expr-eval, max thought for about 27,000 tokens per run and Jev's low and medium settings passed the same hidden tests with about 280. Without expr-eval, cost still fell 21%.

Compared with just using high. The same six tasks at a fixed high cost less than fixed max, so the fair question is whether Jev beats simply turning effort down. On these tasks it matched or beat it. The runs were separate, so treat this as a rough comparison:

Opus 5.5 list prices: $0.20 per million tokens read from cache, $8 per million written to the one-hour cache, $20 per million output tokens. I'm on a subscription, so these are API-equivalent figures, not a bill.

The session averaged 441K tokens of context per step, re-read every time.

Jev would have lowered effort on all 470 steps: to high on 261, xhigh on 149, low on 51, and medium on 9. It never kept max.

The max benchmark's cut is likely too high for this session: there Jev mostly chose low and medium, while here it mostly chose high and xhigh. The break-even is thin. One extra step costs about $0.09 to re-read the context, so the net saving equals 30–67 extra steps across 470. If lower effort makes Claude take more steps than that, it loses money. Shadow mode can't observe that; the benchmarks found step counts roughly unchanged (92 vs 87 turns at max).

• The mechanism works. Effort changes take hold, the cache survives, and every hidden test passed in both arms at both effort levels.

• At high, it doesn't pay. Opus 5.5 at high spends little on thinking during routine steps, so a 46% thinking cut moved total cost by about 1%.

• At max, it can pay. max spends heavily on thinking even when the step doesn't need it. With short contexts the saving is large (55% on the benchmark, and faster). In a long session the same kind of cut is a few percent of the bill, because context dominates.

• It isn't much better than turning effort down yourself. On the benchmark, Jev under amax ceiling cost about the same as fixedhigh . What it adds is keepingmax available for the steps that need it, which small tasks can't show.

• It adds latency, but may not cost time. Jev took 601 ms median and 793 ms at p95 per decision, with no errors. Atmax , less thinking more than made up for it: the Jev arm finished the benchmark in 42% of the time.

• Quality at max on real work is untested. The benchmark tasks are small and well specified. If you runmax because your work needs it, lowering effort may cost more than it saves.

• One real session, at max , 92 minutes. There's no real-session measurement athigh .

• The benchmark tasks are small, and two runs per task shows direction, not a precise effect size. One task accounts for most of the max result.

• Shadow mode can't measure quality or extra steps on real work, so the real-session saving is a range, not a measurement.

• Behind any proxy, Claude Code drops a few direct-connection features, including claude.ai-backed tools such as Artifacts, so each request carried about 4K fewer tokens than a direct session would (details ).

• Dollar figures apply API list prices to subscription usage. How plan limits weigh each token type isn't published.

Found while building this; details and evidence in docs/how-it-works.md.

• Claude Code re-sends some messages as a plain string after first sending them as text blocks. The API caches both the same way, but anything that hashes the history must normalize them.
• Any custom ANTHROPIC_BASE_URL makes Claude Code turn off MCP tool search, every MCP tool definition into every request. With several MCP servers, a fresh session started at 281K–500K tokens instead of 27K.ENABLE_TOOL_SEARCH=true restores it when the proxy forwardstool_reference blocks.
• Claude Code 2.1.260+ has early-access "function hooks" (CLAUDE_CODE_ENABLE_FUNCTION_HOOKS=1 ). Aturn.step hook can rewrite effort per request, and Claude Code then inserts the same cache-safe markers itself.

npx jev-effort --jev-shadow              # use like `claude`; logs Jev's choices, changes nothing
npx jev-effort stats                     # where your spend goes and what Jev would save
npx jev-effort stats --share             # the same, safe to paste (no prompts or ids)
npx jev-effort bench --effort max --runs 2   # the controlled comparison (uses your Claude usage)

Setup, configuration, and privacy details: docs/usage.md. The data behind this page: real session at max, benchmark at max, and benchmark at high. If your numbers look different, please open an issue with jev-effort stats --share.

None of these, as of September 23, 2026, reports total cost against a fixed-effort baseline.

MIT. See THIRD_PARTY_NOTICES.md for the Astra-Ares attribution.