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

A research project found that letting TypeSafe's Jev decision model pick Claude Code's reasoning effort per step cut the cost of a coding benchmark at `max` effort by 55% with every test still passing, while the same setup at `high` effort saved only about 1.1%. In a 470-step real session at `max` effort, the estimated saving was 4–9% because cached context re-reads accounted for 82% of spend, though the proxy held a 99.1% cache hit rate with 0 unexpected cache misses in 411 checked steps. The project is unofficial and not affiliated with Anthropic or TypeSafe.

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 https://openrouter.ai/typesafe/jev-1.13 is TypeSafe's small, cheap decision model. After Astra-Ares https://github.com/miuuyy/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. | Test | Result | |---|---| | Benchmark at high 24 sessions, hidden tests | Cost −1.1% , thinking −46%, every test passed in both arms | | Benchmark at max 24 sessions, hidden tests | Cost −55% −21% without its one outlier task , thinking −98.5%, wall time −58%, every test passed in both arms | | Real session at max , shadow mode 470 steps, 92 min | Estimated saving 4–9% . Cached context was 82% of spend, thinking 9.5% | 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 https://platform.claude.com/docs/en/build-with-claude/effort change-effort-mid-conversation-beta 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 https://platform.claude.com/docs/en/build-with-claude/effort change-effort-mid-conversation-beta : an effort-only system message inside messages 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 https://arxiv.org/abs/2603.07915 , TAB https://arxiv.org/abs/2604.05164 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. | Task | Pass fixed / Jev | Cost fixed / Jev | Thinking tokens fixed / Jev | Jev's effort mix | |---|---|---|---|---| | expr-eval | 2/2 / 2/2 | $2.00 / $0.32 | 54,397 / 551 | low 5, medium 4 | | interval-merge | 2/2 / 2/2 | $0.36 / $0.25 | 2,907 / 45 | low 5, medium 4 | | lru-cache | 2/2 / 2/2 | $0.39 / $0.23 | 2,963 / 0 | low 3, medium 4 | | paginate-bug | 2/2 / 2/2 | $0.35 / $0.28 | 2,788 / 45 | low 4, medium 5 | | rename-refactor | 2/2 / 2/2 | $0.38 / $0.36 | 1,335 / 313 | low 12, high 2 | | stats-bugs | 2/2 / 2/2 | $0.27 / $0.28 | 509 / 0 | low 6, medium 4 | | Total | 12/12 / 12/12 | $3.76 / $1.70 −55% | 64,899 / 954 −98.5% | | 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: | Same six tasks, two runs each | Cost | Thinking tokens | Wall time | Passed | |---|---|---|---|---| | Fixed max | $3.76 | 64,899 | 913 s | 12/12 | | Fixed high | $1.94 | 1,450 | 393 s | 12/12 | | Jev, capped at high | $1.92 | 787 | 417 s | 12/12 | | Jev, capped at max | $1.70 | 954 | 387 s | 12/12 | | | Fixed high | Jev | Change | |---|---|---|---| | Hidden checks passed | 12/12 | 12/12 | | | Thinking tokens | 1,450 | 787 | −45.7% | | Output tokens | 20,281 | 20,515 | +1.2% | | Cost | $1.94 | $1.92 | −1.1% | | Wall time | 393 s | 417 s | +6% | 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. | Category | Spend | Share | |---|---|---| | Cache reads | $41.05 | 63.9% | | Cache writes | $11.51 | 17.9% | | Hidden thinking | $6.10 | 9.5% | | Visible output | $5.13 | 8.0% | | Uncached input | $0.45 | 0.7% | | Total | $64.23 | | 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 . | | Amount | Share of spend | |---|---|---| | Ceiling: all thinking removed on those steps | $6.10 | 9.5% | | Estimate at the high benchmark's thinking cut 46% | $2.80 | 4.4% | | Estimate at the max benchmark's thinking cut 98.5% | $6.01 | 9.4% | | Jev's own cost 2.9M input tokens | −$0.12 | | | Net | $2.68–$5.88 | 4.2–9.2% | 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 a max ceiling cost about the same as fixed high . What it adds is keeping max 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. At max , 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 run max 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 at high . - 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 https://github.com/ifoster01/jev-effort/blob/main/docs/usage.md limitations . - 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 https://github.com/ifoster01/jev-effort/blob/main/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, loading 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 forwards tool reference blocks. - Claude Code 2.1.260+ has early-access "function hooks" CLAUDE CODE ENABLE FUNCTION HOOKS=1 . A turn.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 https://github.com/ifoster01/jev-effort/blob/main/docs/usage.md . The data behind this page: real session at max https://github.com/ifoster01/jev-effort/blob/main/docs/results/2026-09-23-max-session.json , benchmark at max https://github.com/ifoster01/jev-effort/blob/main/docs/results/2026-09-23-opus-5-5-max.json , and benchmark at high https://github.com/ifoster01/jev-effort/blob/main/docs/results/2026-09-23-opus-5-5-high.json . If your numbers look different, please open an issue with jev-effort stats --share . | Project | Approach | |---|---| | Astra-Ares https://github.com/miuuyy/Astra-Ares | The original: Jev picks GPT-6 Astra's effort per step in Codex. jev-effort adapts its Jev prompt wording MIT | | jev-opus https://github.com/WXK-AI/jev-opus | Same proxy approach for Claude Code, plus work-phase adjustments | | jev-model-router https://github.com/moelahmady/jev-model-router | Claude Code function hooks; effort per prompt, optional model routing | | effort-router https://github.com/handpickedlab/effort-router | Claude Code plugin; effort per task | | ARES https://arxiv.org/abs/2603.07915 , TAB https://arxiv.org/abs/2604.05164 | Research on per-step and per-turn reasoning budgets | None of these, as of September 23, 2026, reports total cost against a fixed-effort baseline. MIT. See THIRD PARTY NOTICES.md https://github.com/ifoster01/jev-effort/blob/main/THIRD PARTY NOTICES.md for the Astra-Ares attribution.