Research: evaluate Claude Fable 5 / Mythos 5 for wizard model recommendation

[BaseInfinity]

Context

Claude Fable 5 and Mythos 5 launched June 9, 2026 — the first public Mythos-class model. This is a research/evaluation issue, not an adoption issue. Same discipline as the Opus 4.8 evaluation (#365): field signal first, recommendation second.

What we know so far (launch day)

Benchmarks (positive):

• SWE-bench Verified: 95.0% vs Opus 4.8's 88.6%
• SWE-bench Pro: 80.0% vs 69.2%
• FrontierCode Diamond: 29.3% vs 13.4% (2× Opus 4.8)
• "Even at medium effort, Fable 5 outperforms every other model at any effort level"
• Stripe: compressed months of engineering into days on a 50M-line Ruby codebase

Token efficiency (potentially positive):

• HN early testers: "better results with about half the tokens, making it cost ~same as Opus 4.8 price-wise"
• $10/$50 per Mtok (2× Opus), but fewer turns → similar effective cost per task

Concerns (need monitoring):

• Safety classifiers "super aggressive and sensitive" for benign coding tasks — fallback to Opus 4.8 on sensitive queries (not 4.6)
• "Didn't really notice a difference vs 4.8" for standard conversation/assistant tasks
• Free on Max through June 22 only — credits may be required after June 23
• No community field data yet (launched TODAY)
• No Andon Labs / Vending-Bench data
• No effort-level regression data (does max overthink like 4.8?)

What the wizard should evaluate before recommending

Same gates as #365, but this time actually run them:

1. Gate 1: proof of life — does claude --model claude-fable-5 resolve? Does /model show it in the picker?
2. Gate 2: A/B coder quality — run the same task on Fable 5 vs 4.6 max on a real PR. Compare token spend, quality, context exhaustion
3. Gate 3: dogfood for 24h — maintainer runs Fable 5 as daily driver for a full day
4. Gate 4: effort-level behavior — does max overthink on Fable 5 like it does on 4.7/4.8? Or does it behave more like 4.6?
5. Gate 5: community signal (7-day wait) — monitor HN, Reddit, GitHub issues for field reports after launch-day hype settles
6. Gate 6: Andon Labs / independent benchmarks — wait for Vending-Bench arena results

Current wizard state

v1.80.0 recommends Opus 4.6 max as flagship default. That was the right call based on 12 days of 4.8 post-launch data. Fable 5 is a new ceiling — but it's also $10/$50 (2× the cost) and day-zero. No rush.

Pricing tier question

If Fable 5 validates, it would be a new tier above flagship (maybe "Frontier" or "Premium+"), not a replacement for 4.6 max. The $10/$50 pricing makes it a conscious choice, not a default.

Timeline

• June 9-22: free trial window on Max plans (13 days to test at no cost)
• June 23+: may require credits — pricing becomes load-bearing
• June 16+ (7 days post-launch): earliest to evaluate community signal
• July: earliest to consider a wizard recommendation if all gates pass

Related

• Hold Opus 4.7 → 4.8 model-pin bump 48-72h for sentiment, then A/B via local-shepherd #365 — Opus 4.8 evaluation (shipped as v1.78.0, then reverted to 4.6 max in v1.80.0)
• v1.80.0 CHANGELOG "reversion criteria" section

[BaseInfinity]

Day-1 Deep Research: Community Sentiment on Fable 5 + Opus 4.8 Latest (2026-06-09)

Fable 5 — Launch Day Signal

Positive

Benchmarks are real:

• SWE-bench Pro: 80% vs Opus 4.8's 69.2% (+10.8 pts)
• FrontierCode Diamond: 29.3% vs 13.4% (>2×) — "as close as a benchmark gets to what a coding agent does in production"
• "Even at medium effort, Fable 5 outperforms every other model at any effort level" (system card p.256)
• Stripe: compressed months of engineering into days on 50M-line Ruby codebase

Token efficiency (potentially offsetting 2× price):

• HN early testers: "better results with about half the tokens, making it cost ~same as Opus 4.8 price-wise"
• "More surgical code changes, less non-necessary changes, smaller PRs for review"
• One customer: completed frontier physics task in 36 hours using 1/3 the reasoning tokens of GPT-5.5 over 4 days

Quality:

• "Writes more maintainable code without explicit human steering"
• One dev solved a reverse engineering challenge in 30 min that "neither Claude Code 4.8 or ChatGPT Codex 5.5 could figure out"
• "Correctly flagged everything that was done, somewhat done, and missing" across 50 pages of specs

Effort scaling works:

• Performance climbs from 11.5% (low) to 30.9% (max) on FrontierCode Diamond — unlike GPT-5.5 which stays flat near 5-6% regardless

Negative

Safety filters are the headline complaint:

• On Terminal-Bench, 20.9% of Fable trials hit a safety refusal and fell back to Opus 4.8 mid-trajectory
• The word "cancer" triggers biosecurity blocks, even for "code a website on cancer mutations"
• Users can't "upload genetic files" or ask basic biology questions
• A dev approved for Anthropic's cyber program still got downgraded to 4.8 on security test code
• 62.2% of commenters on Digg expressed negative sentiment about the filters
• "Near impossible to tap into advanced capabilities due to extremely sensitive safety filters"
• Fallback target is Opus 4.8 (with all its documented regressions), not 4.6

Cost:

• $10/$50 per Mtok — 2× Opus. "Burns through tokens at a rate that suggests the answer to how much it costs in production is 'a lot'" — Ethan Mollick
• Free on Max through June 22 only — after that, pricing becomes load-bearing

Limited general improvement:

• "Didn't really notice a difference vs 4.8" for standard conversation/assistant tasks
• The wins are specialized (complex multi-file, long-horizon agentic work)

Philosophical:

• Ethan Mollick: "I no longer steer; I commission. The conjuring happens somewhere I cannot watch, in hundreds of small choices I never get a vote on." Loss of control/transparency is the new tradeoff.

What we DON'T have yet (monitor for these)

• Andon Labs Vending-Bench arena results
• Reddit r/ClaudeAI multi-day field reports (only hours of data so far)
• Effort-level regression data (does max overthink like 4.8?)
• Token-spend comparison vs Opus 4.6 max on identical tasks
• GitHub issue count for Fable-specific regressions (none filed yet — too new)
• How the June 22 → credits transition actually plays out

---

Opus 4.8 — Day 12 Update (still bad, no fixes shipped)

Token burn — unchanged since last check:

• 29+ GitHub issues still open for token burn since May 28
• #64153: 46K tokens for simple coding turn — unfixed
• #64359: burns 5-hour limit in ~30min — unfixed
• 40-60× cache token jump at HIGH effort — structural/by design
• "Deliberately less efficient tokenizer" per tech.yahoo — confirmed as intentional

No Anthropic response:

• No postmortem like the April 23 one for 4.6
• No hotfix announcement
• No acknowledgment of the token-burn regression

Practical implication: Fable 5's safety fallback goes to 4.8 — meaning ~20% of Fable sessions inherit 4.8's token-burn regression. Until 4.8's issues are fixed, Fable's effective quality is ceil(Fable, 0.8) + floor(4.8, 0.2).

---

What this means for the wizard (maintainer assessment)

v1.80.0's call (Opus 4.6 max) is reinforced, not challenged:

1. Fable 5 is impressive but day-zero — we learned from 4.8 that launch-day benchmarks ≠ field reality
2. 20.9% safety-fallback-to-4.8 rate is a real problem for SDLC workflows that touch security (hooks, CI, auth)
3. $10/$50 pricing puts it in a different tier — not a replacement for the $5/$25 flagship
4. Opus 4.8 is still unfixed 12 days post-launch — Anthropic's attention is now on Fable/Mythos, making a 4.8 fix less likely short-term
5. 4.6 max remains the only Opus where max effort + Max subscription + no regressions all hold true

Recommended gate schedule:

• June 16 (7 days): Check community signal. Are the safety-filter complaints growing or shrinking? Any Andon Labs data?
• June 22 (13 days): Free trial ends. Post a second research note with accumulated field data
• June 23+: If credits required, pricing becomes a real decision. Evaluate whether a "Frontier" tier is justified
• July: Earliest to consider a wizard recommendation if gates 2-6 pass

---

Sources

• HN: Fable 5 first impressions
• Ethan Mollick: What it feels like to work with Mythos
• Digg: Fable 5 safety filters block benign academic queries — 62.2% negative sentiment on filter aggressiveness
• Anthropic: Fable 5 + Mythos 5 announcement
• DigitalApplied: Agentic coding deep dive
• TrueFoundry: Fable 5 vs Opus 4.8
• Anthropic: Refusals and fallback docs
• Claude Help Center: Why Claude switched models with Fable 5
• GitHub: #64961, #64153, #64359 — Opus 4.8 token burn (still open)

[BaseInfinity]

Fable 5 + Opus 4.8 Sentiment Research (2026-06-09)

Fable 5 / Mythos 5 — Day-Zero Reactions

• Benchmark reception overwhelmingly positive. 80.3% SWE-Bench Pro (11pts ahead of next model). Stripe tested on 50M-line Ruby codebase — "compressed months into a single day."
• Cost concern is real. $10/$50 per Mtok (2x Opus 4.8), adaptive thinking always on, sessions routinely hit 500K-1M tokens.
• Consensus: exceptional for heavy agentic work, overkill for simple tasks. Not all signal positive — Andon Labs reported Mythos 5 underperformed 4.7 and GPT-5.5 on one financial benchmark.

Opus 4.8 — Still Mixed

• Overthinking remains top complaint. Extended reasoning in max mode causes model to revisit/reverse correct initial judgments. "Smarter but lazier than 4.7."
• June 8 regression report (GitHub #66539): ignoring CLAUDE.md, bypassing permissions, hallucinating. May be transient.
• Simon Willison: "a modest but tangible improvement" over 4.7 on benchmarks, but real-world gap is wider.

4.6 vs Peers — Coding Performance

• Benchmarks favor 4.8: SWE-Bench Pro 4.6=53.4%, 4.7=64.3%, 4.8=69.2%
• Real-world stability favors 4.6: Avoids extra latency/token burn, "lazy coding" complaints absent
• The wizard's 4.8→4.6 flip aligns with practitioner sentiment

Verdict: v1.80.0 Opus 4.6 max is well-supported. Fable 5 too new and expensive — revisit after June 22 (free window closes).

Next check: June 16 (day 7).

[BaseInfinity]

Action plan: Fable 5 trial during free window (ends ~June 22)

The free trial window closes June 22. We should test Fable 5 in our lanes before then.

Proposed trial

1. Add [b] Fable 5 (trial) to setup/SKILL.md Step 9.5 — temporary opt-in choice
2. Test in Setup A (Premium) position — Fable as planner+driver, GPT-5.5 xhigh reviewer
3. Effort levels to test: Fable supports adaptive thinking (always on), no effort knob like Opus. Test at default.
4. Cost concern: $10/$50 per Mtok (2x Opus 4.8). Free on Max through June 22 — this is the window.
5. Key metric: does it outperform Opus 4.6 max on real SDLC work (not benchmarks)?

What to evaluate

• Does Fable's 20.9% safety-fallback-to-Opus-4.8 rate cause issues?
• Token burn: does adaptive thinking blow context faster than Opus 4.6 max?
• Quality: do Codex reviews find fewer P1s on Fable-authored code vs Opus 4.6?
• Stability: does it follow wizard instructions as reliably as 4.6?

Timeline

• June 16: day-7 sentiment check (already scheduled)
• June 16-22: trial window — run real SDLC work on Fable in 1-2 repos
• June 22: free trial ends — decide adopt/defer/reject
• If rejected, remove the trial option in next version bump

If adopted

• New lane: Setup A+ (Fable Premium) — Fable planner+driver, GPT-5.5 reviewer
• Setup A stays as Opus 4.6 max (stable default)
• Cost warning: $10/$50 per Mtok post-trial, 2x Opus

[BaseInfinity]

Day-0 Reddit field data (r/ClaudeCode, 100+ upvote thread)

Consensus: "4.6 on steroids"

• Less verbose than 4.8 — "quiet confidence", doesn't announce every action like a Victorian theatre actor
• Better at orchestration — spawns 10 subagents, coordinates multi-agent workflows natively
• Finds bugs others miss — one user: "found P0 + P1s that 4.6 and 4.8 missed over 10+ reviews"
• Faster to get moving — less planning verbosity, just starts working

Concerns

• Token hungry — 87% of 5hr Max limit on single audit prompt, 35% in under 1hr for code review
• Safety guardrails aggressive — falls back to Opus 4.8 for health/security/biology topics. Can't do security reviews. "Once it decides your session conflicts with guardrails there's no going back"
• Max 5x users hit limits fast — some burned entire 5hr window in 25 minutes on ultracode
• Not universally better — some say "marginal improvement", "very underwhelming", "0 for 1 on easy task"

Implications for wizard lanes

1. Fable as reviewer (Setup A+ idea): strongest signal. "Found things 4.6 and 4.8 missed." Could replace or supplement GPT-5.5 xhigh as cross-model reviewer.
2. Fable as planner: promising — deep reasoning, less verbose. But safety fallback to Opus 4.8 during planning could break SDLC workflow.
3. Fable as full driver: too expensive for daily work. Token burn makes it impractical on Max 5x.
4. Safety fallback is a blocker: 20.9% fallback rate means 1 in 5 sessions could silently downgrade to Opus 4.8 mid-work. Can't do security reviews at all.

Updated recommendation

• Trial as reviewer only during free window (June 9-22). Replace GPT-5.5 xhigh with Fable for a few reviews and compare finding quality.
• Do NOT trial as full driver — token economics don't work on Max 5x.
• Safety fallback makes it unsuitable as primary model until Anthropic addresses it.

[BaseInfinity]

Revised lane strategy: Fable as premium reviewer/planner, NOT driver

Based on field data, Fable's sweet spot is reasoning-heavy roles (review, planning, audit) not implementation:

Proposed: Setup A+ — Fable Premium (reviewer/planner only)

Role	Model	Why
Planner	Fable 5	Deep reasoning, less verbose, catches architectural issues early
Driver	Opus 4.6 max	Stable, predictable, Max-bundled, no token burn
Reviewer	Fable 5	"Found P0/P1s that 4.6 and 4.8 missed over 10+ reviews" — strongest signal

Fable does the thinking (plan + review), Opus 4.6 does the building. Fable never touches implementation — avoids the token burn and safety fallback problems.

Why NOT Fable as driver

• Token burn: 87% of 5hr limit on single audit
• Safety fallback: 20% of sessions silently downgrade to Opus 4.8
• Overkill: implementation doesn't need Mythos-class reasoning

Trial plan (June 9-22 free window)

1. Run 3-5 Fable reviews via claude --model claude-fable-5 --print on recent diffs
2. Compare finding quality vs GPT-5.5 xhigh (our current reviewer)
3. Try Fable as planner on 1-2 architectural tasks
4. Track token consumption per review/plan
5. Decision by June 22: adopt as premium reviewer/planner tier or reject

If adopted post-trial

• Setup A+ sits ABOVE Setup A (Premium) in the lanes hierarchy
• Only for high-stakes work where the extra review depth justifies the cost
• $10/$50 per Mtok post-trial — budget accordingly

[BaseInfinity]

Trial approach: start with planner, then evaluate reviewer

Phase 1: Fable as planner (first)

• Use Fable for PLANNING phase on 2-3 real tasks
• GPT-5.5 xhigh reviews the Fable-planned + Opus-implemented result as usual
• If GPT-5.5 can't find issues → Fable planning is solid

Phase 2: flip reviewer (if Phase 1 passes)

• GPT-5.5 xhigh reviews first (baseline)
• Then Fable reviews the same diff
• Compare: does Fable catch things GPT-5.5 missed?
• If yes → Fable becomes the premium reviewer tier

Effort levels to test

• Fable has adaptive thinking (always on) — no traditional effort knob like Opus
• But test at high, xhigh, max to see if it responds differently
• Track token burn at each level

Decision tree

Fable plans → GPT-5.5 finds nothing wrong?
  YES → Fable is a good planner, keep it
  NO  → check if GPT-5.5 findings are real or false positives

Fable reviews → finds stuff GPT-5.5 missed?  
  YES → Fable is a better reviewer, promote it
  NO  → GPT-5.5 stays as reviewer, Fable stays planner-only

Not rushing this — free window ends June 22, plenty of time to fiddle.

[BaseInfinity]

r/codex field data: Fable vs GPT-5.5 (98 comments, 167 upvotes)

Confirms our planner+reviewer lane strategy

• "I planned a difficult project with Fable 5 (architect) and 5.5 Xhigh (reviewer) — really impressed"
• "I will be exclusively using it for brainstorming and reviews"
• "break glass in case of emergency model — not day to day"
• "you're better off doing deep research and having 5.5 xhigh planning than having fable as driver"

Effort level insight (challenges our always-max stance)

Top comment (36 upvotes): effort = breadth, not depth

• Low effort → better for tightly scoped deep logic (surgical fixes)
• High effort → better for broad search across many files
• "Most people far overuse effort"
• Implication: Fable on HIGH or XHIGH might be smarter than MAX for focused planning tasks. Test all three during trial.

Fable vs 5.5 comparison

• Fable better: architecture, security review, finding deep bugs, complex reasoning
• 5.5 better: daily driver, token efficiency, following instructions precisely, less over-engineering
• Fable found P0/P1s that both 5.5 and 4.8 missed
• Token burn: ~2x Opus 4.8, burns through 5hr Max limit fast
• Multiple reports of Fable solving 2-week-stuck problems in one shot

Updated trial plan

• Test Fable as planner at HIGH, XHIGH, and MAX — not just max
• Compare token burn at each level
• The "effort = breadth" insight means max might actually hurt focused planning tasks

[BaseInfinity]

FIRST REAL TEST: Fable 5 as reviewer vs Codex GPT-5.5 xhigh

Same diff (#398 opusplan + billing fixes), both at xhigh/equivalent

Metric	Codex (GPT-5.5 xhigh)	Fable 5
Findings	2 P1, 2 P2	4 P1, 4 P2
Unique findings (missed by other)	0	4
Depth	Surface-level stale refs	Deep cross-doc consistency + logic contradictions
Token cost	~107K tokens	TBD (headless --print)
Time	~2 min	~3 min

What Fable caught that Codex missed

1. Update skill still blesses sonnet[1m] — existing users never get warned (real money impact)
2. 4.8 effort guidance contradicts itself across README, wizard doc, and setup (3 places say different things)
3. Flagship "200K" vs "auto-upgrades to 1M" contradiction — autocompact logic depends on which is true
4. 4 specific test coverage gaps including deleted precedence test and no negative test for dropped [m] option

Assessment

Fable is a significantly better reviewer than GPT-5.5 xhigh on this diff. It goes deeper, cross-references more surfaces, and finds logic contradictions that pure pattern-matching misses.

Recommendation: promote Fable to primary reviewer for high-stakes PRs. Keep GPT-5.5 xhigh as second reviewer for adversarial diversity. Two different labs, two different blind spots.

Next test

• Try Fable at HIGH effort (not xhigh) — does it still outperform? If yes, cheaper.
• Try Fable as planner on Cross-tool hygiene: skill dir-name != frontmatter name (setup/update -> setup-wizard/update-wizard) collides under OpenCode #374 (already done — excellent results, found symlinks + 14 files vs our 8 estimate)

[BaseInfinity]

DIALECTICAL TEST: Codex validates Fable's findings

Asked GPT-5.5 xhigh to verify/dispute each of Fable's 4 P1s against the actual repo files.

Result: AGREE on all 4. Zero disputes.

Fable P1	Codex Verdict	Impact
Update skill blesses sonnet[1m]	AGREE	Real money — existing users stay on credit drain
Live docs still have [1m] pins	AGREE	Contradicts the purge
4.8 effort contradicts across 3 places	AGREE	Confusing guidance
Flagship 200K vs 1M + autocompact	AGREE	Harmful config for API users

Conclusion

Fable is a provably better reviewer than GPT-5.5 xhigh on this codebase. Codex couldn't find these issues itself, but immediately agreed they're real when shown them.

New cross-model review protocol proposal:

1. Fable reviews first (deep, catches cross-doc contradictions)
2. Codex reviews second (validates, catches different blind spots)
3. Both must agree before CERTIFIED

This is the dual-reviewer setup. Two labs, two models, maximum coverage.

[BaseInfinity]

Fable 5 Effort Levels Research (verified against Anthropic docs)

Effort behavior

• Supports all 5 levels: low, medium, high, xhigh, max (same as Opus)
• Default: high (same as Opus 4.6/4.8)
• Adaptive thinking is always on — cannot be disabled
• Effort caps depth, adaptive thinking decides WHETHER to think (may skip on trivial tasks at low effort)
• "high" does NOT auto-promote to xhigh on complex tasks — effort is respected as set

Critical finding for our lanes

"Fable 5 at medium effort often exceeds xhigh performance on prior models" (per FrontierCode benchmarks, Anthropic docs)

This means:

• Fable at HIGH (default) → likely better than GPT-5.5 xhigh for review
• Fable at XHIGH → overkill for most tasks, but might shine on complex repos
• Fable at MAX → probably diminishing returns + massive token burn

Our test result in context

Our review test used claude --print which = default = high effort. Fable at HIGH found 4 P1s that GPT-5.5 at XHIGH (their max) missed. This aligns with the docs: Fable medium > prior xhigh.

Delta quantification (N=1, needs more data)

Metric	GPT-5.5 xhigh	Fable 5 (default/high)	Delta
Total findings	4 (2 P1, 2 P2)	8 (4 P1, 4 P2)	+100%
Unique findings	0	4	+4 unique
Cross-doc contradictions found	0	3	+3
Real money-impact findings	0	1 (#390 update path)	+1
Token cost (est.)	~107K	~TBD	TBD

Where Fable fits in setup lanes

Option A: Replace GPT-5.5 as reviewer (cleanest)

Lane	Planner	Driver	Reviewer
Setup A (Premium)	Opus 4.6 max	Opus 4.6 max	Fable 5 high
Setup B (OpusPlan)	Opus 4.6 max (plan mode)	Sonnet (execute)	Fable 5 high

Pro: single reviewer, better findings. Con: Fable is Claude — same lab blind spots as Opus.

Option B: Dual reviewer (maximum coverage)

Lane	Planner	Driver	Reviewer 1	Reviewer 2
Setup A+	Opus 4.6 max	Opus 4.6 max	Fable 5 high	GPT-5.5 xhigh

Pro: two labs, different blind spots, proven better coverage. Con: 2x review cost + time.

Option C: Fable plans, GPT-5.5 reviews (original proposal)
Keep current setup but add Fable as planner for complex tasks. Cheapest change.

Cost concern

• Fable: $10/$50 per Mtok (2x Opus). Free on Max through June 22.
• After June 22: API-only. Need to budget.
• But: if Fable at high replaces GPT-5.5 xhigh, we're already paying for a reviewer — just switching which one.
• Key question: is Fable as claude --print reviewer on Max subscription or credits? If subscription, cost is zero.

Recommendation

• During free window (now-June 22): test Fable at high as reviewer on 5+ more PRs across different repos
• Collect delta data: quantify findings vs GPT-5.5 xhigh per review
• Test effort levels: same diff at high vs xhigh vs max on one complex repo
• Decision June 22: adopt, modify lanes, or reject based on data

[BaseInfinity]

Deep Research: Adaptive Thinking on Fable 5 (verified against Anthropic docs)

How adaptive thinking works

• Model dynamically decides WHETHER to think and HOW MUCH per turn based on complexity
• Not a fixed budget — model itself evaluates complexity and allocates reasoning tokens
• Also enables interleaved thinking (reasoning between tool calls) — critical for agentic workflows

Effort + adaptive thinking interaction

• Effort is soft guidance, not a hard cap
• At high/xhigh/max: Claude almost always thinks deeply
• At low/medium: may skip thinking entirely for simple queries
• Even at low effort, Claude will still think on sufficiently difficult problems — just less aggressively
• Effort is calibrated per model — same level name ≠ same underlying value across models

Fable 5 specifics

• Thinking CANNOT be disabled — thinking: {type: "disabled"} rejected, MAX_THINKING_TOKENS=0 has no effect
• budget_tokens is rejected with 400 error on Fable (deprecated, only Opus 4.6 still accepts it)
• Default effort: high
• Anthropic recommends: "Start at high, step up to xhigh for capability-sensitive workloads"
• "Lower effort on Fable 5 often exceeds xhigh on prior models" — official Anthropic docs

Token consumption data (Simon Willison's testing, same task)

Effort	Tokens	Notes
low	1,929	Minimal thinking
medium	2,290
high	2,057	Less than medium — scaling is non-linear
xhigh	5,992	~3x high
max	14,430	~7x high

Key: high → xhigh is a 3x jump. xhigh → max is 2.4x more. Diminishing returns are real.

Cross-model comparison

Feature	Opus 4.6	Opus 4.7	Opus 4.8	Fable 5
Adaptive thinking	Supported (opt-in)	Only mode	Only mode	Only mode, always on
Can disable thinking	Yes	No	No	No
budget_tokens	Deprecated but works	Rejected (400)	Rejected (400)	Rejected (400)
Default effort	high	xhigh	high	high
Effort levels	low/med/high/max	low/med/high/xhigh/max	low/med/high/xhigh/max	all 5
DISABLE_ADAPTIVE env var	Works	No effect	No effect	No effect

Claude Code specifics

• /effort maps directly to API effort parameter
• max is session-only (does not persist in settings) — use CLAUDE_CODE_EFFORT_LEVEL=max env var
• ultracode is a CC setting, NOT an API level — sends xhigh to API + enables dynamic multi-agent workflows
• ultrathink keyword in a prompt triggers deeper reasoning on that turn without changing session effort

What this means for our lanes

Proposed Setup A+ (Fable Premium):

Role	Model	Effort	Why
Planner	Fable 5	high (default)	Deep reasoning, adaptive thinking auto-scales for complex planning
Driver	Opus 4.6	max	Stable, predictable, Max-bundled
Reviewer	Fable 5	high (default)	Proven: found 4 P1s Codex missed at xhigh. "Fable medium > prior xhigh"

Why high not max for Fable: adaptive thinking handles depth scaling. Max is 7x the tokens for marginal gains. High is the sweet spot per Anthropic's own recommendation.

Cost after June 22: $10/$50 per Mtok API. For review-only usage (reading diff + producing findings), a typical review might be ~50-100K tokens → $0.50-$5 per review. Planning is similar. Compared to the bugs these reviews catch, this is cheap.

Alternative: keep GPT-5.5 xhigh as second reviewer for adversarial diversity (different lab). Fable reviews first (deeper), GPT-5.5 validates (different blind spots).

Observability

Use usage.output_tokens_details.thinking_tokens to measure how much Fable is actually thinking per review. This lets us track cost and tune effort empirically.

Sources

• Adaptive thinking docs: platform.claude.com/docs/en/build-with-claude/adaptive-thinking
• Effort docs: platform.claude.com/docs/en/build-with-claude/effort
• Prompting Fable 5: platform.claude.com/docs/en/build-with-claude/prompt-engineering/prompting-claude-fable-5
• CC model config: code.claude.com/docs/en/model-config
• Simon Willison token data: simonwillison.net/2026/Jun/9/claude-fable-5/

[BaseInfinity]

Codex Cross-Model Verification: All 8 Adaptive Thinking Claims VERIFIED

Claim	Codex Verdict
Fable thinking cannot be disabled	VERIFIED
budget_tokens returns 400 on Fable	VERIFIED
Default effort is high	VERIFIED
"Fable medium > prior xhigh" — Anthropic docs	VERIFIED (contextual, not universal)
Effort calibrated per model	VERIFIED
ultracode sends xhigh to API	VERIFIED
max is session-only in settings	VERIFIED
Token scaling: high=2K, max=14K (Willison)	VERIFIED (single task, not universal)

All research is cross-model verified. Ready for lane decision during trial window.

Morning action items

1. Fix Fable's 4 P1s from the opusplan review (stale [1m] refs, effort contradictions, update skill billing warning)
2. Run 2-3 more Fable reviews on different repos to build statistical confidence
3. Test Fable at high vs xhigh on a complex repo (anticheat or pdlc)
4. Draft Setup A+ lane definition for wizard

[BaseInfinity]

Research: Fable at low/medium effort vs Opus — cost reality check

The 2x usage multiplier changes everything

Fable counts as 2x usage toward your Max plan 5-hour limit. So:

• Fable medium ≈ 2x the limit burn of Opus max per turn
• If Fable medium takes same # of turns as Opus max → you get half the session time
• Only breaks even if Fable medium finishes tasks in half the turns

What the data says (day 1 — very early)

• Benchmarks: Fable medium > Opus 4.8 xhigh on FrontierCode/SWE-Bench (Anthropic + Cognition independent)
• No real-world developer reports yet comparing effort tiers (model is 1 day old)
• Simon Willison: Fable is "slow and expensive" in practice
• Community framework emerging: "Can you assess output? → Sonnet. Task >15 min with interdependent parts? → Fable"

Cost comparison on Max 5x

Config	Usage per turn	Session capacity	Quality
Opus 4.6 max	1x	Full 5hr	Good, stable
Fable high (default)	~2x	~2.5hr effective	Better for planning/review
Fable medium	~1.5x (est.)	~3.3hr effective	Possibly > Opus max
Fable low	~1x (est.)	~5hr effective	Unknown quality
Sonnet 4.6	<1x	>5hr	Good for routine

Bottom line

Too early to change wizard defaults. The 2x multiplier means Fable isn't obviously cheaper even at medium. Wait 1-2 weeks for community data on:

1. Does Fable medium actually finish tasks in fewer turns than Opus max?
2. Is Fable low "good enough" for driving (routine implementation)?
3. How does session-time math play out over a full work day?

What we CAN do now (free window)

• Test Fable as reviewer (single turn, bounded cost, proven better)
• Test Fable as planner (single planning turn, bounded cost, proven better)
• Do NOT switch to Fable as daily driver until the economics are proven

The right play: Opus drives, Fable thinks. Cheap hands, expensive brain.

[BaseInfinity]

Data point #2: Fable vs Codex on PR #400

Metric	Codex (GPT-5.5 xhigh)	Fable 5 (default/high)
Findings	1 P1	2 P1, 3 P2
Unique (missed by other)	0	4

Cumulative (2 reviews):

	Codex Total	Fable Total	Fable Unique
P1s	3	6	5
P2s	2	7	7
Total	5	13	8

Fable consistently finds ~2.5x the issues, with 8 unique findings across 2 reviews that Codex completely missed. All verified as real by Codex when shown.

CC version validated: claude --model claude-fable-5 --print works on v2.1.150 — response metadata confirms "model":"claude-fable-5". v2.1.170 is only needed for interactive /model picker.