Head-to-head
Claude Fable 5 vs Claude Opus 4.7: which AI model wins in 2026?
Claude Fable 5 ($50/1M out) and Claude Opus 4.7 ($25/1M out) are two of the most-used AI models in 2026. Across 7 community votes, Claude Fable 5 leads with 63% approval.
Quick verdict
On Reasoning, pick Claude Fable 5: the arena rates it 5/5 against 4.5/5 for Claude Opus 4.7. On budget, Claude Opus 4.7 wins: it starts at $25/1M out versus $50/1M out for Claude Fable 5.
Line-by-line comparison
Strengths and weaknesses
Claude Fable 5
- 80.3% on SWE-bench Pro vs 69.2% for Opus 4.8, 58.6% for GPT-5.5 and 54.2% for Gemini 3.1 Pro, roughly 11 points ahead of the next frontier model
- 95.0% on SWE-bench Verified (Opus 4.8: 88.6%, GPT-5.5: 82.6%) and 29.3% on Cognition's FrontierCode Diamond split, more than double Opus 4.8's 13.4%
- Long-horizon autonomy is the real story: Stripe reported a 50-million-line Ruby codebase migration done in one day instead of 2+ months, and Cursor's CEO calls it state of the art on CursorBench
- Field reports match the benchmarks: HN engineers describe it working 'like an actual engineer' (CRDTs with minimal hand-holding, writing its own fuzzers, one 46x allocation reduction), Simon Willison measured 'several days' worth of work' in a single session
- 1M token context window by default plus 128K output, and state-of-the-art vision on dense documents (29.8% on GDP.pdf vs 24.9% for GPT-5.5 and 22.5% for Opus 4.8)
- Refused-before-output requests are not billed, and server-side fallback to Opus 4.8 with fallback credit is built into the API
- Double the price of Opus 4.8 ($10/$50 vs $5/$25) and slow: single requests on hard tasks routinely run many minutes, Simon Willison bluntly calls it 'slow, expensive'
- Dual-use safety classifiers misfire on legitimate work: a medical physicist reported fluid dynamics problems and MRI segmentation code refused as biosecurity risks, with requests silently rerouted to Opus 4.8 (the viral HN thread was titled 'If Claude Fable stops helping you, you'll never know'; Anthropic says under 5% of sessions)
- Rocky launch: US export controls forced Anthropic to suspend access worldwide from June 12 to June 30, 2026, three days after release, with full restoration only on July 1
- Requires 30-day data retention and is not available under zero data retention, a hard blocker for strict-compliance orgs; also no thinking-off mode, raw chain of thought never returned, assistant prefill returns a 400
- Not universally state of the art: GPT-5.5 still leads ARC-AGI-2 (85.0% vs 77.1%), and Andon Labs found unblocked Mythos 5 underperformed both Opus 4.7 and GPT-5.5 on Vending-Bench, with reasoning that optimized for detectability rather than actual harm
Claude Opus 4.7
- 87.6% SWE-bench Verified (up from 80.8% on Opus 4.6) and 64.3% SWE-bench Pro at launch, ahead of GPT-5.4 (57.7%) and Gemini 3.1 Pro (54.2%)
- 1M-token context window and 128K max output at flat $5/$25 pricing with no long-context premium (300K output via Batch API beta)
- First Claude with high-resolution vision: accepts images up to 2576px on the long edge with pixel-accurate coordinates, ~3x prior detail
- Standout code review: finds more real bugs with stronger cross-file reasoning than rivals in independent tests, and 21% fewer document-reasoning errors than Opus 4.6
- Fine cost control via new xhigh effort level and Task Budgets (beta): low-effort 4.7 roughly matches medium-effort 4.6 output quality
- Recent knowledge: reliable cutoff of January 2026, the freshest of any Claude model at release
- New tokenizer inflates token counts roughly 30% for the same text versus pre-4.7 models (per Anthropic's own docs), raising effective per-request cost despite the unchanged sticker price
- Very verbose in agentic use: one benchmark found GPT-5.5 used 72% fewer output tokens on equivalent coding tasks, and reviewers call its narration over-communicative
- Breaking API changes bite migrators: temperature/top_p/top_k and thinking budget_tokens now return 400 errors, and thinking text is hidden by default
- Moderate latency with minutes-long turns at high effort; fast mode is a premium research preview already deprecated on 4.7
- Superseded by Opus 4.8 at the same $5/$25 within ~3 months, and real-time cybersecurity safeguards can false-positive on legitimate security work
Cast your verdict
One recommendation per tool per gladiator. It reshapes the crowd score everyone sees.
The arena’s verdict on Claude Fable 5
Take Claude Fable 5 if your workload is genuinely long-horizon: overnight agentic runs, monster migrations, tasks where one multi-hour session replaces days of supervised work. There, the 2x premium over Opus 4.8 pays for itself in task compression, and the benchmarks (80.3% SWE-bench Pro, 11 points clear of the field) are backed by real deployments at Stripe and Cursor. For interactive coding and everyday work, stay on Opus 4.8: 88.6% on SWE-bench Verified at half the price, no classifier misfires, faster turns. Cost-sensitive teams get near-Opus coding from Sonnet 5 at $3/$15 (intro $2/$10 through August 2026). Avoid Fable 5 entirely if your org requires zero data retention or if you work anywhere near biology, medical imaging or security tooling, where the dual-use classifiers still produce false positives and silently swap in Opus 4.8 mid-session.
The arena’s verdict on Claude Opus 4.7
Choose Opus 4.7 only if you are already pinned to it for reproducibility: Opus 4.8 costs the same $5/$25, keeps an identical API surface, and outperforms it, making it the better default for new projects. It remains a very strong pick for agentic coding, code review and 1M-context document work, and is a clear upgrade over Opus 4.6. Teams migrating from 4.6 should budget for breaking API changes and a tokenizer that yields roughly 30% more tokens per prompt. Cost-sensitive users should look at Sonnet 5, which delivers near-Opus quality at $3/$15 (intro $2/$10 through August 31, 2026).
What the crowd says
On Claude Fable 5
“I do medical imaging research and the bio classifier keeps flagging my MRI segmentation prompts, then it silently falls back to Opus 4.8 mid-session. At $50 per million output tokens I expect to at least know which model actually answered me.”
“Yes it's 2x the price of Opus and yes the turns are slow. But one overnight Fable run replaced what used to be a week of supervising shorter runs. On a per-task basis it's actually the cheapest model we use.”
“The 1M context is real, not marketing. I fed it our entire service mesh config plus six months of incident postmortems and it traced a flaky timeout to a retry policy nobody remembered writing. Opus 4.8 never connected those dots.”
“Gave it a monorepo migration that Opus 4.8 kept stalling on. It ran for about 40 minutes, came back with the whole thing done plus a test harness it wrote for itself. Felt like reviewing a senior engineer's PR, not babysitting a chatbot.”
On Claude Opus 4.7
“Watch your invoices. New tokenizer counts ~30% more tokens for the same text, and it narrates every tiny step. Sticker price unchanged, effective cost definitely not.”
“Came from 4.6 and stopped chunking repos entirely. 1M context, 128K output, flat $5/$25 with no long-context premium. That pricing decision alone won me over.”
“87.6 SWE-bench Verified is not just marketing, it closes tickets GPT-5.4 fumbles. And the hi-res vision with pixel-accurate coords finally makes screenshot debugging useful.”
Keep comparing
Frequently asked questions
Is Claude Fable 5 better than Claude Opus 4.7?
The crowd currently sides with Claude Fable 5: 63% recommend it, versus 57% for Claude Opus 4.7 (7 votes). On Reasoning, Claude Fable 5 rates higher (5/5 vs 4.5/5). The right pick depends on your use case. The line-by-line comparison on this page breaks down pricing, key specs and arena ratings.
Which is cheaper, Claude Fable 5 or Claude Opus 4.7?
Claude Opus 4.7 is cheaper: it starts at $25/1M out, while Claude Fable 5 starts at $50/1M out.
How much do Claude Fable 5 and Claude Opus 4.7 cost per 1M tokens?
Claude Fable 5: $10/1M in per 1M input tokens, $50/1M out per 1M output tokens. Claude Opus 4.7: $5/1M in per 1M input tokens, $25/1M out per 1M output tokens.