# Claude Fable 5 vs Claude Opus 4.7 (2026): side-by-side comparison

Source: [GLAD-AI-TOR](https://glad-ia-tor.com) · Full page: https://glad-ia-tor.com/vs/claude-fable-5-vs-claude-opus-4-7
Arena: llm-models · Crowd scores are live visitor verdicts (one per person per tool, never paid, Bayesian-smoothed).

## At a glance

| | Claude Fable 5 | Claude Opus 4.7 |
|---|---|---|
| Price | $50/1M out | $25/1M out |
| Crowd score | 63% (4 votes) | 57% (3 votes) |
| provider | Anthropic | Anthropic |
| contextWindow | 1M tokens | 1M tokens (128K max output) |
| priceIn | $10/1M in | $5/1M in |
| priceOut | $50/1M out | $25/1M out |
| modalities | text, vision | text + image input (up to 2576px), text output |
| openWeights | no | no |
| reasoning (1-5) | 5 | 4.5 |
| coding (1-5) | 5 | 4.5 |
| writing (1-5) | 4.5 | 4.5 |
| speed (1-5) | 2 | 2.5 |
| valueForMoney (1-5) | 3 | 3 |

### Claude Fable 5

> Anthropic's June 2026 Mythos-class flagship: 80.3% on SWE-bench Pro, 11 points clear of every other frontier model

Strengths:
- 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

Weaknesses:
- 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

Verdict: 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.

Full review: https://glad-ia-tor.com/tool/claude-fable-5 · Markdown: https://glad-ia-tor.com/tool/claude-fable-5.md

### Claude Opus 4.7

> Anthropic's April 2026 Opus: 87.6% SWE-bench Verified, 1M context, high-res vision, now behind Opus 4.8

Strengths:
- 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

Weaknesses:
- 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

Verdict: 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).

Full review: https://glad-ia-tor.com/tool/claude-opus-4-7 · Markdown: https://glad-ia-tor.com/tool/claude-opus-4-7.md

## More

Full llm-models ranking: https://glad-ia-tor.com/hall-of-fame/llm-models

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