# Claude Fable 5 vs Kimi K3 (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-kimi-k3
Arena: llm-models · Crowd scores are live visitor verdicts (one per person per tool, never paid, Bayesian-smoothed).

## At a glance

| | Claude Fable 5 | Kimi K3 |
|---|---|---|
| Price | $50/1M out | $15/1M out |
| Crowd score | 63% (4 votes) | 57% (3 votes) |
| provider | Anthropic | Moonshot AI |
| contextWindow | 1M tokens | 1M tokens |
| priceIn | $10/1M in | $3/1M in |
| priceOut | $50/1M out | $15/1M out |
| modalities | text, vision | text, vision, video input |
| openWeights | no | no |
| reasoning (1-5) | 5 | 4.5 |
| coding (1-5) | 5 | 4.5 |
| writing (1-5) | 4.5 | 4 |
| speed (1-5) | 2 | 2 |
| valueForMoney (1-5) | 3 | 3.5 |

### 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

### Kimi K3

> Moonshot's 2.8T-parameter MoE that sparked the 'new DeepSeek moment': frontier-adjacent scores at $3/$15

Strengths:
- #3 on the Artificial Analysis Intelligence Index (57) at launch, comparable to Claude Opus 4.8 and GPT-5.5: the closest a Chinese lab has come to the closed US frontier
- 93.4% on SWE-bench Verified in Vals AI's independent harness (GPT-5.6 Sol: 96.2%, Claude Fable 5: 95.0%) and #1 on Arena.ai's Frontend Code Arena at 1679 points, ahead of Fable 5
- 93.5% on GPQA Diamond (between Fable 5's 92.6% and GPT-5.6's 94.1%), 96.1% on AIME 2025, and Elo 1668 on GDPval-AA v2 agentic work, second only to Fable 5
- Strong agentic profile: #1 on AutomationBench-AA SaaS workflows (53%), long-horizon terminal and repo navigation via Kimi Code, and ~21% fewer output tokens than K2 for more intelligence

Weaknesses:
- 3x price jump over Kimi K2.6 ($0.95/$4 to $3/$15) makes it the most expensive Chinese model ever shipped, at Claude Sonnet 5 list price: the '10x cheaper than US models' era is over (Simon Willison documented the hike)
- Slow: 33 output tokens/s, ranked #145 of 190 models on Artificial Analysis, a poor fit for interactive use
- Hallucination rate climbed from 39% (K2.6) to 51% on AA-Omniscience as the model now attempts answers it would previously refuse (Fable 5 sits at a comparable 54.9%)

Verdict: Kimi K3 is the strongest argument yet that the frontier is no longer exclusively American: #3 on Artificial Analysis, top-tier GPQA and SWE-bench Verified scores, and the best frontend-code arena ranking in the business, at roughly half to a third of US closed-frontier prices. Take it for agentic coding, frontend work and SaaS automation where its benchmarks are strongest, or if your roadmap depends on self-hosting a frontier-class model once the weights land. Skip it for interactive products (33 tokens/s is slow), for anything touching politically sensitive content or strict EU data-residency requirements (Mandarin-language censorship, Beijing jurisdiction), and for high-accuracy retrieval where its 51% hallucination rate on AA-Omniscience demands a verification layer. Cost-obsessed teams should note DeepSeek V4 still delivers vastly more tokens per dollar; K3's pitch is peak capability per dollar, not cheapest tokens.

Full review: https://glad-ia-tor.com/tool/kimi-k3 · Markdown: https://glad-ia-tor.com/tool/kimi-k3.md

## More

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

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