Head to head

Kimi K3 vs Claude Fable 5.1

Claude Fable 5.1 scores higher on 3 of the 3 benchmarks both models publish. The widest gap is on Humanity's Last Exam, where Claude Fable 5.1 leads by 17.4 points. Kimi K3 has open weights, so unlike Claude Fable 5.1 you can run it on your own GPU or deploy it on OpenLLM Buddy with no per-token fees.

Shared benchmarks

Raw published scores; the higher score on each row is highlighted. LMArena Elo is a human-preference vote and is not counted as a win.

  • GPQA Diamond
    93.593.7

    max reasoning effortGPQA Diamond; AA independent run (Max, Default Fallback)

  • Humanity's Last Exam
    43.560.9

    HLE-Full, no tools, max effortHumanity's Last Exam, no tools; max effort

  • Humanity's Last Exam (with tools)
    56.065.0

    HLE-Full, with tools, max effortHumanity's Last Exam, with tools; max effort (Opus 5.5 system card lists Fable 5.1 at 65.6%)

  • LMArena Text Elo · not counted
    14881504

    LMArena text overall, 'Kimi K3 (Max)', ±4, as of 2026-10-27LMArena Text, Overall; snapshot Oct 27, 2026; Claude Fable 5.1 (Max); +/-6

Should you pick Kimi K3 or Claude Fable 5.1?

Pick Kimi K3 if…

  • You need to self-host: keep prompts and data on your own GPU, avoid per-token fees, or fine-tune the weights.
  • Cost matters: $15.00 per million output tokens against $50.00.
  • You want it running in minutes on a dedicated GPU through OpenLLM Buddy, in an uncensored build.

Pick Claude Fable 5.1 if…

  • You care most about Humanity's Last Exam, Humanity's Last Exam (with tools), where it scores at least 2 points higher.
  • You want a managed API and would rather not run or pay for GPUs yourself.
  • People prefer its answers in blind chats: LMArena Elo 1504 against 1488.

Hardware and cost to run

Kimi K3

Even at 4-bit it needs about 1.9 TB (Multiple nodes (over 1.1 TB)), well beyond a home workstation, so most people run it on rented multi-GPU servers. Full BF16 precision needs about 6.4 TB (Multiple nodes (over 1.1 TB)).

On OpenLLM Buddy: Kimi-K3-Uncensored-GGUF runs on 8× H100 80GB as a GGUF quantized build, with an OpenAI-compatible API and no per-token fees. Prices start at $422 USD for 11 hours.

Deploy Kimi K3
Full GPU memory table for Kimi K3

Claude Fable 5.1

Claude Fable 5.1 is only available through Anthropic's API, so it cannot be self-hosted; it costs $10 per million input tokens and $50 per million output tokens.

Category profile

Category index uses every test each model reports, not only the shared ones.

ReasoningCodingAgenticKnowledge
Kimi K3Claude Fable 5.1
Reasoning82.496.1
Coding97.584.7
Agentic89.592.9
Knowledge93.1—

Specs side by side

Kimi K3Claude Fable 5.1
LabMoonshot AIAnthropic
WeightsOpenAPI only
Released2026-07-272026-09-01
Parameters2.8T total, 104B active (MoE)—
Context1M1M
LicenseKimi K3 License (custom)—
4-bit memory1.9 TB · Multiple nodes (over 1.1 TB)—
API price$3.00 in / $15.00 out per 1M tokens$10.00 in / $50.00 out per 1M tokens
API speed66 tok/s71 tok/s

Related comparisons

Sources

ModelTestScoreSettingSource
Kimi K3GPQA Diamond93.5max reasoning effortKimi K3 model card (Hugging Face)
Claude Fable 5.1GPQA Diamond93.7GPQA Diamond; AA independent run (Max, Default Fallback)Artificial Analysis
Kimi K3Humanity's Last Exam43.5HLE-Full, no tools, max effortKimi K3 model card (Hugging Face)
Claude Fable 5.1Humanity's Last Exam60.9Humanity's Last Exam, no tools; max effortAnthropic Claude Fable 5.1 / Mythos 5.1 System Card (Table 8.1.A)
Kimi K3Humanity's Last Exam (with tools)56.0HLE-Full, with tools, max effortKimi K3 model card (Hugging Face)
Claude Fable 5.1Humanity's Last Exam (with tools)65.0Humanity's Last Exam, with tools; max effort (Opus 5.5 system card lists Fable 5.1 at 65.6%)Anthropic Claude Fable 5.1 / Mythos 5.1 System Card (Table 8.1.A)
Kimi K3LMArena Text Elo1488LMArena text overall, 'Kimi K3 (Max)', ±4, as of 2026-10-27Arena (LMArena) text leaderboard
Claude Fable 5.1LMArena Text Elo1504LMArena Text, Overall; snapshot Oct 27, 2026; Claude Fable 5.1 (Max); +/-6LMArena (arena.ai) Text leaderboard

Scores are published results from model cards, lab reports, and public leaderboards, each linked to its source. Settings such as thinking mode or test version differ between sources and are shown next to each score. OpenLLM Buddy catalog models are uncensored fine-tunes; their scores are those of the official base model. Data collected 2026-10-30. How the index works