Claude Fable 5 vs Kimi K3: API Pricing and Benchmarks (2026)

Claude Fable 5 holds #1 on both of Arena's Text and Vision boards as of July 23, 2026. Kimi K3 launched on July 16 with a benchmark table aimed directly at it, and Moonshot published the rows it loses as well as the ones it wins. Both models carry a 1M-token context window and both target the same work: agents that run for hours, read a lot, call tools, and come back with something finished. The split between them is unusually clean.

TL;DR: Claude Fable 5 wins the depth tests and the crowd: Arena English Text #1 at 1507 against K3's #10 at 1486, HLE-Full 53.3 vs 43.5, FrontierSWE 86.6 vs 81.2, plus adaptive thinking, image input, and #1 on Arena Vision at 1318. Kimi K3 wins the endurance tests: SWE Marathon 42.0 vs 35.0, BrowseComp 91.2 vs 88.0, OmniDocBench 91.1 vs 89.8, and a 131k default output raisable to 1M. It also costs roughly a third of what Fable 5 does: $2.10 against $5.50 per 1M input tokens, $10.50 against $27.50 on output. Both are live on Unifically through the same endpoint.

The two models in one minute

Claude Fable 5 is Anthropic's June 2026 flagship and the first model in the Claude 5 family, opening a Mythos class above the Opus line. It holds 1M tokens of context, returns up to 128k, takes text and image input, and runs adaptive thinking: the model decides how much to reason per request rather than being told.

Kimi K3 arrived July 16, 2026: 2.8 trillion parameters, 16 of 896 experts active per pass, a 1,048,576-token window, and 131,072 output tokens by default that can be raised to the full million. Thinking is always on at max effort. It reads images and video natively, and Moonshot has committed to publishing the weights by July 27, 2026.

Benchmarks, wins on both sides

These figures come from Moonshot's published comparison table, both models at max settings, except the Arena ratings which are live as of July 23, 2026.

The pattern holds across the table. Fable 5 wins the tests that reward getting a hard single problem right: frontier knowledge, the toughest software-engineering set, economically valuable knowledge work, tool orchestration. K3 wins the tests that reward staying power: multi-hour engineering runs, live web research, long document parsing.

Worth knowing when you read those K3 wins: Moonshot ran its own rows on its in-house harness while competitor rows come from other vendors' tooling, and Moonshot itself says overall performance still trails Fable 5. Independent testing is thinner. Artificial Analysis places K3 fourth of 187 models on its Intelligence Index at a score of 57.

The Arena gap is also younger than it looks. K3's 1486 rests on about 3,600 votes and the board still marks the rating preliminary, against 14,600 votes behind Fable 5's 1507. K3 has already moved from #6 in launch week to #10, and the board's own confidence interval still spans ranks 4 through 27. Expect the number to keep moving.

The differences the table misses

Thinking control. Fable 5 decides how much to reason per request. K3 runs max on everything, so a one-line question pays the same reasoning tax as a repo migration. Moonshot has promised lower effort modes; until they land, this is the biggest operational gap between the two.

Speed. Artificial Analysis measures K3 at 62 output tokens per second, below the median for its class. Combined with mandatory max reasoning, a chatty agent loop feels noticeably slower on K3.

Input types. Fable 5 takes text and images through the same endpoint and holds #1 on Arena's Vision board at 1318, ahead of every Opus variant. K3 runs text in, text out on the platform. If screenshots, charts, and diagrams are inputs to your agent rather than afterthoughts, that settles it.

Output length. K3's 131k default output beats Fable 5's 128k ceiling, and K3 can be pushed to a 1M-token response. For a single very long artifact, that removes a stitching step.

Openness and retention. K3's weights are promised for July 27, 2026; nothing is published yet. Fable 5 is closed and requires 30-day data retention, so it is unavailable under zero-data-retention terms. For regulated workloads that difference can outrank every benchmark in this post.

Behavior. K3 acts on ambiguous instructions rather than asking, and is sensitive to thinking history: a harness that trims earlier reasoning content can destabilise a long session. Fable 5 carries safeguards in cyber, biology, chemistry, and distillation that occasionally decline harmless requests in those areas.

Claude Fable 5 vs Kimi K3 pricing

Both bill per token on Unifically, quoted per million:

K3 costs about 62% less on both input and output. That is the single fact most likely to decide this comparison, and nothing in the benchmark table above accounts for it.

One agent step sending 50,000 input tokens and returning 5,000 costs $0.41 on Fable 5 and $0.16 on K3. Fable 5 is 2.6 times the price for that request, so the question is whether its wins are worth paying 2.6x for. On a few hard architecture questions a day, the difference is rounding error and you should buy the better answer. On an unattended run making thousands of calls across a large repo, the multiplier is the budget, and K3's SWE Marathon lead means you are not even trading quality away on that kind of work.

Two things soften the gap. K3 runs max reasoning on every request and reasoning bills as output, so its real output volume runs higher than a like-for-like comparison suggests. And Fable 5's cheaper cached reads matter for agents that resend a long transcript each turn, though at $0.55 against $0.21 per million it stays the more expensive side of that line too.

How to call both

Both speak the OpenAI-compatible Chat Completions format, so switching between them is a one-word change:

curl -X POST https://api.unifically.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "anthropic/claude-fable-5",
    "messages": [
      {"role": "user", "content": "Review this migration plan and flag what breaks."}
    ]
  }'

Swap "model" to "moonshotai/kimi-k3" to send the identical request to K3. Fable 5 also accepts Anthropic's native /v1/messages format if you are porting existing code, and both models work through /v1/responses.

Which one should you use?

Pick Claude Fable 5 when quality per attempt is what you are buying: frontier-difficulty problems, architecture decisions, knowledge-heavy analysis, vision-led work, and tool-orchestration-heavy agents. Adaptive thinking also makes it the better operational fit when request difficulty varies across your traffic.

Pick Kimi K3 when the run is long and mostly unattended: overnight repo work, research agents that browse the live web, document pipelines, or single responses that need to run past 128k tokens. It is also the only one of the two with an open-weights path, and at roughly a third of Fable 5's token price it is the default when call volume rather than single-answer quality drives your bill.

Both run on Unifically through the same OpenAI-compatible /v1/chat/completions endpoint, so switching is a model-id change: anthropic/claude-fable-5 or moonshotai/kimi-k3. See every language model on the platform for the rest of the lineup.

We will update this comparison when K3's weights land, when its lower reasoning-effort modes ship, and when independent benchmarks put both models on the same harness.