Head-to-head comparison across 4benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Claude Fable 5
96
Kimi K2.5
63
Verified leaderboard positions: Claude Fable 5 #2 · Kimi K2.5 #17
Pick Claude Fable 5 if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
Agentic
+30.6 difference
Coding
+21.4 difference
Knowledge
+9.7 difference
Multimodal
+13.9 difference
Claude Fable 5
Kimi K2.5
$10 / $50
$0.6 / $3
N/A
45 t/s
N/A
2.38s
1M+
256K
Pick Claude Fable 5 if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
Claude Fable 5 is clearly ahead on the provisional aggregate, 96 to 63. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Claude Fable 5's sharpest advantage is in agentic, where it averages 85.2 against 54.6. The single biggest benchmark swing on the page is HLE, 64.5% to 30.1%.
Claude Fable 5 is also the more expensive model on tokens at $10.00 input / $50.00 output per 1M tokens, versus $0.60 input / $3.00 output per 1M tokens for Kimi K2.5. That is roughly 16.7x on output cost alone. Claude Fable 5 is the reasoning model in the pair, while Kimi K2.5 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Claude Fable 5 gives you the larger context window at 1M+, compared with 256K for Kimi K2.5.
Claude Fable 5 is ahead on BenchLM's provisional leaderboard, 96 to 63. The biggest single separator in this matchup is HLE, where the scores are 64.5% and 30.1%.
Claude Fable 5 has the edge for knowledge tasks in this comparison, averaging 74.8 versus 65.1. Inside this category, HLE is the benchmark that creates the most daylight between them.
Claude Fable 5 has the edge for coding in this comparison, averaging 85.6 versus 64.2. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Claude Fable 5 has the edge for agentic tasks in this comparison, averaging 85.2 versus 54.6. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
Claude Fable 5 has the edge for multimodal and grounded tasks in this comparison, averaging 92.4 versus 78.5. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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