Head-to-head comparison across 4benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Grok 4.20
64
Kimi K2.5
64
Verified leaderboard positions: Grok 4.20 unranked · Kimi K2.5 #13
Treat this as a split decision. Grok 4.20 makes more sense if you need the larger 2M context window or you want the stronger reasoning-first profile; Kimi K2.5 is the better fit if multimodal & grounded is the priority or you want the cheaper token bill.
Agentic
+7.5 difference
Coding
+3.2 difference
Reasoning
+7.7 difference
Multimodal
+7.7 difference
Grok 4.20
Kimi K2.5
$2 / $6
$0.6 / $3
233 t/s
45 t/s
10.33s
2.38s
2M
256K
Treat this as a split decision. Grok 4.20 makes more sense if you need the larger 2M context window or you want the stronger reasoning-first profile; Kimi K2.5 is the better fit if multimodal & grounded is the priority or you want the cheaper token bill.
Grok 4.20 and Kimi K2.5 finish on the same provisional overall score, so this is less about a single winner and more about where the edge shows up. The provisional headline says tie; the benchmark table is where the real choice happens.
Grok 4.20 is also the more expensive model on tokens at $2.00 input / $6.00 output per 1M tokens, versus $0.60 input / $3.00 output per 1M tokens for Kimi K2.5. That is roughly 2.0x on output cost alone. Grok 4.20 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. Grok 4.20 gives you the larger context window at 2M, compared with 256K for Kimi K2.5.
Grok 4.20 and Kimi K2.5 are tied on the provisional overall score, so the right pick depends on which category matters most for your use case.
Kimi K2.5 has the edge for coding in this comparison, averaging 64.2 versus 61. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for reasoning in this comparison, averaging 61 versus 53.3. Grok 4.20 stays close enough that the answer can still flip depending on your workload.
Kimi K2.5 has the edge for agentic tasks in this comparison, averaging 54.6 versus 47.1. Inside this category, DeepSearchQA is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for multimodal and grounded tasks in this comparison, averaging 78.5 versus 70.8. 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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