Head-to-head comparison across 4benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-5.5
89
Kimi 2.6
83
Verified leaderboard positions: GPT-5.5 #2 · Kimi 2.6 #4
Pick GPT-5.5 if you want the stronger benchmark profile. Kimi 2.6 only becomes the better choice if coding is the priority or you want the cheaper token bill.
Agentic
+8.7 difference
Coding
+13.4 difference
Knowledge
+12.6 difference
Multimodal
+10.4 difference
GPT-5.5
Kimi 2.6
$5 / $30
$0.95 / $4
N/A
N/A
N/A
N/A
1M
256K
Pick GPT-5.5 if you want the stronger benchmark profile. Kimi 2.6 only becomes the better choice if coding is the priority or you want the cheaper token bill.
GPT-5.5 is clearly ahead on the provisional aggregate, 89 to 83. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.5's sharpest advantage is in knowledge, where it averages 66.4 against 53.8. The single biggest benchmark swing on the page is HLE, 52.2% to 34.7%. Kimi 2.6 does hit back in coding, so the answer changes if that is the part of the workload you care about most.
GPT-5.5 is also the more expensive model on tokens at $5.00 input / $30.00 output per 1M tokens, versus $0.95 input / $4.00 output per 1M tokens for Kimi 2.6. That is roughly 7.5x on output cost alone. GPT-5.5 gives you the larger context window at 1M, compared with 256K for Kimi 2.6.
GPT-5.5 is ahead on BenchLM's provisional leaderboard, 89 to 83. The biggest single separator in this matchup is HLE, where the scores are 52.2% and 34.7%.
GPT-5.5 has the edge for knowledge tasks in this comparison, averaging 66.4 versus 53.8. Inside this category, HLE is the benchmark that creates the most daylight between them.
Kimi 2.6 has the edge for coding in this comparison, averaging 72 versus 58.6. Inside this category, terminalBench2 is the benchmark that creates the most daylight between them.
GPT-5.5 has the edge for agentic tasks in this comparison, averaging 81.8 versus 73.1. Inside this category, MCP Atlas is the benchmark that creates the most daylight between them.
Kimi 2.6 has the edge for multimodal and grounded tasks in this comparison, averaging 79.4 versus 69. Inside this category, MMMU-Pro w/ Python is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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