Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.1
83
Kimi K2.6
84
Verified leaderboard positions: GLM-5.1 #21 · Kimi K2.6 #6
Pick Kimi K2.6 if you want the stronger benchmark profile. GLM-5.1 only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Agentic
+7.8 difference
Coding
+11.1 difference
Knowledge
+1.5 difference
GLM-5.1
Kimi K2.6
$1.4 / $4.4
$0.95 / $4
N/A
N/A
N/A
N/A
203K
256K
Pick Kimi K2.6 if you want the stronger benchmark profile. GLM-5.1 only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Kimi K2.6 finishes one point ahead on BenchLM's provisional leaderboard, 84 to 83. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
Kimi K2.6's sharpest advantage is in coding, where it averages 72 against 60.9. The single biggest benchmark swing on the page is HLE, 52.3% to 34.7%.
GLM-5.1 is also the more expensive model on tokens at $1.40 input / $4.40 output per 1M tokens, versus $0.95 input / $4.00 output per 1M tokens for Kimi K2.6. Kimi K2.6 gives you the larger context window at 256K, compared with 203K for GLM-5.1.
Kimi K2.6 is ahead on BenchLM's provisional leaderboard, 84 to 83. The biggest single separator in this matchup is HLE, where the scores are 52.3% and 34.7%.
Kimi K2.6 has the edge for knowledge tasks in this comparison, averaging 53.8 versus 52.3. Inside this category, HLE is the benchmark that creates the most daylight between them.
Kimi K2.6 has the edge for coding in this comparison, averaging 72 versus 60.9. Inside this category, Vibe Code Bench is the benchmark that creates the most daylight between them.
Kimi K2.6 has the edge for agentic tasks in this comparison, averaging 73.1 versus 65.3. Inside this category, MCP Atlas is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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