MiniMax M2.7 vs Qwen3.5-27B

Pick Qwen3.5-27B if you want the stronger benchmark profile. MiniMax M2.7 only becomes the better choice if agentic is the priority or you would rather avoid the extra latency and token burn of a reasoning model.

Qwen3.5-27B finishes one point ahead on BenchLM's provisional leaderboard, 63 to 62. 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.

Qwen3.5-27B's sharpest advantage is in coding, where it averages 63 against 53.7. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 57% to 41.6%. MiniMax M2.7 does hit back in agentic, so the answer changes if that is the part of the workload you care about most.

MiniMax M2.7 is also the more expensive model on tokens at $0.30 input / $1.20 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.5-27B. That is roughly Infinityx on output cost alone. Qwen3.5-27B is the reasoning model in the pair, while MiniMax M2.7 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. Qwen3.5-27B gives you the larger context window at 262K, compared with 200K for MiniMax M2.7.