Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Qwen3.5-27B
62
Step 3.7 Flash
72
Verified leaderboard positions: Qwen3.5-27B #20 · Step 3.7 Flash unranked
Pick Step 3.7 Flash if you want the stronger benchmark profile. Qwen3.5-27B only becomes the better choice if coding is the priority or you want the cheaper token bill.
Agentic
+14.3 difference
Coding
+6.7 difference
Qwen3.5-27B
Step 3.7 Flash
$0 / $0
$0.2 / $1.15
N/A
N/A
N/A
N/A
262K
256K
Pick Step 3.7 Flash if you want the stronger benchmark profile. Qwen3.5-27B only becomes the better choice if coding is the priority or you want the cheaper token bill.
Step 3.7 Flash is clearly ahead on the provisional aggregate, 72 to 62. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Step 3.7 Flash's sharpest advantage is in agentic, where it averages 65.9 against 51.6. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 41.6% to 59.5%. Qwen3.5-27B does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Step 3.7 Flash is also the more expensive model on tokens at $0.20 input / $1.15 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 gives you the larger context window at 262K, compared with 256K for Step 3.7 Flash.
Step 3.7 Flash is ahead on BenchLM's provisional leaderboard, 72 to 62. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 41.6% and 59.5%.
Qwen3.5-27B has the edge for coding in this comparison, averaging 63 versus 56.3. Step 3.7 Flash stays close enough that the answer can still flip depending on your workload.
Step 3.7 Flash has the edge for agentic tasks in this comparison, averaging 65.9 versus 51.6. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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