Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.1
82
Step 3.7 Flash
72
Verified leaderboard positions: GLM-5.1 #25 · Step 3.7 Flash unranked
Pick GLM-5.1 if you want the stronger benchmark profile. Step 3.7 Flash only becomes the better choice if agentic is the priority or you want the cheaper token bill.
Agentic
+0.6 difference
Coding
+4.6 difference
GLM-5.1
Step 3.7 Flash
$1.4 / $4.4
$0.2 / $1.15
N/A
N/A
N/A
N/A
203K
256K
Pick GLM-5.1 if you want the stronger benchmark profile. Step 3.7 Flash only becomes the better choice if agentic is the priority or you want the cheaper token bill.
GLM-5.1 is clearly ahead on the provisional aggregate, 82 to 72. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5.1's sharpest advantage is in coding, where it averages 60.9 against 56.3. The single biggest benchmark swing on the page is BrowseComp, 68% to 75.8%. Step 3.7 Flash does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
GLM-5.1 is also the more expensive model on tokens at $1.40 input / $4.40 output per 1M tokens, versus $0.20 input / $1.15 output per 1M tokens for Step 3.7 Flash. That is roughly 3.8x on output cost alone. Step 3.7 Flash gives you the larger context window at 256K, compared with 203K for GLM-5.1.
GLM-5.1 is ahead on BenchLM's provisional leaderboard, 82 to 72. The biggest single separator in this matchup is BrowseComp, where the scores are 68% and 75.8%.
GLM-5.1 has the edge for coding in this comparison, averaging 60.9 versus 56.3. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Step 3.7 Flash has the edge for agentic tasks in this comparison, averaging 65.9 versus 65.3. Inside this category, Gert Labs is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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