Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-4.7
68
Step 3.7 Flash
72
Pick Step 3.7 Flash if you want the stronger benchmark profile. GLM-4.7 only becomes the better choice if coding is the priority or you want the cheaper token bill.
Agentic
+20.6 difference
Coding
+14.3 difference
GLM-4.7
Step 3.7 Flash
$0 / $0
$0.2 / $1.15
82 t/s
N/A
1.10s
N/A
200K
256K
Pick Step 3.7 Flash if you want the stronger benchmark profile. GLM-4.7 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 68. 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 45.3. The single biggest benchmark swing on the page is BrowseComp, 52% to 75.8%. GLM-4.7 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 GLM-4.7. That is roughly Infinityx on output cost alone. Step 3.7 Flash gives you the larger context window at 256K, compared with 200K for GLM-4.7.
Step 3.7 Flash is ahead on BenchLM's provisional leaderboard, 72 to 68. The biggest single separator in this matchup is BrowseComp, where the scores are 52% and 75.8%.
GLM-4.7 has the edge for coding in this comparison, averaging 70.6 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 45.3. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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