Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Claude Haiku 4.5
59
GLM-4.7
71
Pick GLM-4.7 if you want the stronger benchmark profile. Claude Haiku 4.5 only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+2.7 difference
Claude Haiku 4.5
GLM-4.7
$1 / $5
$0 / $0
N/A
82 t/s
N/A
1.10s
200K
200K
Pick GLM-4.7 if you want the stronger benchmark profile. Claude Haiku 4.5 only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
GLM-4.7 is clearly ahead on the provisional aggregate, 71 to 59. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Claude Haiku 4.5 is also the more expensive model on tokens at $1.00 input / $5.00 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. GLM-4.7 is the reasoning model in the pair, while Claude Haiku 4.5 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.
GLM-4.7 is ahead on BenchLM's provisional leaderboard, 71 to 59. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 73.3% and 73.8%.
Claude Haiku 4.5 has the edge for coding in this comparison, averaging 73.3 versus 70.6. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
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