Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Claude Haiku 4.5
58
ZAYA1-74B-Preview
58
Treat this as a split decision. Claude Haiku 4.5 makes more sense if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model; ZAYA1-74B-Preview is the better fit if you want the cheaper token bill or you need the larger 256K context window.
Coding
+20.1 difference
Claude Haiku 4.5
ZAYA1-74B-Preview
$1 / $5
$0 / $0
N/A
N/A
N/A
N/A
200K
256K
Treat this as a split decision. Claude Haiku 4.5 makes more sense if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model; ZAYA1-74B-Preview is the better fit if you want the cheaper token bill or you need the larger 256K context window.
Claude Haiku 4.5 and ZAYA1-74B-Preview finish on the same provisional overall score, so this is less about a single winner and more about where the edge shows up. The provisional headline says tie; the benchmark table is where the real choice happens.
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 ZAYA1-74B-Preview. That is roughly Infinityx on output cost alone. ZAYA1-74B-Preview 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. ZAYA1-74B-Preview gives you the larger context window at 256K, compared with 200K for Claude Haiku 4.5.
Claude Haiku 4.5 and ZAYA1-74B-Preview are tied on the provisional overall score, so the right pick depends on which category matters most for your use case.
Claude Haiku 4.5 has the edge for coding in this comparison, averaging 73.3 versus 53.2. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
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