Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Claude Haiku 4.5
59
Qwen3.6-27B
72
Verified leaderboard positions: Claude Haiku 4.5 unranked · Qwen3.6-27B #10
Pick Qwen3.6-27B 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
Qwen3.6-27B
$1 / $5
$0 / $0
N/A
N/A
N/A
N/A
200K
262K
Pick Qwen3.6-27B 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.
Qwen3.6-27B is clearly ahead on the provisional aggregate, 72 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 Qwen3.6-27B. That is roughly Infinityx on output cost alone. Qwen3.6-27B 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. Qwen3.6-27B gives you the larger context window at 262K, compared with 200K for Claude Haiku 4.5.
Qwen3.6-27B is ahead on BenchLM's provisional leaderboard, 72 to 59. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 73.3% and 77.2%.
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.
Estimates at 50,000 req/day · 1000 tokens/req average.
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