Head-to-head comparison across 7benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Qwen3.5-122B-A10B
65
Qwen3.5 397B
64
Verified leaderboard positions: Qwen3.5-122B-A10B #8 · Qwen3.5 397B #15
Pick Qwen3.5-122B-A10B if you want the stronger benchmark profile. Qwen3.5 397B only becomes the better choice if reasoning is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Agentic
+0.1 difference
Coding
+11.7 difference
Reasoning
+3.0 difference
Knowledge
+16.4 difference
Multilingual
+2.5 difference
Multimodal
+2.4 difference
Inst. Following
+0.8 difference
Qwen3.5-122B-A10B
Qwen3.5 397B
$0 / $0
$0.6 / $3.6
N/A
96 t/s
N/A
2.44s
262K
128K
Pick Qwen3.5-122B-A10B if you want the stronger benchmark profile. Qwen3.5 397B only becomes the better choice if reasoning is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.5-122B-A10B finishes one point ahead on BenchLM's provisional leaderboard, 65 to 64. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
Qwen3.5-122B-A10B's sharpest advantage is in knowledge, where it averages 81.6 against 65.2. The single biggest benchmark swing on the page is SWE-bench Verified, 72% to 76.2%. Qwen3.5 397B does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
Qwen3.5 397B is also the more expensive model on tokens at $0.60 input / $3.60 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.5-122B-A10B. That is roughly Infinityx on output cost alone. Qwen3.5-122B-A10B is the reasoning model in the pair, while Qwen3.5 397B 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. Qwen3.5-122B-A10B gives you the larger context window at 262K, compared with 128K for Qwen3.5 397B.
Qwen3.5-122B-A10B is ahead on BenchLM's provisional leaderboard, 65 to 64. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 72% and 76.2%.
Qwen3.5-122B-A10B has the edge for knowledge tasks in this comparison, averaging 81.6 versus 65.2. Inside this category, SuperGPQA is the benchmark that creates the most daylight between them.
Qwen3.5-122B-A10B has the edge for coding in this comparison, averaging 72 versus 60.3. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Qwen3.5 397B has the edge for reasoning in this comparison, averaging 63.2 versus 60.2. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Qwen3.5 397B has the edge for agentic tasks in this comparison, averaging 56.2 versus 56.1. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Qwen3.5 397B has the edge for multimodal and grounded tasks in this comparison, averaging 79.6 versus 77.2. Inside this category, CharXiv is the benchmark that creates the most daylight between them.
Qwen3.5-122B-A10B has the edge for instruction following in this comparison, averaging 93.4 versus 92.6. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Qwen3.5 397B has the edge for multilingual tasks in this comparison, averaging 84.7 versus 82.2. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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