Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
Qwen3 235B 2507 (Reasoning) finishes one point ahead overall, 40 to 39. 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 235B 2507 (Reasoning) is the reasoning model in the pair, while Phi-4 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 235B 2507 (Reasoning) gives you the larger context window at 128K, compared with 16K for Phi-4.
Pick Qwen3 235B 2507 (Reasoning) if you want the stronger benchmark profile. Phi-4 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 235B 2507 (Reasoning)
34.7
Phi-4
70.5
Qwen3 235B 2507 (Reasoning)
22
Phi-4
82.6
Qwen3 235B 2507 (Reasoning)
62
Phi-4
80.6
Qwen3 235B 2507 (Reasoning) is ahead overall, 40 to 39. The biggest single separator in this matchup is HumanEval, where the scores are 32 and 82.6.
Phi-4 has the edge for knowledge tasks in this comparison, averaging 70.5 versus 34.7. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Phi-4 has the edge for coding in this comparison, averaging 82.6 versus 22. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
Phi-4 has the edge for multilingual tasks in this comparison, averaging 80.6 versus 62. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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