Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
MiniMax M2.7 is clearly ahead on the aggregate, 57 to 46. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
MiniMax M2.7's sharpest advantage is in agentic, where it averages 57 against 38.3. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 57% to 44%.
MiniMax M2.7 is also the more expensive model on tokens at $0.30 input / $1.20 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Phi-4. That is roughly Infinityx on output cost alone. MiniMax M2.7 gives you the larger context window at 200K, compared with 16K for Phi-4.
Pick MiniMax M2.7 if you want the stronger benchmark profile. Phi-4 only becomes the better choice if you want the cheaper token bill.
MiniMax M2.7
57
Phi-4
38.3
MiniMax M2.7
56.2
Phi-4
55
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
Benchmark data for this category is coming soon.
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
Benchmark data for this category is coming soon.
MiniMax M2.7 is ahead overall, 57 to 46. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 57% and 44%.
MiniMax M2.7 has the edge for coding in this comparison, averaging 56.2 versus 55. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
MiniMax M2.7 has the edge for agentic tasks in this comparison, averaging 57 versus 38.3. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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