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 coding, where it averages 56.2 against 20.
GPT-4o is also the more expensive model on tokens at $2.50 input / $10.00 output per 1M tokens, versus $0.30 input / $1.20 output per 1M tokens for MiniMax M2.7. That is roughly 8.3x on output cost alone. MiniMax M2.7 gives you the larger context window at 200K, compared with 128K for GPT-4o.
Pick MiniMax M2.7 if you want the stronger benchmark profile. GPT-4o only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.
GPT-4o
20
MiniMax M2.7
56.2
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.
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.
MiniMax M2.7 is ahead overall, 57 to 46.
MiniMax M2.7 has the edge for coding in this comparison, averaging 56.2 versus 20. GPT-4o stays close enough that the answer can still flip depending on your workload.
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