Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
DeepSeek V3.2 (Thinking) is clearly ahead on the aggregate, 66 to 57. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek V3.2 (Thinking)'s sharpest advantage is in agentic, where it averages 69.4 against 57. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 71% to 57%. MiniMax M2.7 does hit back in coding, so the answer changes if that is the part of the workload you care about most.
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 DeepSeek V3.2 (Thinking). That is roughly Infinityx on output cost alone. DeepSeek V3.2 (Thinking) is the reasoning model in the pair, while MiniMax M2.7 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. MiniMax M2.7 gives you the larger context window at 200K, compared with 128K for DeepSeek V3.2 (Thinking).
Pick DeepSeek V3.2 (Thinking) if you want the stronger benchmark profile. MiniMax M2.7 only becomes the better choice if coding is the priority or you need the larger 200K context window.
DeepSeek V3.2 (Thinking)
69.4
MiniMax M2.7
57
DeepSeek V3.2 (Thinking)
51
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.
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.
DeepSeek V3.2 (Thinking) is ahead overall, 66 to 57. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 71% and 57%.
MiniMax M2.7 has the edge for coding in this comparison, averaging 56.2 versus 51. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
DeepSeek V3.2 (Thinking) has the edge for agentic tasks in this comparison, averaging 69.4 versus 57. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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