MiniMax M2.7
According to BenchLM.ai, MiniMax M2.7 ranks #61 out of 119 models on the provisional leaderboard with an overall score of 54/100. It does not yet have enough sourced coverage for BenchLM's verified leaderboard. While not a frontier model, it offers specific advantages depending on the use case.
MiniMax M2.7 is a open weight model with a 200K token context window. It processes queries without explicit chain-of-thought reasoning, offering faster response times and lower token usage.
BenchLM links it directly to MiniMax M2.5 as the earlier related model in that lineage. This profile currently has 36 of 225 tracked benchmarks. BenchLM only exposes non-generated benchmark rows publicly, so missing categories stay blank until a sourced evaluation is available.
Its strongest category is Instruction Following (#42), while its weakest is Coding (#50). This performance profile makes it a well-rounded choice across a range of tasks.
Ranking Distribution
Category rank across 4 benchmark categories — sorted by best rank
Category Performance
Scores across all benchmark categories (0-100 scale)
Category Breakdown
Agentic
Coding
#50Reasoning
Knowledge
Math
Multilingual
Multimodal
Inst. Following
#42Chatbot Arena Performance
Benchmark Details
Only benchmark rows with an attached exact-source record are shown here. Source-unverified manual rows and generated rows are hidden from model pages.
Compare This Model
See how MiniMax M2.7 stacks up against similar models
Frequently Asked Questions
How does MiniMax M2.7 perform overall in AI benchmarks?
MiniMax M2.7 currently ranks #61 out of 119 models on BenchLM's provisional leaderboard with an overall score of 54. It is created by MiniMax and features a 200K context window.
Is MiniMax M2.7 good for knowledge and understanding?
MiniMax M2.7 has visible benchmark coverage in knowledge and understanding, but BenchLM does not currently assign it a global category rank there.
Is MiniMax M2.7 good for coding and programming?
MiniMax M2.7 ranks #50 out of 119 models in coding and programming benchmarks with an average score of 54.2. There are stronger options in this category.
Is MiniMax M2.7 good for mathematics?
MiniMax M2.7 has visible benchmark coverage in mathematics, but BenchLM does not currently assign it a global category rank there.
Is MiniMax M2.7 good for reasoning and logic?
MiniMax M2.7 has visible benchmark coverage in reasoning and logic, but BenchLM does not currently assign it a global category rank there.
Is MiniMax M2.7 good for agentic tool use and computer tasks?
MiniMax M2.7 has visible benchmark coverage in agentic tool use and computer tasks, but BenchLM does not currently assign it a global category rank there.
Is MiniMax M2.7 good for multimodal and grounded tasks?
MiniMax M2.7 has visible benchmark coverage in multimodal and grounded tasks, but BenchLM does not currently assign it a global category rank there.
Is MiniMax M2.7 good for instruction following?
MiniMax M2.7 ranks #42 out of 119 models in instruction following benchmarks with an average score of 76.3. There are stronger options in this category.
Is MiniMax M2.7 open source?
Yes, MiniMax M2.7 is an open weight model created by MiniMax, meaning it can be downloaded and run locally or fine-tuned for specific use cases.
Does MiniMax M2.7 have full benchmark coverage on BenchLM?
Not yet. MiniMax M2.7 currently has 36 published benchmark scores out of the 225 benchmarks BenchLM tracks. BenchLM only exposes non-generated public benchmark rows, so missing categories stay blank until a sourced evaluation is available.
What is the context window size of MiniMax M2.7?
MiniMax M2.7 has a context window of 200K, which determines how much text it can process in a single interaction.
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