โ† Back to Model Hub/SIDE-BY-SIDE REVIEW
SHARE THIS:

MiniMax-01 vs MiniMax M2.7

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for MiniMax-01 and MiniMax M2.7.

MiniMax

MiniMax-01

MiniMax-01 is a combines MiniMax-Text-01 for text generation and MiniMax-VL-01 for image understanding. It has 456 billion parameters, with 45.9 billion parameters activated per inference, and can handle a context...

View Full Specs
MiniMax

MiniMax M2.7

MiniMax-M2.7 is a next-generation large language model designed for autonomous, real-world productivity and continuous improvement. Built to actively participate in its own evolution, M2.7 integrates advanced agentic capabilities through multi-agent...

View Full Specs

Technical Specifications

SpecificationMiniMax-01MiniMax M2.7
ProviderMiniMaxMiniMax
Context Window1,000,192 tokens204,800 tokens
Agent SuitabilityN/AN/A
Time to First Token (TTFT)N/AN/A
Deployment Modelmanaged apimanaged api
Production Stabilitybetastable
API AvailableYesYes
Released Date2025-01-152026-03-18

API Pricing Comparison

Input Price per Million Tokens

MiniMax-01

$0.20

MiniMax M2.7

$0.18

Output Price per Million Tokens

MiniMax-01

$1.10

MiniMax M2.7

$0.72

Want to test both models live?

Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.

Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

MMLUGeneral knowledge & multi-task understanding
8100.0%vs8250.0%
MiniMax-01
MiniMax M2.7
HumanEvalPython coding & logic synthesis
7820.0%vs8000.0%
MiniMax-01
MiniMax M2.7
MATHComplex mathematical problem solving
5050.0%vs5400.0%
MiniMax-01
MiniMax M2.7
GPQAGraduate-level expert reasoning
3700.0%vs3900.0%
MiniMax-01
MiniMax M2.7
HellaSwagCommonsense reasoning and inference
8300.0%vs8400.0%
MiniMax-01
MiniMax M2.7
MT-BenchMulti-turn conversation flow quality
855.0%vs870.0%
MiniMax-01
MiniMax M2.7

MiniMax-01 Quirks & Gotchas

No developer gotchas reported.

MiniMax M2.7 Quirks & Gotchas

No developer gotchas reported.