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MiniMax
minimax/minimax-m2.7Catalogue: Indexed in the reference catalogue; nothing on this deployment answers as this model yet.
Published by the vendor; the platform bills at the target's declared price.
MiniMax 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...
MiniMax M2.7 earns a request when it needs step-by-step reasoning, tool calls, very long inputs; a short, general request would go to GLM 5.2 instead - $0.01 per million input tokens against $0.30 here; and the hardest requests, where the quality floor rules it out, to Claude Opus 5.5 (batch). Your rules decide the floor; the trace shows the comparison for every request.
Lower input price and an intelligence index within 5 points.
Nothing on this deployment answers as this model yet. Connect your own provider under Model providers, or add a target to your catalogue - the router picks it up without retraining.
# targets.yaml - declare a target that runs MiniMax M2.7
targets:
- id: minimax-m2-7
kind: llm
name: MiniMax M2.7
metadata:
model: minimax/minimax-m2.7 # links the target to this catalogue entry
capabilities:
domains: [general]
supports_tools: true
context_window: 204800
cost:
usd_per_1k_tokens: 0.000750Input list price per million tokens; MiniMax M2.7 is the 4th cheapest of 9 listed.
Other models by the same vendor in the catalogue.
Photo: RDNE Stock project
Cached input reads $0.06 per 1M · weights on Hugging Face as MiniMaxAI/MiniMax-M2.7
A higher intelligence index, best first, with the price it comes at.
The catalogue prints input and output prices and what three typical monthly workloads cost on each.
