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  1. Models
  2. Text
  3. MiniMax

MiniMax M2.7 Turbo

MiniMax M2.7 Turbo is the highspeed and higher priced route for M2.7.

  • Reasoning
  • Tool Calling
  • Structured Output

Added Mar 18, 2026

Model weights

Pricing

Auto routing · per 1M tokens
Input
$0.60
Output
$2.40
Cache read
$0.30
Compare provider prices

Specifications

Context window
204.8K
Max output
131.1K
Parameters
230B / 10B
Total / active

Benchmarks

Sourced from Vectara.

Hallucination Rate

12.9%

Hallucination Rate: 12.9
Better than 19% of models compared

Factual Consistency

87.1%

Factual Consistency: 87.1
Better than 19% of models compared

Answer Rate

99.4%

Answer Rate: 99.4
Better than 45% of models compared

Avg Summary Length

Average generated summary length

131.9

Last updated 2026-10-22 · Matched as MiniMaxAI/minimax-m2p7

Vectara Leaderboard

Providers

Choose or restrict providers with the API. Provider-selection prices include the selection markup. Provider selection guide · Pricing API (JSON, USD per 1K tokens)

Cache write /M
Auto
Auto
Recommended
$0.60$2.40$0.30N/AN/AN/A
Runware
Runware
10% off
UNK
$0.63$0.567$2.52$2.27$0.063$0.0567$0.354N/AN/A
Privacy:Retention and training unclear
Batch API
ProviderInputOutputCached input
Auto
Autominimax/minimax-m2.7-turbo
$0.60/M$2.40/M$0.30/M

Batch rows for this model use normal routing and cost the same as standard API requests. Set provider in a row to choose a provider.

Related text models

Compare MiniMax M2.7 Turbo with similar models from the same provider or model family.

MiniMax M3.1 Flash

minimax/minimax-m3.1-flash

MiniMax M3.1 Flash is a multimodal model for coding, tool use, and long-context work. It accepts text, images, and video and produces text, with a 1,000,000-token context window and up to 524,288 output tokens within the remaining context. Thinking is always enabled, with low, medium, high, xhigh, and max effort levels; max is the default. Thinking tokens count toward the output limit. Temperature is fixed at 1 and top-p at 0.95; custom values have no effect.

MiniMax M3

minimax/minimax-m3

MiniMax M3 is MiniMax's open-weights frontier model, built for coding, agent workflows, tool use, and multimodal understanding from step zero. Thinking stays off for faster direct answers unless a request asks for it; MiniMax M3 Thinking has it on by default. MiniMax reports 59.0% on SWE-Bench Pro and 66.0% on Terminal Bench 2.1, with Sparse Attention designed to scale context to 1M. It starts with a 512K context cap on NanoGPT for now.

MiniMax M3 Thinking

minimax/minimax-m3:thinking

MiniMax M3 Thinking is the adaptive-thinking version of MiniMax's open-weights frontier model for coding, agent workflows, tool use, long-context tasks, and native multimodal understanding. MiniMax reports 59.0% on SWE-Bench Pro and 66.0% on Terminal Bench 2.1, with Sparse Attention designed to scale context to 1M. It starts with a 512K context cap on NanoGPT for now.

MiniMax Latest

minimax/minimax-latest

Compatibility alias for MiniMax M3 Thinking, retaining its existing request format and structured-output support. The newer MiniMax M3.1 Flash is available separately as minimax/minimax-m3.1-flash and has different request parameter support.

MiniMax M2.7

minimax/minimax-m2.7

MiniMax M2.7 is the first model deeply involved in iterating on its own training. It excels in real-world software engineering (SWE-Pro 56.22%), end-to-end project delivery (VIBE-Pro 55.6%), and complex office workflows with strong tool-use compliance and agentic capabilities.

MiniMax M2.5

minimax/minimax-m2.5

MiniMax M2.5 is a productivity-focused flagship model that builds on M2.1 with stronger coding and real-world office workflow performance (Word, Excel, PowerPoint), plus better tool-use planning and token efficiency.