MiniMax: MiniMax M1
Analysis Summary
MiniMax M1 is a long-context model with a one-million-token window, tool use, and function calling. Its coding results are stronger than its general intelligence and agentic measurements suggest, making it useful for repository-scale code assistance and large document processing at moderate cost.
Suitable workloads include code search, summarisation across extensive source material, technical drafting, and supervised tool workflows. The large context is valuable for contracts, research archives, and codebases, but weak terminal-task and tool-trajectory results argue against deploying it as an unsupervised agent. Text-only input also limits multimodal production use.
Its pricing supports experimentation and high-volume long-context tasks. Adopt it for supervised coding and document workflows, with explicit validation around tool execution and final outputs.
Assessed August 9, 2026
Editorial notes
MiniMax M1 pairs a million-token context with strong coding test results, tool use, and very competitive pricing. Weak agentic reliability and limited instruction-following performance make supervision important in autonomous workflows.
Rankings consider pricing, capabilities, benchmarks, and real-world applicability and are refreshed as new models launch. Feedback?
DFO Verdict
MiniMax M1 pairs a million-token context with strong coding test results, tool use, and very competitive pricing. Weak agentic reliability and limited instruction-following performance make supervision important in autonomous workflows.
Benchmark scores
Magenta = intelligence · Ink = technical/agentic · Cyan = content & long-context · Grey = community benchmarks. Data: Artificial Analysis, Hugging Face.
17.7 Intelligence Index·18.6 Agentic Index·61 Math Index
How MiniMax: MiniMax M1 compares
MiniMax: MiniMax M1 ranks #196 of 427 AI models we track for overall intelligence, #97 of 182 for agentic tasks. Its 1M-token context window is larger than 86% of the models we list. At $0.55 per million input tokens it is cheaper than 37% of comparable models.
Dark bar = input · light bar = output, scaled to the priciest peer.
1M tokens ≈ 8 full-length novels or ~2,500 pages of business documents in a single request.
Strongest on value. The pulled-in technical corner is the trade-off, and if the shape matters more than the price, this is your model.
Compare shapes side-by-side →Pricing
| Token Type | Cost per 1M tokens | Cost per 1K tokens |
|---|---|---|
| Input | $0.55 | $0.000550 |
| Output | $2.20 | $0.002200 |
What would MiniMax: MiniMax M1 cost your business?
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A website chatbot handling around 100 customer conversations a day, a few short messages each.
Full calculator with 713 models → Price Calculator
These numbers get smaller with the right architecture.
We route routine calls to cheap models and save MiniMax: MiniMax M1 for the hard ones. Most clients cut their estimate by 60-80%.
Talk to our teamAbout MiniMax: MiniMax M1
MiniMax-M1 is a large-scale, open-weight reasoning model designed for extended context and high-efficiency inference. It leverages a hybrid Mixture-of-Experts (MoE) architecture paired with a custom "lightning attention" mechanism, allowing it..
Frequently asked questions about MiniMax: MiniMax M1
How much does MiniMax: MiniMax M1 cost?
MiniMax: MiniMax M1 costs $0.55 per million input tokens and $2.20 per million output tokens.
What is the context window of MiniMax: MiniMax M1?
MiniMax: MiniMax M1 has a context window of 1,000,000 tokens (1M).
What can MiniMax: MiniMax M1 do?
MiniMax: MiniMax M1 supports tool use and function calling.
Who created MiniMax: MiniMax M1?
MiniMax: MiniMax M1 is developed by MiniMax and was released on June 17, 2025.