What is native multi-agent collaboration in MiniMax M2.7?

MiniMax M2.7 operates within multi-agent networks, handling context passing, handoffs, and dependency tracking between agents without custom orchestration middleware.

What is dynamic tool search?

Instead of using a fixed tool list, MiniMax M2.7 discovers and invokes relevant tools at runtime based on the task at hand. This expands its adaptability during long-horizon workflows.

How does MiniMax M2.7 differ from M2.5?

M2.5 introduced planning-before-coding for single-agent workflows. MiniMax M2.7 adds multi-agent coordination, complex skill orchestration, and dynamic tool search for production-grade distributed agent systems.

Does MiniMax M2.7 support professional office tasks beyond coding?

Yes. It performs well on professional office tasks including document processing and data workflows alongside software engineering.

Is there a speed-optimized variant of MiniMax M2.7?

Yes. `minimax/minimax-m2.7-highspeed` is the throughput-optimized variant at roughly double the listed input and output rates versus the standard variant.

How do I access MiniMax M2.7 through the AI SDK?

Set the model identifier to `minimax/minimax-m2.7` in your SDK configuration.

Can MiniMax M2.7 handle production debugging across multiple services?

Yes. Production debugging and end-to-end project delivery are key improvements in the MiniMax M2.7 generation.

MiniMax

MiniMax M2.7

MiniMax M2.7 is MiniMax's high-capability agentic model targeting end-to-end software engineering: project delivery, log analysis, bug troubleshooting, and code security. It supports a context window of 204.8K tokens and a max output of 196.6K tokens per request.

View API reference

Input and output price: Prices from: Input $0.30, Output $1.20, Per 1M tokens
24h uptime: Loading AI Gateway uptime

Get API key Read the docs

import { streamText } from 'ai'

const result = streamText({
  model: 'minimax/minimax-m2.7',
  prompt: 'Why is the sky blue?'
})

Read docs

Overview Playground Providers Uptime Status Throughput Latency API Similar About FAQ

Copy link to headingPlayground

Try out MiniMax M2.7 by MiniMax. Usage is billed to your team at API rates. Free users (those who haven't made a payment) get $5 of credits every 30 days.

MiniMax M2.7

Copy link to headingProviders

Route requests across multiple providers. Copy a provider slug to set your preference. Visit the docs for more info. Using a provider means you agree to their terms, listed under Legal.

Provider

Context	Max Output	Latency	Throughput	Input	Output	Cache	Web Search	Capabilities	ZDR	No Training	Free Tier	Release Date

MiniMax

Legal:Terms•Privacy

205K

131K

1.6 s

57 tps

$0.30/M+1 more

$1.20/M+1 more

Read$0.06/M

Write$0.38/M

—

03/18/2026

Novita AI

Legal:Terms•Privacy

205K

131K

1.0 s

78 tps

$0.30/M

$1.20/M

Read$0.06/M

—

03/18/2026

GMICloud

Legal:Terms•Privacy

197K

1.2 s

61 tps

$0.30/M

$1.20/M

Read$0.06/M

—

03/18/2026

Getting started

Call MiniMax M2.7 through AI Gateway with the AI SDK generateText and streamText functions, or through the OpenAI Chat Completions, OpenAI Responses, and Anthropic Messages APIs by changing the base URL. AI Gateway authenticates the request and routes it to an available provider.

Install the AI SDK (pnpm add ai dotenv), create an API key from the API Keys page, and set it as AI_GATEWAY_API_KEY in your environment. Full setup is covered in the text generation quickstart.

index.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'minimax/minimax-m2.7',
    prompt: 'Why is the sky blue?',
  });

  console.log(result.text);
}

main().catch(console.error);

Top-level parameters

The same MiniMax M2.7 request in each API format AI Gateway supports.

top-level-params.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'minimax/minimax-m2.7',
    system: 'You are a concise technical assistant.',
    prompt: 'Summarize the tradeoffs between static generation and SSR.',
    maxOutputTokens: 1024,
  });

  console.log(result.text);
}

main().catch(console.error);

Standard parameters like prompt, messages, temperature, and tools work as documented in the AI SDK docs. These are the parameters with model-specific behavior.

Parameter	Type	Required	Description
`model`	`string`	Yes	Model ID in the form `creator/model`, e.g. `minimax/minimax-m2.7`. AI Gateway routes the request to an available provider.
`maxOutputTokens`	`number`	No	Hard cap on generated tokens. MiniMax M2.7 supports up to `196,608` output tokens. Reasoning tokens count toward this limit.
`reasoning`	`'provider-default' \| 'none' \| 'minimal' \| 'low' \| 'medium' \| 'high' \| 'xhigh'`	No	Provider-agnostic reasoning effort, available in AI SDK 7 or later. Maps to the provider’s native reasoning configuration; reasoning settings under `providerOptions` take precedence when both are set. See the Reasoning section below.
`providerOptions`	`Record<string, JSONValue>`	No	AI Gateway routing options under `gateway`, plus any provider-native options under the provider’s own namespace — see the table below.

Input limits

Input	Formats	Sources	Max count	Max size	Limits
Text	—	—	—	—	Prompt and response share the 205K-token context window

Provider options

Set AI Gateway routing options under providerOptions.gateway. For provider-specific options, pass them under the provider’s namespace as documented by the AI SDK.

Learn more in the AI SDK provider docs.

provider-options.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'minimax/minimax-m2.7',
    prompt: 'Why is the sky blue?',
    providerOptions: {
      gateway: {
        only: ['minimax', 'novita'],
      },
    },
  });

  console.log(result.text);
}

main().catch(console.error);

These AI Gateway routing options apply to every model. Provider-specific options pass through under the provider’s own namespace (for example providerOptions.anthropic) exactly as documented by the AI SDK.

Parameter	Type	Required	Description
`providerOptions.gateway.only`	`string[]`	No	Restrict routing to these provider slugs. Requests fail over only within the listed providers.
`providerOptions.gateway.order`	`string[]`	No	Preferred provider order. Listed providers are tried first; unlisted providers remain available as fallbacks.
`providerOptions.gateway.sort`	`'cost' \| 'ttft' \| 'tps'`	No	Rank candidate providers by price, time to first token, or tokens per second instead of the default routing order.
`providerOptions.gateway.zeroDataRetention`	`boolean`	No	Route only to providers with a zero-data-retention policy for this model.

Routing across providers

AI Gateway serves the same model through multiple providers and fails over automatically. order expresses a preference while keeping every provider eligible; only is a hard allowlist — if none of the listed providers are available the request fails instead of falling back.

Options under a provider's own namespace (for example providerOptions.anthropic) are forwarded to that provider with the request. Providers ignore option namespaces that don't apply to them, so it is safe to set provider options alongside gateway routing options.

Reasoning

AI Gateway bridges reasoning across every API format. The AI SDK exposes a provider-agnostic top-level reasoning level (none, minimal, low, medium, high, or xhigh); the Chat Completions and Responses formats take the same effort under reasoning.effort; and the Anthropic Messages format uses a native thinking token budget. Whichever you send, the gateway maps it to the target model’s native configuration, converting between effort levels and token budgets as needed. Reasoning-related settings under providerOptions take full precedence over the top-level reasoning value and are never merged. Reasoning tokens typically count toward your output-token usage, though how they’re reported and billed varies by provider.

Learn more in the AI Gateway reasoning guide.

reasoning.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'minimax/minimax-m2.7',
    prompt: 'Explain the Monty Hall problem step by step.',
    reasoning: 'high',
  });

  console.log(result.text);
}

main().catch(console.error);

Tool calling

Expose tools the model can call. Define each tool’s inputs with a Zod schema.

tool-calling.ts

import { generateText, tool } from 'ai';
import { z } from 'zod';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'minimax/minimax-m2.7',
    prompt: 'What is the weather in San Francisco?',
    tools: {
      getWeather: tool({
        description: 'Get the current weather for a location',
        inputSchema: z.object({ location: z.string() }),
        execute: async ({ location }) => ({ location, temperatureC: 18 }),
      }),
    },
  });

  console.log(result.text);
}

main().catch(console.error);

Copy link to headingMore models by MiniMax

Model

Context	Latency	Throughput	Input	Output	Cache	Web Search	Capabilities	Providers	ZDR	No Training	Free Tier	Release Date

minimax/minimax-m3

0.9 s

145 tps

$0.26/M

$1.02/M

Read$0.06/M

—

05/31/2026

minimax/minimax-m2.7-highspeed

205K

0.9 s

61 tps

$0.60/M

$2.40/M

Read$0.06/M

Write$0.38/M

—

03/18/2026

minimax/minimax-m2.5

0.5 s

129 tps

$0.27/M+1 more

$0.95/M+1 more

Read$0.03/M

Write$0.38/M

—

02/12/2026

minimax/minimax-m2.5-highspeed

205K

0.9 s

84 tps

$0.60/M

$2.40/M

Read$0.03/M

Write$0.38/M

—

02/12/2026

minimax/minimax-m2.1

205K

0.4 s

124 tps

$0.30/M

$1.20/M

Read$0.03/M

Write$0.38/M

—

12/23/2025

minimax/minimax-m2

205K

0.9 s

69 tps

$0.30/M

$1.20/M

Read$0.03/M

Write$0.38/M

—

10/27/2025

Copy link to headingAbout MiniMax M2.7

Released on March 18, 2026, MiniMax M2.7 introduces native support for multi-agent collaboration and complex skill orchestration. Where earlier generations focused on single-agent task completion, MiniMax M2.7 coordinates across agent networks, passing context, managing handoffs, and tracking dependencies between parallel workstreams.

The model also introduces dynamic tool search, letting agents discover and invoke relevant tools at runtime rather than relying on a pre-specified list. This expands the model's effective action space and makes it more adaptable to novel task types during long-horizon workflows.

Beyond software engineering, MiniMax M2.7 handles professional office tasks. This makes it suitable for enterprise automation spanning document processing, financial modeling, and multi-system data workflows alongside its coding capabilities.

Copy link to headingWhat To Consider When Choosing a Provider

Configuration: For teams building multi-agent systems, MiniMax M2.7's native collaboration capabilities reduce the custom orchestration code you need to coordinate agents.
Zero Data Retention: Zero Data Retention is offered on a per-provider and model basis. See the documentation for details.
Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.

Copy link to headingWhen to Use MiniMax M2.7

Best for

Multi-agent systems: Native agent-to-agent coordination without custom middleware
Production debugging workflows: Investigation spanning multiple files, services, or repositories
End-to-end project delivery: Full workflows from specification through implementation and deployment
Enterprise office automation: Workflows combining software engineering with document and data processing
Dynamic tool discovery: Pipelines that need runtime tool selection rather than fixed tool lists

Consider alternatives when

Single-agent workload: Your workflow doesn't benefit from orchestration features
Critical response latency: Latency-sensitive interactive use where M2.7-highspeed is a better fit
Lower cost sufficient: Earlier M2 generations cover your requirements at lower cost

Copy link to headingConclusion

MiniMax M2.7 is the right choice when your application outgrows single-agent patterns and needs a model that can coordinate, delegate, and dynamically discover tools across a production workflow.

Copy link to headingFrequently Asked Questions

What is native multi-agent collaboration in MiniMax M2.7?
MiniMax M2.7 operates within multi-agent networks, handling context passing, handoffs, and dependency tracking between agents without custom orchestration middleware.
What is dynamic tool search?
Instead of using a fixed tool list, MiniMax M2.7 discovers and invokes relevant tools at runtime based on the task at hand. This expands its adaptability during long-horizon workflows.
How does MiniMax M2.7 differ from M2.5?
M2.5 introduced planning-before-coding for single-agent workflows. MiniMax M2.7 adds multi-agent coordination, complex skill orchestration, and dynamic tool search for production-grade distributed agent systems.
Does MiniMax M2.7 support professional office tasks beyond coding?
Yes. It performs well on professional office tasks including document processing and data workflows alongside software engineering.
Is there a speed-optimized variant of MiniMax M2.7?
Yes. minimax/minimax-m2.7-highspeed is the throughput-optimized variant at roughly double the listed input and output rates versus the standard variant.
How do I access MiniMax M2.7 through the AI SDK?
Set the model identifier to minimax/minimax-m2.7 in your SDK configuration.
Can MiniMax M2.7 handle production debugging across multiple services?
Yes. Production debugging and end-to-end project delivery are key improvements in the MiniMax M2.7 generation.

Your use is subject to MiniMax's Terms & Privacy Policies.

Agent Stack

Core Platform

Tools

Learn

Build

Explore

MiniMax M2.7

Copy link to headingPlayground

Copy link to headingProviders

Copy link to headingUptime24 hours

Copy link to headingThroughput24 hours

Copy link to headingLatency24 hours

Getting started

Top-level parameters

Input limits

Provider options

Routing across providers

Reasoning

Tool calling

Copy link to headingMore models by MiniMax

Copy link to headingAbout MiniMax M2.7

Copy link to headingWhat To Consider When Choosing a Provider

Copy link to headingWhen to Use MiniMax M2.7

Best for

Consider alternatives when

Copy link to headingConclusion

Copy link to headingFrequently Asked Questions