> ## Documentation Index
> Fetch the complete documentation index at: https://docs.apiyi.com/llms.txt
> Use this file to discover all available pages before exploring further.

# MiniMax-M2.7 Launch: Self-Evolving Agent Model with Just 10B Parameters

> MiniMax releases M2.7 and M2.7-highspeed — only 10B active parameters achieving Tier-1 performance with SWE-bench Pro 56.22%, at up to 1/50th the cost of competitors. Now available on APIYI.

## Key Highlights

* **Smallest Tier-1 Model**: Only 10B active parameters, SWE-bench Pro 56.22%, SWE-bench Verified 78%, matching Opus-level performance
* **Self-Evolution**: Industry's first model deeply participating in its own training, autonomously handling 30-50% of its RL development workflow
* **Native Multi-Agent**: Built-in Agent Teams collaboration, 40+ complex skills with 97% adherence rate
* **Extreme Value**: Input \$0.30 / Output \$1.20 per million tokens — roughly 1/50th of comparable competitors
* **Two Variants**: Standard and highspeed produce identical output quality; highspeed runs at \~100 TPS

## Background

On March 18, 2026, MiniMax officially released the M2.7 series. Dubbed the "smallest Tier-1 model," M2.7 achieves performance comparable to Claude Opus 4.6 and GPT-5.3 Codex on major benchmarks with just 10B active parameters.

M2.7's standout feature is its "self-evolution" capability — it autonomously triggers log analysis, debugging, and metric evaluation, independently handling 30-50% of its own reinforcement learning development workflow, including analyzing its own failures, rewriting code segments, running evaluations, and deciding what to keep or discard.

APIYI has launched both M2.7 and M2.7-highspeed with pay-per-token Chat billing.

## Detailed Analysis

### Core Features

<CardGroup cols={2}>
  <Card title="Self-Evolving" icon="dna">
    First model to deeply participate in its own training, autonomously handling 30-50% of RL workflow
  </Card>

  <Card title="Native Multi-Agent" icon="users">
    Built-in Agent Teams with role boundaries, adversarial reasoning, and protocol adherence as internalized capabilities
  </Card>

  <Card title="Minimal Parameters" icon="minimize">
    Just 10B active parameters achieving Tier-1 performance — extremely efficient
  </Card>

  <Card title="Advanced Tool Use" icon="wrench">
    Manages 40+ complex skills (each exceeding 2,000 tokens) with 97% adherence rate
  </Card>
</CardGroup>

### Benchmark Performance

| Benchmark | M2.7 Score | Notes |
| - | - | - |
| SWE-bench Pro | 56.22% | Near Opus-level |
| SWE-bench Verified | 78% | Strong software engineering |
| VIBE-Pro | 55.6% | End-to-end project delivery |
| Terminal Bench 2 | 57.0% | Complex engineering systems |
| MM Claw | 62.7% | Agent tasks |
| MLE Bench Lite | 66.6% | ML competition medal rate |
| Skill Adherence | 97% | Across 40+ complex skills |

Scores **50** on the Artificial Analysis Intelligence Index, tying with GLM-5, ahead of MiMo-V2-Pro (49) and Kimi K2.5 (47), while using 20% fewer output tokens at less than one-third the cost.

### M2.7 vs M2.7-highspeed

Both variants produce **identical output quality** — the difference is speed and cost:

| Aspect | M2.7 Standard | M2.7-highspeed |
| - | - | - |
| Output Quality | Identical | Identical |
| Speed | \~60 TPS | \~100 TPS |
| Context Window | 204,800 tokens | 204,800 tokens |
| Best For | Budget-conscious | Latency-sensitive production |

### Technical Specifications

* **Context Window**: 204,800 tokens (\~205K)
* **Max Output**: 131,072 tokens
* **Reasoning**: Supports mandatory reasoning with `<think>` tags
* **Architecture**: MoE (Mixture of Experts)

## Pricing & Availability

| Model | Input Price | Output Price | Billing Type |
| - | - | - | - |
| MiniMax-M2.7 | \$0.30 / 1M tokens | \$1.20 / 1M tokens | Pay-per-token - Chat |
| MiniMax-M2.7-highspeed | \$0.60 / 1M tokens | \$2.40 / 1M tokens | Pay-per-token - Chat |

<Info>
  M2.7-highspeed is approximately 1.7x faster than the standard version, ideal for latency-sensitive production workloads. Both variants are identical in intelligence — choose based on your needs.
</Info>

## Summary & Recommendations

MiniMax-M2.7 delivers Tier-1 performance with just 10B active parameters — a remarkable achievement in efficiency. Its self-evolution capability and native multi-agent collaboration are unique differentiators, excelling in software engineering, tool calling, and complex workflow orchestration.

**Recommended Use Cases**:

* Developers needing high intelligence on a budget
* Agent workflows and multi-step task orchestration
* Software engineering assistance and code generation
* Production environments requiring strong tool-calling capabilities


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.