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.
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
Self-Evolving
First model to deeply participate in its own training, autonomously handling 30-50% of RL workflow
Native Multi-Agent
Built-in Agent Teams with role boundaries, adversarial reasoning, and protocol adherence as internalized capabilities
Minimal Parameters
Just 10B active parameters achieving Tier-1 performance — extremely efficient
Advanced Tool Use
Manages 40+ complex skills (each exceeding 2,000 tokens) with 97% adherence rate
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 |
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 |
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.
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