- Text Generation
Rerank 4 Fast is Cohere’s fourth-generation multilingual reranking model optimized for low-latency, high-throughput retrieval with a context window of around 32K–33K tokens. It is designed to…
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MiniMax M2.7
- Text Generation
MiniMax M2.7 is a 230B-parameter Mixture-of-Experts large language model from MiniMax, with 10B active parameters and a 204,800-token context window, optimized for coding, agentic tool use, and complex multi-step workflows.
Providers Comparison
Speed
Output tokens per second · Higher is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
End-to-End Response Time
Seconds · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
Price
USD per 1M tokens (blended) · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
About the model
What is MiniMax M2.7?
MiniMax M2.7 is a self-improving, agent-focused large language model released by MiniMax in March 2026, designed as a 230B-parameter Sparse Mixture-of-Experts system with 10B active parameters per token and a 204,800-token context window. It is primarily used for software engineering tasks, including system design, full-stack development, code review, and other coding-intensive workflows, as well as long-horizon agentic workflows that require tool use, search, and multi-round reasoning in production environments. It also targets enterprise automation and complex office or productivity tasks where persistent agents coordinate multi-step work across tools and documents. The model belongs to MiniMax’s M2-series family of LLMs, succeeding models such as M2, M2.1, and M2.5 and sitting below the later multimodal MiniMax M3 line.
Model capabilities
5 Core Capabilities
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Advanced Reasoning
Performs complex logical, mathematical, and multi-step reasoning tasks, achieving top-tier scores on composite intelligence and analysis benchmarks.
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Code Generation
Generates, debugs, and refactors code across multiple languages, supporting software engineering workflows like SWE-Pro and Terminal-Bench tasks.
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Instruction Following
Understands and follows detailed natural-language instructions to complete diverse text-based tasks, from structured workflows to open-ended requests.
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Multilingual Text
Handles multilingual input and output for text-to-text tasks, enabling cross-language interactions and content creation for global users.
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Document Handling
Creates and manipulates long-form documents such as reports, spreadsheets, and presentations within extended text-only office-style workflows.
Use cases
6 Most Valuable Use Cases
- Autonomous Coding Agent
- Complex Task Orchestration
- Long-Context Document Analysis
- Business Workflow Automation
- Reasoning-Heavy Research Aid
- Structured Tool Calling
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and fastest MiniMax‑class access across providers.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 120ms | 70 tps | 99.99% | $0.20 | $0.40 | 64K |
| MiniMax | Global | ~180ms | ~60 tps | ~99.9% | ~$0.30 | ~$0.60 | ~32K |
| OpenAI (closest: GPT-4o-mini class) | Global | ~200ms | ~80 tps | 99.9% | ~$0.25 | ~$0.50 | 128K |
| Amazon Bedrock (MiniMax-equivalent) | US East | ~220ms | ~70 tps | 99.9% | ~$0.28 | ~$0.55 | ~32K |
| Azure AI (MiniMax-equivalent) | EU West | ~210ms | ~75 tps | 99.9% | ~$0.27 | ~$0.53 | 128K |
Performance benchmarks
Technical Specifications
| Metric | MiniMax M2.7 | OpenAI GPT-4.1 Mini | Anthropic Claude 3.5 Haiku |
|---|---|---|---|
| Avg Latency | ~220ms | ~180ms | ~200ms |
| Context Window | 128K | 128K | 200K |
| Input Price ($/1M) | $0.15 | $0.15 | $0.25 |
| Output Price ($/1M) | $0.60 | $0.60 | $0.80 |
| Max Output Tokens | 4K | 4K | 4K |
| Throughput | 40 tps | 50 tps | 45 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 11.5B
- Prompt tokens processed (last 30 days)
- 7.8B
- Completion tokens generated (last 30 days)
- 9.3M
- API requests served (last 30 days)
- 99.8%
- Avg uptime (last 30 days)
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Intelligent Model Routing
Automatically route each request to the optimal model across providers based on latency, cost, and quality—without changing your integration or redeploying code.
One endpoint, every model. -
Cost-Aware Orchestration
Control spend with dynamic price-aware routing, transparent usage metrics, and configurable policies that keep your AI workloads within budget at scale.
More performance, less spend. -
Resilient Fallback Flows
Design multi-provider fallback chains so requests seamlessly fail over to backup models when providers throttle, error, or go down—no user ever hits a dead end.
Zero-downtime AI calls. -
Deep AI Observability
Get full visibility into prompts, latencies, errors, and model choices with traceable logs and metrics, so you can debug faster and continuously tune performance.
See every token move. -
Task-Level Abstractions
Define tasks like chat, RAG, tools, and workflows once, then map them to any underlying model stack—keeping business logic stable as models evolve.
Code to tasks, not models. -
High-Throughput Batch Jobs
Run massive batch inference—file processing, backfills, evaluations—through a single API with automatic chunking, retries, and progress tracking built in.
Scale to millions of calls.
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a lightweight general-purpose model for everyday chat and assistant-style interactions.
- You need reasonably capable reasoning and coding without paying for top-tier frontier models.
- Your use case involves prototyping AI features where good-enough intelligence beats perfect accuracy.
- Your use case involves batch-processing many short text tasks with moderate complexity constraints.
- You need a model from a non-U.S. provider for jurisdictional or vendor-diversification reasons.
Avoid if...
- You need state-of-the-art reasoning, math, or coding comparable to the strongest frontier models.
- Your workload requires very long-context processing of large documents or multi-hour transcripts.
- You need highly specialized domain expertise, such as complex legal, medical, or scientific analysis.
- You need rigorous enterprise guarantees, certifications, or compliance evidence from widely adopted vendors.
- Your workload requires access to a very large ecosystem of tools, plugins, and integrations.
FAQ
Frequently Asked Questions
-
What is MiniMax M2.7?
MiniMax M2.7 is a large language model from MiniMax focused on fast, cost-efficient text generation for general-purpose applications.
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What is the context window of MiniMax M2.7?
MiniMax M2.7 supports a context window up to tens of thousands of tokens, suitable for moderately long conversations and documents.
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What modalities does MiniMax M2.7 support via LLM.API?
Via LLM.API, MiniMax M2.7 is available as a text-only model for prompts and completions.
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How does MiniMax M2.7 pricing work on LLM.API?
MiniMax M2.7 is billed on LLM.API per 1,000 tokens for both input and output, with exact rates set by LLM.API’s current pricing table.
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How fast is MiniMax M2.7 in terms of latency?
MiniMax M2.7 is optimized for low latency and typically returns short responses in under a second under normal network conditions.
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What is MiniMax M2.7 best suited for?
MiniMax M2.7 is best for everyday coding assistance, content drafting, customer support bots, and lightweight reasoning tasks.
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How do I call MiniMax M2.7 through LLM.API?
Specify the MiniMax M2.7 model name in your LLM.API request payload, send a text prompt, and parse the returned completion text.
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How does MiniMax M2.7 compare to similar models on LLM.API?
Compared to larger models, MiniMax M2.7 generally offers lower cost and latency but slightly weaker performance on complex reasoning and long-context tasks.
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What are the main limitations of MiniMax M2.7?
MiniMax M2.7 can produce incorrect or outdated information, struggles with very long contexts, and is less capable on highly specialized or domain-expert tasks.
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Does MiniMax M2.7 support tools or function calling via LLM.API?
Tool or function-calling support for MiniMax M2.7 depends on LLM.API’s orchestration features, not the base model alone.
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