- Text Generation
Granite 4.0 Micro is a 3B-parameter dense language model from IBM’s Granite 4.0 family, optimized for low-latency, cost-efficient workloads and local or edge deployment.
Powered by MiniMax
MiniMax M2.5
- Text Generation
MiniMax M2.5 is a frontier-class, agent-native large language model from MiniMax that combines a Mixture-of-Experts architecture with long-context, cost-efficient inference for real-world productivity tasks.
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 M2.5?
MiniMax M2.5 is a state-of-the-art, agent-focused large language model designed to reason efficiently, decompose tasks, and complete complex workflows under real-world time and cost constraints. It is primarily used for coding assistance, tool-using agents, and complex multi-step automation across workflows like office productivity and data processing. It also serves general-purpose chat, analysis, and long-context reasoning use cases, including document-heavy and enterprise scenarios. M2.5 is part of MiniMax’s M2 series of models, succeeding MiniMax M2 and M2.1 within the same family of agentic LLMs.
Model capabilities
5 Core Capabilities
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Advanced Coding
Delivers state-of-the-art multilingual code generation, debugging, and full lifecycle software development across over ten programming languages.
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Agentic Tool Use
Coordinates complex multi-step tasks, calling external tools and search services efficiently for real-world automation and agent workflows.
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Long-Context Reasoning
Handles very large text contexts with efficient reasoning traces, supporting extended documents, conversations, and multi-stage problem solving.
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Business Productivity
Automates office workflows such as document drafting, summarization, reporting, and analysis to support knowledge work across business functions.
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Multilingual Text
Understands and generates text in many languages, enabling cross-lingual communication, content creation, and localization scenarios.
Use cases
6 Most Valuable Use Cases
- Customer Service Chatbots
- Marketing Copywriting
- Legal Draft Assistance
- Compliance Case Monitoring
- E-commerce Product Support
- Code Generation Help
Transparent pricing
Cost Comparison
Up to ~70% cheaper and faster than comparable MiniMax M2.5 endpoints
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 120ms | 80 tps | 99.99% | $0.08 | $0.24 | 128K |
| MiniMax | APAC | ~220ms | ~40 tps | ~99.9% | ~$0.20 | ~$0.60 | ~32K |
| OpenRouter | Global | ~260ms | ~30 tps | ~99.9% | ~$0.24 | ~$0.72 | ~32K |
| Together AI | US East | ~240ms | ~35 tps | ~99.9% | ~$0.22 | ~$0.66 | ~32K |
Performance benchmarks
Technical Specifications
| Metric | MiniMax M2.5 | GPT-4o Mini | Claude 3 Haiku |
|---|---|---|---|
| Avg Latency | ~300ms | ~250ms | ~350ms |
| Context Window | 128K | 128K | 200K |
| Input Price ($/1M) | ~$0.15 | $0.15 | $0.25 |
| Output Price ($/1M) | ~$0.60 | $0.60 | $1.25 |
| Max Output Tokens | 4K | 4K | 4K |
| Throughput | ~120 tps | ~150 tps | ~100 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 9.8B
- Prompt tokens processed (30 days)
- 720M
- Completion tokens generated (30 days)
- 12.5M
- API requests served (30 days)
- 99.8%
- Avg uptime over 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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Unified AI Routing
Automatically route each request to the best model across providers based on latency, cost, and capability—without changing your integration or redeploying.
One endpoint, every model -
Cost-Aware Orchestration
Optimize spend by mixing premium and budget models per request, enforcing hard budgets and quotas with centralized policies instead of per-provider custom logic.
Cut costs, keep quality -
Resilient Fallback Flows
Define provider-agnostic failover chains so timeouts, rate limits, or outages automatically retry against backup models—keeping production apps responsive and reliable.
No single point of failure -
Full-Stack Observability
Get unified logs, traces, metrics, and structured events for every model call, across all vendors, to debug latency, errors, and quality from one place.
See every token, everywhere -
Task-Level Abstractions
Call high-level tasks—chat, tools, retrieval, structured output—without wiring provider-specific APIs, freeing you to evolve models without refactoring application code.
Code to tasks, not models -
High-Throughput Batch Runs
Run large offline workloads—evaluations, backfills, fine-tuning prep—through a single batch API with queuing, retries, and cost controls built in.
Scale batches without chaos
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a capable general-purpose LLM for chatbots and virtual assistants deployment.
- You need solid coding assistance for common programming languages and everyday software engineering tasks.
- Your use case involves multilingual chat or content, especially including strong Chinese language support.
- You need a balance of quality and cost for large-scale text generation workloads.
- Your use case involves summarizing or transforming moderately long business documents or knowledge articles.
- You need to prototype AI features quickly with a reasonably capable, general LLM backend.
Avoid if...
- You need frontier-level reasoning performance on complex math, logic, or scientific problems.
- Your workload requires state-of-the-art code generation and debugging on very large codebases.
- You need guaranteed, best-in-class safety filters and enterprise compliance certifications across jurisdictions.
- Your workload requires extremely long context handling for book-length documents or massive transcripts.
- You need a fully open-source, self-hostable model with transparent weights and training data.
- Your workload requires tight integration with a specific proprietary ecosystem MiniMax does not support.
FAQ
Frequently Asked Questions
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What is MiniMax M2.5?
MiniMax M2.5 is a general-purpose large language model by MiniMax focused on fast, cost-efficient text generation for mainstream application workloads.
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What is the context window of MiniMax M2.5 via LLM.API?
MiniMax M2.5 supports up to a 32,768-token context window when accessed through LLM.API.
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What is MiniMax M2.5 best suited for?
MiniMax M2.5 is best for chatbots, content generation, lightweight reasoning, and other latency-sensitive, high-throughput text applications.
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How much does it cost to use MiniMax M2.5 on LLM.API?
LLM.API exposes MiniMax M2.5 with usage-based pricing per 1,000 tokens for input and output; check the LLM.API pricing page for current rates.
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How fast is MiniMax M2.5 in terms of latency and throughput?
MiniMax M2.5 is optimized for low latency and high throughput, making it suitable for real-time and large-scale concurrent request scenarios.
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What modalities does MiniMax M2.5 support on LLM.API?
On LLM.API, MiniMax M2.5 supports text input and text output; it does not natively handle images, audio, or video.
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How do I call MiniMax M2.5 through LLM.API?
You select the MiniMax M2.5 model name in LLM.API requests, send standard chat or completion payloads, and receive responses in a unified JSON schema.
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How does MiniMax M2.5 compare to similar mid-tier LLMs?
MiniMax M2.5 typically offers a tradeoff of lower cost and faster responses with somewhat weaker reasoning and coding than top-tier flagship models.
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What are the main limitations of MiniMax M2.5?
MiniMax M2.5 can hallucinate facts, struggle with very complex multi-step reasoning, and lacks up-to-date real-world knowledge beyond its training cutoff.
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Can I fine-tune or customize MiniMax M2.5 through LLM.API?
LLM.API currently exposes MiniMax M2.5 as a hosted, non-fine-tunable model, but you can steer behavior using system prompts and few-shot examples.
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