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.

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.

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.

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.

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.

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.

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.

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.

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.

Which endpoint do I call for MiniMax M2.5?

POST https://api.llmapi.ai/v1/chat/completions with model "minimax-m2.5" and your LLM.API key. The request and response bodies are OpenAI-compatible.

How do I switch from OpenAI to MiniMax M2.5 on LLM.API?

Change two lines: set base_url to https://api.llmapi.ai/v1 and api_key to your LLM.API key. The rest of your OpenAI SDK code stays the same.

Does MiniMax M2.5 support function calling and structured outputs?

Function calling: supported via tools. Structured outputs: supported via response_format.

MiniMax M2.5: Self-Hosting & Deployment Guide

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.

Start Using API

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.

How to run MiniMax M2.5 yourself

Open weights (other), 228.7B parameters. Below: how much memory the weights need at each quantization, which hardware fits them, and the commands to serve it.

MiniMax M2.5 VRAM requirements

Precision / quantization	Weights	Figure	Source
BF16 (full precision)	457.4 GB	Estimate	228.7B params × 2 bytes
AWQ 4-bit	130.5 GB	File size	cyankiwi/MiniMax-M2.5-AWQ-4bit
MLX 4-bit	128.7 GB	File size	lmstudio-community/MiniMax-M2.5-MLX-4bit
GGUF Q8_0	243.1 GB	File size	unsloth/MiniMax-M2.5-GGUF
GGUF Q4_K_M	138.3 GB	File size	unsloth/MiniMax-M2.5-GGUF
GGUF Q2_K	83.3 GB	File size	unsloth/MiniMax-M2.5-GGUF

File size = actual download size on Hugging Face. Estimate = parameter count × bytes per weight. Weights only — the KV cache for long contexts needs extra memory; the fit tables below add 15% headroom as a rule of thumb.

GPU requirements: deploy MiniMax M2.5 on your hardware

Datacenter GPUsConsumer GPUsApple SiliconCloud instances

Hardware	BF16 (full precision)	AWQ 4-bit	MLX 4-bit	GGUF Q8_0	GGUF Q4_K_M	GGUF Q2_K
NVIDIA H200141 GB	4×	2×	2×	2×	2×	1×
NVIDIA H10080 GB	7×	2×	2×	4×	2×	2×
NVIDIA A100 80GB80 GB	7×	2×	2×	4×	2×	2×
NVIDIA L40S48 GB	—	4×	4×	6×	4×	2×

GPUs needed per variant.

Hardware	GGUF Q8_0	GGUF Q4_K_M	GGUF Q2_K
RTX 509032 GB	—	5×	3×
RTX 409024 GB	—	7×	4×
RTX 309024 GB	—	7×	4×

GGUF via llama.cpp; more cards or CPU offload for larger files.

Hardware	GGUF Q8_0	GGUF Q4_K_M	GGUF Q2_K	MLX 4-bit
Mac, 512 GBM3 Ultra	Fits	Fits	Fits	Fits
Mac, 192 GBM2 Ultra	—	—	Fits	—
Mac, 128 GBM4 Max / M3 Max	—	—	Fits	—
Mac, 64 GBM4 Pro / Max	—	—	—	—

Assumes ~75% of unified memory is usable by the GPU.

Hardware	BF16 (full precision)	AWQ 4-bit	MLX 4-bit	GGUF Q8_0	GGUF Q4_K_M	GGUF Q2_K
AWS p5.48xlarge8× H100 · 640 GB	Fits	Fits	Fits	Fits	Fits	Fits
AWS p4de.24xlarge8× A100 80GB · 640 GB	Fits	Fits	Fits	Fits	Fits	Fits
AWS g6e.12xlarge4× L40S · 192 GB	—	Fits	Fits	—	Fits	Fits
Google Cloud a3-highgpu-8g8× H100 · 640 GB	Fits	Fits	Fits	Fits	Fits	Fits
Azure ND H100 v58× H100 · 640 GB	Fits	Fits	Fits	Fits	Fits	Fits

Whole-instance GPU memory; public instance specs.

Serve it yourself

vLLMSGLangllama.cpp (GGUF)MLX (Mac)

pip install -U vllm
vllm serve MiniMaxAI/MiniMax-M2.5

pip install 'sglang[all]'
python -m sglang.launch_server --model-path MiniMaxAI/MiniMax-M2.5

llama-server -hf unsloth/MiniMax-M2.5-GGUF:Q4_K_M \
  --ctx-size 32768 --n-gpu-layers 99

pip install -U mlx-lm
mlx_lm.generate --model lmstudio-community/MiniMax-M2.5-MLX-4bit --prompt "Hello"

Commands use the official repositories above; vLLM and SGLang expose an OpenAI-compatible endpoint.

Skip the deploy — use LLM.API as your endpoint

No GPUs, no quantization trade-offs. Same model, OpenAI-compatible, up to 30% below list price.

Get API key See API code ↓

Providers

Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro (fastest), or Exacto (highest tool-calling accuracy).

List price by provider

InputOutput

aws-bedrock$0.3 in

$1.2 out

embercloud$0.2 in

$1.2 out

novita$0.3 in

$1.2 out

Provider list prices; the LLM.API discount applies on top.

Provider	Input	Output	Cache read /M
aws-bedrock	in $0.3; out $1.2 per 1M tokens	205K tokens	tools, streaming, JSON, structured
embercloud	in $0.2; out $1.2 per 1M tokens	205K tokens	tools, streaming, reasoning, JSON
novita	in $0.3; out $1.2 per 1M tokens	205K tokens	tools, streaming, reasoning, JSON

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 11 Oct 2026.

Try this model

Test MiniMax M2.5 right here — free to start.

MiniMax M2.5

Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call the model through the OpenAI-compatible API.

Switching from OpenAI? Change 2 lines.

- base_url="https://api.openai.com/v1"
- api_key="YOUR_OPENAI_KEY"
+ base_url="https://api.llmapi.ai/v1"
+ api_key="YOUR_LLMAPI_KEY"

Everything else stays the same — SDK, message format, tools, streaming. Set model to minimax-m2.5 and the call runs unchanged.

bash

curl https://api.llmapi.ai/v1/chat/completions \
  -H "Authorization: Bearer $LLMAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "minimax-m2.5",
    "messages": [
      {"role": "user", "content": "Give me three crisp launch checklist items."}
    ]
  }'

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMAPI_KEY",
    base_url="https://api.llmapi.ai/v1",
)

resp = client.chat.completions.create(
    model="minimax-m2.5",
    messages=[
        {"role": "system", "content": "You are a precise product assistant."},
        {"role": "user", "content": "Give me three crisp launch checklist items."},
    ],
)
print(resp.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.LLMAPI_KEY,
  baseURL: "https://api.llmapi.ai/v1",
});

const resp = await client.chat.completions.create({
  model: "minimax-m2.5",
  messages: [
    { role: "user", content: "Give me three crisp launch checklist items." },
  ],
});

console.log(resp.choices[0].message.content);

stream = client.chat.completions.create(
    model="minimax-m2.5",
    messages=[{"role": "user", "content": "Write a haiku about latency."}],
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta.content
    if delta:
        print(delta, end="", flush=True)

{
  "model": "minimax-m2.5",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ]
}

ENDPOINTS

Supported endpoints & features

Base URL https://api.llmapi.ai/v1 · model ID minimax-m2.5 · OpenAI-compatible.

POST /v1/chat/completionsChat Completions — supported

Feature	Status	How to set it
Function calling (tools)	Supported	`tools, tool_choice`
Parallel tool calls	Not supported	`parallel_tool_calls`
JSON mode	Supported	`response_format: {"type": "json_object"}`
Structured outputs	Supported	`response_format: {"type": "json_schema", …}`
Streaming	Supported	`stream: true`
Vision (image input)	Not supported	`image_url content parts`
Web search	Not supported	`web_search`

ERRORS

Errors & fallback routing

Code	What it means	What to do
`401`	Missing or invalid API key.	Send `Authorization: Bearer YOUR_LLMAPI_KEY` and check the key is active.
`429`	Rate limited, or the upstream provider is throttling the request.	Back off and retry with jitter; the gateway also retries the request on another provider where one is available.
`5xx`	Upstream provider error or timeout.	Retry; fallback routing sends the retry to the next healthy provider for this model.

When a provider fails or throttles, the request is routed to the next provider serving this model — the ones listed in the providers table above.

MiniMax M2.5: Self-Hosting & Deployment Guide in production

Best workloadsLLM.API features

What the model is good at.

Advanced Coding

Delivers state-of-the-art multilingual code generation, debugging, and full lifecycle software development across over ten programming languages.
Agentic Tool Use

Coordinates complex multi-step tasks, calling external tools and search services efficiently for real-world automation and agent workflows.
Long-Context Reasoning

Handles very large text contexts with efficient reasoning traces, supporting extended documents, conversations, and multi-stage problem solving.
Business Productivity

Automates office workflows such as document drafting, summarization, reporting, and analysis to support knowledge work across business functions.
Multilingual Text

Understands and generates text in many languages, enabling cross-lingual communication, content creation, and localization scenarios.

What you get when you call MiniMax M2.5: Self-Hosting & Deployment Guide through LLM.API.

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

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.

LLM API rating for MiniMax M2.5

★ 3.4/ 5.0LLM API rating
50% production adoption · 30% category benchmark · 20% tooling & value✓ Verified benchmark · LMArena + Artificial Analysis

3.5Production adoptionActive open-weight community usage

2.7Benchmark qualityArtificial Analysis Intelligence Index 22.8 · LMArena text rank #191, Elo 1359

4.0Developer ecosystemContext window, tool and JSON support, price against the category median

Reasoning & chat 3.4Coding 3.4Tool & function calling 3.9Long context 3.6

How is this rating calculated?

Every model gets a separate 1.0–5.0 score for each category it can be used in, so a model can rate high for coding and lower for vision. The score combines three parts:

Production adoption — 50%. How widely the model is actually used in production: provider traffic, LiteLLM and gateway usage, open-weight downloads and its LMArena rank. Proven, widely used models score highest.
Category benchmark — 30%. The public benchmark that matters for that category — the Artificial Analysis Intelligence Index and LMArena Elo for reasoning, LMArena coding Elo for coding, image and document support plus vision Elo for vision, tool and JSON support for function calling, measured speed for voice.
Tooling & value — 20%. Context window, support for tools, structured outputs and streaming, and price compared with the category median.

Scores of 4.0 and above are shown in green, 3.0–3.9 in yellow, below 3.0 in red. When a model has no public data for a part, it gets a neutral value instead of an invented number.

A “Verified benchmark” mark (✓) means an independent lab — LMArena or Artificial Analysis — has actually tested that model, and the source figure is shown. Models without that mark are labelled “Estimated from published specs”: the score comes from the published context window, tool and schema support, price and adoption only, because no independent lab has tested them yet.

One exception: a brand-new top-tier model that scores very high on the independent benchmarks but has no usage history yet is rated on the benchmark (60%) and tooling & value (40%) alone, so a missing adoption figure can't hold it back. All inputs come from public sources and are refreshed weekly.

Adoption and benchmark inputs come from public sources — LMArena, Artificial Analysis Intelligence Index and published model specs. Updated weekly.

COMMUNITY

What developers say about MiniMax M2.5

LMArena#191 · Elo 1359Arena coding#184 · Elo 1380Speed—Sources: LMArena, Artificial Analysis

Summarised from public developer discussions and write-ups. Opinions are the sources’, not LLM.API’s.

Developers on Hacker News report that MiniMax M2.5 demonstrates strong architectural planning and thinking, passing recent logic tests that other state-of-the-art models fail, though it can suffer from performance slowdowns.
Reviewers highlight the model's exceptional cost-performance ratio for coding tasks, noting it matches frontier models like Claude Opus 4.6 on benchmarks while being significantly cheaper to run via API.
Some developers express concerns regarding factual reliability, citing a reported increase in hallucination rates compared to previous versions and potential issues with context window management on local hardware.

SOURCES

Frequently Asked Questions

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.
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.
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.
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.
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.
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.
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.
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.
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.
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.

COMPARE

Competitive Models

MiniMax M2 is an open‑weight Mixture‑of‑Experts large language model from MiniMax, designed to deliver high coding and agentic workflow performance with low latency and cost. It uses 230B total parameters with only about 10B active per token to balance strong reasoning with efficient deployment.

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.

MiniMax M2.5 (free)

Up to 30%

Instruction Following

MiniMax M2.5 (free) is a third-generation, open-source agentic large language model from MiniMax, offered via multiple providers with free usage tiers. It is notable for its long context window and strong coding and productivity capabilities while remaining cost-efficient.

Get one key to every model

Swap your API key. Keep your code.

Start for free Talk to sales

LLM API sells to AI agents - live prices and self-serve payment

MiniMax M2.5: Self-Hosting & Deployment Guide

What is MiniMax M2.5?

How to run MiniMax M2.5 yourself

MiniMax M2.5 VRAM requirements

GPU requirements: deploy MiniMax M2.5 on your hardware

Serve it yourself

Providers

Try this model

Code snippet

Supported endpoints & features

Errors & fallback routing

MiniMax M2.5: Self-Hosting & Deployment Guide in production

Advanced Coding

Agentic Tool Use

Long-Context Reasoning

Business Productivity

Multilingual Text

Unified AI Routing

Cost-Aware Orchestration

Resilient Fallback Flows

Full-Stack Observability

Task-Level Abstractions

High-Throughput Batch Runs

When to Use — When NOT to Use

Use it if...

Avoid if...

LLM API rating for MiniMax M2.5

What developers say about MiniMax M2.5

Frequently Asked Questions

Competitive Models

MiniMax M2.7

Codestral

GLM-4.6V

MiniMax M2.7

MiniMax M2.5 (free)

Get one key to every model