MiniMax M2.5
MiniMax/MiniMax-M2.5Compare MiniMax/MiniMax-M2.5 API pricing, supported endpoints, capabilities and access options on Modelsell.
- Starting price
- Input / Output · 1M
- Context
- 204.8K
- Maximum input window
- Max output
- 128K
- Maximum tokens per response
- Modalities
- →
Pricing by Supplier
Capabilities / Supported modalities
Provider & data privacy
ABAB tokenizerPerformance
Benchmarks
Scores on standardized evaluations. Higher percentages are better — and rank percentile shows
Metrics sourced fromArtificial Analysis 2026-09-30·MiniMax M2.5
This model is not included in the current benchmark snapshot.
Missing models or measurements are not zero scores.
Benchmark charts preserve the source model selection and reasoning settings. Missing models or measurements are not zero scores, and benchmark cost or speed is not this site’s service commitment.
About MiniMax M2.5
把一项工作拆成能执行的步骤
MiniMax M2.5 适合需要先理解目标、再连续执行的生产力任务。它可以规划软件功能、阅读与修改代码，也可以组织检索、分析资料和起草办公内容。对于开发助手和业务智能体，可以让它围绕一个明确交付物安排步骤，并根据工具反馈修正计划。
编程时，它能够从功能、结构和界面设计开始考虑实现，适用于多种语言与全栈项目。既可以讨论后端 API、业务逻辑和数据存储，也可以协助 Web 与移动端功能迭代。给出现有代码、项目规范和兼容要求，有助于让设计与实现衔接起来。
它以文字处理任务，输入可以是需求、源码、日志和文档。大型工作可先建立一份简短计划，明确每步产物与验收行为，再依次实现和测试。模型生成的代码需要在真实环境中运行；工具调用后也应检查返回结果，而不是只根据预期继续下一步。
办公场景可以围绕研究报告、演示文稿和表格分析展开。模型能够规划内容、整理证据与编写计算逻辑，实际文件编辑和版式检查则需要相应工具。接入成熟的文档、表格与检索工具后，它可以参与更完整的交付流程。
如何组织任务
适合做全栈迭代吗？ 可以把页面行为、接口、数据模型与错误处理一起给出，分阶段完成并核对端到端效果。
怎样让计划不过于空泛？ 要求每个步骤说明需要读取的材料、实际动作、产物和验证方法，发现信息缺口时先列明。
Use cases and prompting
先说明交付物及工作边界
给出已有项目和验收流程，让规划直接服务于实现。
为现有预约系统增加改期功能。
先检查预约状态、时间冲突校验和通知逻辑，再规划页面、接口与数据变更。
实现最小可用流程，保留取消规则，验证并发改期、无可用时段和通知失败。
运行相关测试，最终说明修改内容和端到端验证结果。
检索工作指定证据标准与范围；办公工作明确文件格式、计算口径和版式要求。使用工具时返回真实操作结果，并将未完成事项与完成内容分开报告。
API access
API documentation
Code samples
/v1/chat/completions| Parameter | Type | Default / range | Description |
|---|---|---|---|
temperature | number | = 10 ~ 2 | Sampling temperature; lower is more deterministic |
top_p | number | = 10 ~ 1 | Nucleus sampling probability mass |
max_tokens | integer | >= 1 | Maximum number of tokens in the response |
frequency_penalty | number | = 0-2 ~ 2 | Penalises repetition of frequent tokens |
presence_penalty | number | = 0-2 ~ 2 | Encourages introducing new topics |
stop | array | — | Up to 4 strings that stop generation |
seed | integer | — | Deterministic sampling seed (best-effort) |
n | integer | = 1>= 1 | Number of completions to generate |
stream | boolean | = false | Stream tokens via Server-Sent Events |
response_format | object | — | Force JSON object or schema-conforming output |
tools | array | — | Tool / function declarations the model may call |
tool_choice | string | autononerequired | Tool-choice policy or specific tool name |
logprobs | boolean | = false | Return per-token log probabilities |
top_logprobs | integer | 0 ~ 20 | Number of top log probabilities returned per token |
logit_bias | object | — | Per-token logit bias map |
user | string | — | End-user identifier for abuse monitoring |
Replace <YOUR_API_KEY> with the API key from your token settings.
Authentication
All requests must include Authorization: Bearer <TOKEN> header. Anthropic-formatted endpoints accept the x-api-key header instead.
Generate tokens from the Tokens page; you can scope them to specific models, groups, IPs, and rate-limits.
Supported parameters
| Parameter | Type | Default / range | Description |
|---|---|---|---|
temperature | number | = 10 ~ 2 | Sampling temperature; lower is more deterministic |
top_p | number | = 10 ~ 1 | Nucleus sampling probability mass |
max_tokens | integer | >= 1 | Maximum number of tokens in the response |
frequency_penalty | number | = 0-2 ~ 2 | Penalises repetition of frequent tokens |
presence_penalty | number | = 0-2 ~ 2 | Encourages introducing new topics |
stop | array | — | Up to 4 strings that stop generation |
seed | integer | — | Deterministic sampling seed (best-effort) |
n | integer | = 1>= 1 | Number of completions to generate |
stream | boolean | = false | Stream tokens via Server-Sent Events |
response_format | object | — | Force JSON object or schema-conforming output |
tools | array | — | Tool / function declarations the model may call |
tool_choice | string | autononerequired | Tool-choice policy or specific tool name |
logprobs | boolean | = false | Return per-token log probabilities |
top_logprobs | integer | 0 ~ 20 | Number of top log probabilities returned per token |
logit_bias | object | — | Per-token logit bias map |
user | string | — | End-user identifier for abuse monitoring |
Rate limits
| Supplier | RPM | TPM | RPD |
|---|---|---|---|
| official | Unlimited | Unlimited | Unlimited |
No restriction
Frequently asked questions about MiniMax/MiniMax-M2.5
What is MiniMax/MiniMax-M2.5?
Compare MiniMax/MiniMax-M2.5 API pricing, supported endpoints, capabilities and access options on Modelsell.
How do I call MiniMax/MiniMax-M2.5?
Create an API key with access to MiniMax/MiniMax-M2.5, then use the exact model ID and a supported endpoint from the API access section. Request fields depend on the selected endpoint.
How is MiniMax/MiniMax-M2.5 priced?
Pricing depends on the selected provider group and the model billing unit. The current input, output, request, or media prices are shown on this page before sign-up.
What is the context window of MiniMax/MiniMax-M2.5?
The model catalog lists a context window of 204800 tokens. Check the selected endpoint for request limits.
What is the maximum output of MiniMax/MiniMax-M2.5?
The model catalog lists a maximum output of 128000 tokens. Your request settings may set a lower limit.
How should I evaluate MiniMax/MiniMax-M2.5 for my project?
Start with the use cases and prompting guidance on this page, then evaluate the model with representative inputs from your project.