MiniMax M3
MiniMax/MiniMax-M3Compare MiniMax/MiniMax-M3 API pricing, supported endpoints, capabilities and access options on Modelsell.
- Starting price
- Input / Output · 1M
- Context
- 1M
- Maximum input window
- Max output
- 512K
- 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 M3
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 M3
在同一项工作中连接文字与视觉材料
MiniMax-M3 是原生多模态模型，可以联合理解文本、图片与视频，并用文本给出分析和实现结果。它适合资料较多、推进步骤较长的开发与办公协作，例如结合需求文档和演示视频理解产品，再整理实现计划或修改建议。
面对跨材料任务，可以先建立共同的问题清单：文档承诺了什么，实际页面呈现了什么，操作视频中又发生了什么。模型能够把这些线索放在同一背景下分析，帮助发现描述与实现之间的差异。较长上下文也便于保留项目约定、讨论历史和工具反馈，让后续步骤接着已有结论推进。
它的应用重点包括编程和协作型知识工作。提供代码、业务约束与验收要求后，可以让它拆解任务、安排检查，并在工具返回结果后修正方案。文件编辑、命令运行和业务查询等实际操作，需要应用提供对应工具与执行环境。
M3 提供三种思考模式：enabled 始终思考，adaptive 由模型判断何时需要更多推理，disabled 直接回答。可以根据任务复杂程度选择，简单整理与复杂决策不必采用同一设置。为了方便检查，建议结果明确列出材料依据、已完成动作和待验证内容，让协作进度能被其他人接续。
Use cases and prompting
产品验收分析示例
“请比较需求文档、当前页面截图和操作视频。按功能列出预期行为、实际表现和差异，保留对应段落或视频位置。把问题分为功能缺失、交互不清和需要进一步验证三类，再给出按依赖排序的修复计划。”
常见问题
- 思考模式如何选？复杂对照与开发判断可用 enabled；混合任务可试 adaptive；简单改写可用 disabled。
- 视频与文档有冲突时怎么办？要求同时保留两边依据，并指出还需要哪些检查才能定论。
- 能直接修改项目吗？需要文件和执行工具；完成后检查实际差异与运行结果，确认任务达到验收标准。
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 |
| Alibaba | Unlimited | Unlimited | Unlimited |
No restriction
Frequently asked questions about MiniMax/MiniMax-M3
What is MiniMax/MiniMax-M3?
Compare MiniMax/MiniMax-M3 API pricing, supported endpoints, capabilities and access options on Modelsell.
How do I call MiniMax/MiniMax-M3?
Create an API key with access to MiniMax/MiniMax-M3, 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-M3 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-M3?
The model catalog lists a context window of 1048576 tokens. Check the selected endpoint for request limits.
What is the maximum output of MiniMax/MiniMax-M3?
The model catalog lists a maximum output of 512000 tokens. Your request settings may set a lower limit.
How should I evaluate MiniMax/MiniMax-M3 for my project?
Start with the use cases and prompting guidance on this page, then evaluate the model with representative inputs from your project.