MiniMax M2.7
MiniMax-M2.7Compare MiniMax-M2.7 API pricing, supported endpoints, capabilities and access options on Modelsell.
- Starting price
- Input / Output · 1M
- Context
- 204.8K
- Maximum input window
- Max output
- 176.9K
- Maximum tokens per response
- Modalities
- →
Pricing by Supplier
| Input/ 1M tokens | Output/ 1M tokens | Cache Read/ 1M tokens | Cache Write (5m)/ 1M tokens | Cache Write (1h)/ 1M tokens |
|---|---|---|---|---|
$0.255 | $1.02 | $0.051 | $0.31875 | $0.51 |
- Input/ 1M tokens
- $0.255
$0.3 - Output/ 1M tokens
- $1.02
$1.2 - Cache Read/ 1M tokens
- $0.051
$0.06 - Cache Write (5m)/ 1M tokens
- $0.31875
$0.375 - Cache Write (1h)/ 1M tokens
- $0.51
$0.6
| Input/ 1M tokens | Output/ 1M tokens | Cache Read/ 1M tokens | Cache Write (5m)/ 1M tokens | Cache Write (1h)/ 1M tokens |
|---|---|---|---|---|
$0.3 | $1.2 | $0.06 | $0.375 | $0.6 |
- Input/ 1M tokens
- $0.3
- Output/ 1M tokens
- $1.2
- Cache Read/ 1M tokens
- $0.06
- Cache Write (5m)/ 1M tokens
- $0.375
- Cache Write (1h)/ 1M tokens
- $0.6
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 M2.7
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 M2.7
把排查线索串成可验证的判断
MiniMax-M2.7 面向真实工作中的编程、智能体协作和办公生产任务。它以文本为输入输出，适合日志分析、故障调查、重构和文档生成，也能在工具环境中组织多个角色共同完成复杂工作。
工程排障时，可以把监控变化、请求链路、错误日志和数据库记录交给它，让它比较不同线索是否指向同一原因。它的应用重点不止是生成代码，还包括系统层面的判断：故障发生在哪一层，哪些证据支持当前假设，应该先做哪项检查。要求它区分观察与推断，能让排查过程更便于团队接手。
办公场景中，它可以协助生成与多轮修改 Word、Excel 和 PowerPoint 交付物。实际文件操作需要应用提供相应工具；开始时给出模板、数据口径和布局要求，修改时明确保留哪些内容，可以减少反复整理的成本。支持任务编排的应用还可以按资料整理、分析和审阅拆分角色，再合并结果。
它支持函数调用，也可以返回 JSON。需要程序接收结果时，应在应用侧检查字段和类型，不要把 JSON 格式等同于已经通过业务校验。无论做排障还是文档，最后都应保留验证记录：原因是否被复现、修复是否生效、文件中的公式和内容是否符合要求。
Use cases and prompting
故障调查提示词
“请根据请求链路、监控曲线的文字数据和错误日志分析这次超时。按时间顺序串联事实，列出最可能的故障层及相反证据。先设计验证根因的检查，再提出最小修复与回滚方法；不要把单条报错当作完整请求结果。”
常见问题
- 怎样让排查结论更可信？同时提供日志、指标与请求编号，要求说明各条证据如何关联。
- 能直接交付办公文件吗？需要文件处理工具；应检查生成文件的布局、公式和可编辑性。
- JSON 输出能直接入库吗？先做解析和业务校验，特别检查必填项、枚举和金额等关键字段。
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 |
|---|---|---|---|
| Alibaba | Unlimited | Unlimited | Unlimited |
| official | Unlimited | Unlimited | Unlimited |
No restriction
Frequently asked questions about MiniMax-M2.7
What is MiniMax-M2.7?
Compare MiniMax-M2.7 API pricing, supported endpoints, capabilities and access options on Modelsell.
How do I call MiniMax-M2.7?
Create an API key with access to MiniMax-M2.7, 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-M2.7 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-M2.7?
The model catalog lists a context window of 204800 tokens. Check the selected endpoint for request limits.
What is the maximum output of MiniMax-M2.7?
The model catalog lists a maximum output of 176947 tokens. Your request settings may set a lower limit.
How should I evaluate MiniMax-M2.7 for my project?
Start with the use cases and prompting guidance on this page, then evaluate the model with representative inputs from your project.