MiniMax M3.1 Flash
MiniMax-M3.1-FlashCompare MiniMax-M3.1-Flash API pricing, supported endpoints, capabilities and access options on anyumodel.
- Starting price
- Input / Output · 1M
- Context
- —
- Maximum input window
- Modalities
- →
Pricing by Supplier
| Input/ 1M tokens | Output/ 1M tokens | Cache Read/ 1M tokens | Cache Write (5m)/ 1M tokens | Cache Write (1h)/ 1M tokens |
|---|---|---|---|---|
$0.24 | $0.96 | $0.048 | $0.24 | $0.384 |
- Input/ 1M tokens
- $0.24
$0.3 - Output/ 1M tokens
- $0.96
$1.2 - Cache Read/ 1M tokens
- $0.048
$0.06 - Cache Write (5m)/ 1M tokens
- $0.24
$0.3 - Cache Write (1h)/ 1M tokens
- $0.384
$0.48
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 M3.1 Flash
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 M3.1 Flash
复杂任务与自动化能力：
在 AutomationBench-AA 中位居最前列。在跨 SaaS 系统的真实企业业务工作流中，能完整解决销售、营销、运营、客服、财务、HR 的真实问题。 在 BankerToolBench 中 76.2 分，擅长多步骤规划、跨文档 / 跨文件一致性、金融业务合规、工具链式调用等真实复杂问题。 编程与软件工程能力：
在 DeepSWE v1.1 评测中取得 73.7% 的成绩，达到顶尖模型水平。DeepSWE 聚焦复杂软件任务，要求模型独立完成代码理解、功能开发、问题修复与测试验证。M3.1 Flash 展现出出色的长链路任务执行能力，能够以更低的成本支持复杂的软件开发工作流。 Computer Use 能力：
在衡量 AI 电脑操作与跨应用任务执行能力的 OSWorld V2 中取得 61.7 分，在衡量真实并有经济价值场景下电脑操作能力的 Agents' Last Exam（ALE）中取得 29.6 分，两项得分均位列国产模型第一。 M3.1 Flash 展现出出色的真实环境交互、工具使用与长链路任务执行能力，能够从理解任务进一步走向实际操作与交付。 网络安全能力：
在 CyberGym 基准测试中取得 82% 的高分，展现出领先的漏洞发现与验证能力。 经内部安全专家深度评测，模型在代码审计、漏洞修补、渗透测试、恶意代码分析与逆向工程等场景下均表现出色，能够以低成本、高效率支持复杂的安全体系建设。
Use cases and prompting
Starting points for evaluation; supported inputs and options are listed in API access.
Use cases to explore
- Draft and revise text with explicit audience, tone and format requirements.
- Summarize supplied documents and compare answers against the original sources.
Practical tips
Separate instructions from source material, describe the desired output and provide an example. Test tool use or structured output only when listed as supported.
Example prompt
Summarize the following document in five bullet points. Separate confirmed facts from open questions, cite the relevant passages and do not invent missing information. Document: [paste your text]
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-M3.1-Flash
What is MiniMax-M3.1-Flash?
Compare MiniMax-M3.1-Flash API pricing, supported endpoints, capabilities and access options on anyumodel.
How do I call MiniMax-M3.1-Flash?
Create an API key with access to MiniMax-M3.1-Flash, 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-M3.1-Flash 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.
How should I evaluate MiniMax-M3.1-Flash for my project?
Separate instructions from source material, describe the desired output and provide an example. Test tool use or structured output only when listed as supported.