MiniMax M3.1 フラッシュガイド：API、価格、ベンチマーク、パラメータ、および無料アクセス

For this protocol, MiniMax documents 推論の努力 そして 最大完了トークン数. The response can include reasoning metadata, but your application should only display or store it according to your own logging and privacy policy.

Anthropic-compatible example

Anthropic-compatible calls use output_config.effort. The provider’s reference also describes content blocks for text, images, and video, plus tool calls. Check the current protocol reference before copying parameters between the two routes; a parameter name that works in one compatibility layer may be ignored in the other.

For a second perspective on compatible model endpoints, see this Claude APIガイド. It covers a different provider, but the same practical lesson applies: pin the base URL, model ID, protocol, and error handling together.

Parameters, thinking, tools, and multimodal limits

Thinking is forced on for M3.1 Flash according to the official API references. You control its intensity through the documented effort field: ロー, 中, 高, xhigh, あるいは max. Treat effort as a workload setting rather than a quality guarantee: raise it for difficult planning or debugging, and start lower for short structured transformations.

Multimodal support does not mean every client library will serialize every content block correctly. Test the exact SDK, content shape, and endpoint you plan to deploy. Keep file size checks on your side so a rejected 50 MB video does not become a confusing generic 4xx response.

Long-context and multimodal systems also make data handling more important. Before sending private repositories, screenshots, or videos, review your retention and access assumptions; this AIデータプライバシーガイド is a useful checklist for that conversation.

What our GlobalGPT tests returned

We tested three real tasks on October 26, 2026: an exact JSON response, a JavaScript debugging fix, and a three-step HTTP 503 diagnosis. Each task completed successfully. The results below are functional observations from that test session, not a general benchmark.

テスト	タスク	結果	Observed usage
T1	Exact JSON reply	{"status":"READY","model":"MiniMax M3.1 Flash"}	5.757s · 3,958 in / 17 out
T2	JavaScriptのデバッグ	返却済み Number(a) + Number(b) and explained string concatenation	11.976s · 3,979 in / 511 out
T3	Three-step HTTP 503 diagnosis	Identified likely causes and recommended the next diagnostic checks	11.954s · 3,991 in / 376 out

All three generations completed. The platform metadata reported customerChargeAmount: 0 for these test turns. That is an observation for this authenticated account/session, not evidence that every GlobalGPT user has free access.

This is a small functional check, not a benchmark. It shows that the verified route handled exact formatting, a short code correction, and a multi-step diagnostic response on the recorded day. It does not establish average latency, throughput, general coding quality, or a stable price across accounts.

For practical use, start with a small request, confirm the model name and request format, then scale after the response and cost match your workflow.

Free access, M Plan, and MiniMax Code

There are three different questions hidden inside “Is MiniMax M3.1 Flash free?”

• 公式API： the published route has token prices, so it is usage-billed rather than universally free.
• MiniMax products: MiniMax says the model is available through the M Plan and MiniMax Code. Plan inclusion, quotas, and regional availability are separate from API billing.
• Hosted platforms: our GlobalGPT test account showed a zero customer charge for three completed turns. That observation does not establish a permanent free tier, a public quota, or free access for every account.

If you want to explore hosted access without setting up a direct API key, use the オールインワンAIモデルガイド to compare platform workflows, then verify the current model card and account terms before a larger run. Do not compare a platform’s credits with MiniMax token prices until you know what each unit buys.

M3.1 Flash vs M3

M3.1 Flash is the newer Flash-branded release in the checked October 2026 material. The useful comparison is therefore operational: do you want the newer 1M-context, speed-focused model and its documented thinking controls, or are you maintaining an existing M3 integration that has already been tuned and validated?

MiniMax’s checked public material does not give this article a like-for-like M3-versus-M3.1 Flash score table. That is why the comparison above focuses on documented interface and migration decisions instead of inventing a numerical winner.

どのような人に適していますか？

フィット感良好： developers building agents, coding assistants, repository analyzers, computer-use flows, or long-context workflows that benefit from adjustable reasoning effort and tool calls. The low direct input rate also makes it practical for repeated context-heavy experiments when your cache strategy is sound.

Use caution: teams that require a stable, independently verified benchmark, a guaranteed free tier, or a single compatibility endpoint across every provider. Test structured output, tool-call arguments, error handling, and the exact region before moving production traffic.

For a broader evaluation, pair a model test with your actual data-analysis or coding workflow. The tested AI data-analysis tools guide is a useful reminder that a model score alone does not measure the quality of the whole workflow.

評決

MiniMax M3.1 Flash is a credible low-cost option for agentic coding and long-context work. The direct API price is easy to model, the compatibility layers reduce migration work, and the documented multimodal and tool support make it more useful than a text-only “fast” model. The strongest evidence here is the combination of official interface details and three completed functional tests.

Keep the boundaries clear: MiniMax-reported benchmark scores are not independent measurements, and The observed zero-charge result applies to that test session. Start with a small side-by-side test, then scale only after your prompts, tools, budgets, and access route behave the way your application needs.

If you want a hosted starting point, open the GlobalGPT workspace and check the current MiniMax M3.1 Flash model card before submitting production work.

よくあるご質問

What is MiniMax M3.1 Flash?

MiniMax M3.1 Flash is a 1M-context reasoning model released on October 19, 2026 for agentic coding, computer-use, tool-calling, cybersecurity, and other long-context workflows.

How much does MiniMax M3.1 Flash cost?

The official API rates checked on October 26, 2026 are $0.15 per million input tokens, $0.60 per million output tokens, and $0.015 per million cached input tokens. These are direct MiniMax API rates.

Does MiniMax M3.1 Flash have a 1M context window?

Yes. MiniMax’s official invocation guide lists a 1M-token context window. Your effective usable context still depends on the endpoint, request shape, tool payloads, and output budget.

Can I call it through the OpenAI SDK?

Yes. MiniMax documents an OpenAI-compatible base URL and the model ID MiniMax-M3.1-Flash. The documented controls include reasoning_effort and max_completion_tokens; validate the exact route before production use.

Can I use an Anthropic-compatible API?

Yes. MiniMax documents an Anthropic-compatible messages route using output_config.effort for thinking effort, along with text, image, video, and tool content blocks.

Is MiniMax M3.1 Flash free?

There is no universal free API claim in the checked sources. MiniMax lists paid token rates and also names M Plan and MiniMax Code as access routes. The three-turn test session showed zero charge at the time of testing; this observation can vary by plan and account.

What did the GlobalGPT tests show?

Three real-task runs completed successfully: exact JSON, JavaScript debugging, and a three-step HTTP 503 diagnosis. Observed durations were 5.757, 11.976, and 11.954 seconds; these are single-run observations, not a latency benchmark.

What are the multimodal input limits?

The checked API references list images up to 10 MB, video URLs or base64 up to 50 MB, and video through the Files API up to 512 MB. Confirm the current endpoint and client serialization before sending production media.