MoE-Powered Efficiency
Utilizes Mixture-of-Experts architecture to deliver low TTFT and high-speed processing for complex reasoning tasks.
MiniMax-M3 / image-to-text
The MiniMax M3 api powers high-intelligence workflows with a 1M token context window and MoE architecture. It excels in bilingual reasoning, native multimodal fusion, and code analysis, offering GPT-4o level logic at a fraction of the cost.
$ 0.48
$ 0.6
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$ 0.96
$ 1.2
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Core MiniMax M3 api Features
Key technical advantages that set the MiniMax M3 api apart from other LLMs.
Native Multimodal Fusion
Processes interleaved text, image, and audio inputs for unified reasoning without the lag of late-fusion models.
Bilingual Logical Reasoning
Specifically optimized for English and Chinese, achieving elite scores in MATH and GSM8K reasoning benchmarks.
1M Token Context window
Maintains 99.9% retrieval accuracy across 1 million tokens, outperforming dense models in document-heavy analysis.
How to Get a MiniMax-M3 API Key
Getting a MiniMax-M3 API key takes four steps and a few minutes. Create a free GPTProto account, add credits, generate your key, and make your first call — at $0.48 / $0.96 it's a cheaper MiniMax-M3 API key than going direct, and one key works across every model on the platform. Full MiniMax-M3 Documentation is in the docs.
Sign up
Create your free GPT Proto account to begin. You can set up an organization for your team at any time.
Top up
Your balance can be used across all models on the platform, including MiniMax-M3, giving you the flexibility to experiment and scale as needed.
Generate your API key
In your dashboard, create an API key — you'll need it to authenticate when making requests to MiniMax-M3.
Make your first API call
Use your API key with our sample code to send a request to MiniMax-M3 via GPT Proto and see instant AI-powered results.
MiniMax M3 api FAQs & Technical Details
Find answers about the MiniMax M3 api including context window limits, bilingual performance, and GPTProto.com integration.
How does MiniMax M3 api compare to official access?
GPTProto.com provides an OpenAI-compatible interface for the MiniMax M3 api with unified billing and a 99.9% SLA. We manage automatic failover between regions to ensure your MiniMax M3 api calls remain stable even if primary clusters experience high load, all without requiring you to manage multiple vendor accounts.
Is data sent to the MiniMax M3 api used for training?
No. Privacy is a core priority for the MiniMax M3 api via our platform. All data processed through the MiniMax M3 api is handled under a strict Zero-Retention policy. Your proprietary business data, codebases, and multimodal inputs are never used to train or refine the underlying model.
What is the typical latency for the MiniMax M3 api?
For standard prompts, the MiniMax M3 api features a Time-To-First-Token (TTFT) of approximately 400ms. When processing ultra-long contexts exceeding 100k tokens, response times may scale to 2-5 seconds. The MoE architecture ensures the MiniMax M3 api remains faster than many dense models at similar intelligence tiers.
How do I migrate from GPT-4o to MiniMax M3 api?
Migration is seamless. Since GPTProto.com uses a unified schema, you simply update your base URL and change the model parameter to 'MiniMax-M3'. The MiniMax M3 api supports standard JSON mode and tool calling, allowing your existing OpenAI-based logic to work with minimal code changes.
Is fine-tuning available for the MiniMax M3 api?
Currently, fine-tuning is not supported for the MiniMax M3 api. However, with its massive 1M token context window, most users find that few-shot prompting and RAG (Retrieval-Augmented Generation) strategies are more effective and easier to maintain for the MiniMax M3 api than traditional fine-tuning.
Does the MiniMax M3 api support prompt caching?
Yes. The MiniMax M3 api features passive prompt caching for inputs of 512 tokens or more. This automatically identifies repeated content like system instructions or tool definitions. Hit tokens are billed at a significant discount, allowing the MiniMax M3 api to be highly cost-effective for multi-turn conversations.