ChatDeepSeek
DeepSeek V4
The DeepSeek V4 API — Flash + Pro, 1M context, thinking by default, frontier reasoning at a low per-token price.
From $0.140 per 1M tokens
MiniMax M3 — Frontier Coding, 1M Context
MiniMax M3 is an open-weight model that pairs frontier coding and agentic benchmarks with a 1M-token context window and native multimodal input. MiniMax M3 reasons before it answers, calls tools across long-horizon runs, and reads images and video in the same call — exposed on api.reapi.ai as a drop-in OpenAI-compatible endpoint. Pay-as-you-go in USD at a fraction of closed-source frontier rates.
MiniMax M3model
minimax/minimax-m3MiniMax M3 playground
Open the chat playground to run MiniMax M3 through the OpenAI-compatible chat completions surface with your api.reapi.ai key.
What you can build with this model
Real-world workflows and production use cases you can build and ship with this model.
Long-horizon agentic coding and software engineering
Agentic coding is the headline of the MiniMax M3. MiniMax reports frontier-level results on software-engineering benchmarks — 59.0% on SWE-Bench Pro and 66.0% on Terminal-Bench 2.1 — putting MiniMax M3 in range of the top closed-source coding models while staying open-weight. Point a coding agent at the MiniMax M3 and it scopes the task, calls tools, reasons through multi-step work, and self-corrects across a long run, all in one session.
Read the API docs
Million-token codebase and document analysis
The MiniMax M3 defaults to a 1M-token context window — enough to load a whole mid-size repository, a long research pack, or a multi-turn agent trace in a single call. MiniMax Sparse Attention keeps long-context inference efficient, so MiniMax M3 workloads like architecture review, dependency audits, and migration planning rarely need chunking. Stable prompt prefixes hit the low cache-read rate on every repeat.
Native multimodal understanding and tool use
The MiniMax M3 is multimodal from the ground up: send images and video alongside text in the same Chat Completions call — screenshots, diagrams, document scans, and clips — and the model reasons over all of it. Combined with reliable function calling and JSON output, the MiniMax M3 drives browser agents, document pipelines, and tool-using workflows that mix vision, retrieval, and code.
Pricing
Credit-based — 1 credit = $0.001 USD. Pay only for completed generations.
| Category | Unit | Price |
|---|---|---|
| Token pricing | ||
| Input | 1M tokens | $0.6 |
| Output | 1M tokens | $2.4 |
| Cache read | 1M tokens | $0.12 |
Why reAPI
OpenAI-compatible drop-in
The MiniMax M3 speaks OpenAI Chat Completions verbatim. Moving an existing OpenAI integration to MiniMax M3 is a base URL, an API key, and a model-string change — `minimax/minimax-m3` — not a platform rewrite. The same `messages` array, the same streaming format, the same tool-calling shape.
Frontier coding at value pricing
MiniMax M3 is open-weight and priced to match. It posts frontier coding and agentic benchmarks while costing a fraction of closed-source models per token — and prompt caching drops the price again on repeated context. Run premium agentic work without premium per-token bills.
One key across MiniMax, GPT, Claude, and Gemini
A single api.reapi.ai key unlocks the MiniMax M3 alongside GPT-5.5, Claude Opus 4.8, DeepSeek V4, Gemini, and every other frontier chat model on the platform. Compare vendors, add fallbacks, and route traffic per call with a configuration change instead of an integration project.
MiniMax M3 vs DeepSeek V4
MiniMax M3 and DeepSeek V4 are both open-weight, value-priced models with a 1M-token context window, thinking, and tool use. Here is how MiniMax M3 is positioned against DeepSeek V4 on the dimensions that matter for agentic and coding work.
Capability
MiniMax M3 on reAPI
DeepSeek V4
Positioning
Single open-weight model tuned for frontier coding, long-horizon agents, and native multimodality.
Two open-weight variants — Flash (fast / low cost) and Pro (flagship reasoning).
Context window
1M tokens by default, with a guaranteed 512K minimum and MiniMax Sparse Attention for efficient long context.
1M-token context window with DeepSeek Sparse Attention.
Max output
Up to 512K output tokens per response (128K recommended).
Up to 384K output tokens per response.
Thinking
Native thinking with interleaved reasoning during tool use; adaptive by default, can be disabled.
Thinking mode on by default, with a dual thinking / non-thinking switch.
Multimodal input
Native image and video understanding in the same Chat Completions call.
Image input supported (beta); text-and-image.
Agentic and coding focus
Vendor-reported frontier results on SWE-Bench Pro, Terminal-Bench, and agent benchmarks; tuned for long-horizon coding agents.
Dedicated agentic optimizations; open-source SOTA on agentic coding per DeepSeek.
Comparison reflects publicly documented behavior from MiniMax's M3 release notes and DeepSeek's V4 documentation at the time of writing. Benchmark figures are vendor-reported. Model behavior and pricing can change; check the pricing card above and the API docs for current values.
Ship the MiniMax M3 in three steps
- step 01
Create an account and key on api.reapi.ai
Sign up at api.reapi.ai, open the console, generate an API key under API Keys, and top up tokens under Top Up. The chat workspace is separate from the reapi.ai image/video gateway — keys do not cross over.
Open - step 02
Send your first request
POST https://api.reapi.ai/v1/chat/completions with `model` set to `minimax/minimax-m3`, your `messages` array, and `max_tokens`. The MiniMax M3 endpoint is OpenAI-compatible, including streamed responses, so most SDKs work with only a base URL change.
Open - step 03
Tune for reasoning and cost
MiniMax M3 thinks adaptively — it reasons when a task is hard and answers directly when it is not. Reuse stable system prompts and tool schemas across calls to hit the low cache-read rate, and set `max_tokens` high enough to fit the chain-of-thought on reasoning-heavy work.
Open
Frequently asked questions
Common questions about this model.
The MiniMax M3 is billed pay-as-you-go in USD against your api.reapi.ai token balance. The pricing card on this page shows the live per-1M-token input, output, and cache-read rates. Cache reads are dramatically cheaper than re-sending the same tokens, and failed requests are not charged.
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start building
Ready to ship?
Try it in the playground or grab an API key to integrate now.