MiniMax: MiniMax M3 (batch)
MiniMax-M3 is a multimodal foundation model from MiniMax. It supports text, image, and video inputs with text output, a 1M-token context window, and is suited for long-horizon agentic work, coding,...
Anyone in the Project can @-mention MiniMax: MiniMax M3 (batch) with the team's shared context - pooled credits, one chat, one memory.
Starter is free forever - 1 Project, 100 credits/month, 1 MCP. No card.
Verdict
Best for
- Batch processing of long documents with images
- Cost-sensitive video content analysis
- Large-scale multimodal data pipelines
- Document extraction across mixed media types
Strengths
The 524K context window handles entire books, multi-hour video transcripts, or hundreds of pages with embedded images in one pass. Batch pricing undercuts most competitors by 3-4x, making it viable for high-volume jobs where per-token cost dominates your budget. Native video understanding eliminates the need to extract frames manually. The model accepts all three modalities in a single API call, simplifying workflows that mix PDFs, screenshots, and video clips.
Trade-offs
No public benchmarks means you're flying blind on accuracy relative to GPT-4o, Claude, or Gemini — expect to run your own evals before committing production traffic. Batch processing adds latency (typically minutes to hours), ruling out real-time use cases. MiniMax has less ecosystem maturity than OpenAI or Anthropic, so expect fewer integrations and community resources. The model is new enough that edge-case behavior and safety guardrails remain unproven at scale.
Specifications
- Provider
- minimax
- Category
- llm
- Context length
- 524,288 tokens
- Max output
- 471,859 tokens
- Modalities
- text, image, video
- License
- proprietary
- Released
- 2026-05-31
Pricing
- Input
- $0.30/Mtok
- Output
- $1.20/Mtok
- Model ID
minimax/minimax-m3:batch
Per-token prices show what the model costs upstream. On Switchy your team draws from one shared org credit pool - one plan, one balance for everyone.
Team cost calculator
5 seats · 80 msgs/day
Switchy meters this against your org's shared credit pool - one plan, one balance for everyone.
Providers
Performance
Benchmarks
Works well with
Top MCPs
Compatibility data comes from first-party telemetry; once we have enough co-usage signal, top MCPs for this model will appear here.
How Switchy teams use it
Starter prompts
Extract Tables from PDF
Extract all tables from the attached PDF as CSV. Preserve column headers exactly as shown. If a table spans multiple pages, merge it into one output. Include the page number where each table starts.Open in a Project →
Summarize Video Content
Watch this video and create a summary with timestamps. For each major topic or scene change, note the time and write a 1-2 sentence description. Focus on key points and decisions.Open in a Project →
Compare Document Versions
Compare these two document versions and list all substantive changes. Ignore formatting differences. For each change, quote the old text, the new text, and the page number. Flag any changes to images or diagrams.Open in a Project →
Analyze Mixed-Media Reports
Read this report and extract: (1) executive summary, (2) all numerical data from charts and tables, (3) descriptions of any photos or diagrams. Output as JSON with those three keys.Open in a Project →
Batch Classify Support Tickets
Classify each support ticket into one of these categories: billing, technical, feature request, bug report. If a screenshot is attached, describe what it shows in 10 words or less. Output as a CSV with columns: ticket_id, category, screenshot_summary.Open in a Project →
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