MiniMax: MiniMax M3Active

minimax/minimax-m3

MiniMax M3 is the first open-weights flagship that brings coding, agentic reasoning, million-token context, and native multimodality together in one model. It handles autonomous task decomposition, tool use, and multi-step reasoning with ease — and writes code that's meant to ship, not code that just runs. Built on MiniMax's proprietary Sparse Attention architecture, M3 natively supports million-token context windows for long-horizon agents, large codebases, and long-video understanding. Its multimodality isn't bolted on — it's trained in from day one, with text and vision deeply aligned at the semantic level.

By:MiniMaxInput Type:Output Type:Publish time:2026-06-01

Providers

Route requests across multiple providers. Copy a provider slug to set your preference.

ProviderCache Hit RateContext

MiniMax

minimax

95.8%$0.3-0.6/ M tokens$1.2-2.4/ M tokensRead:0.06-0.12/ M tokensWrite:-/ M tokens1M1.53s134tps

Uptime

24hours

Direct request success rate on AI Gateway and per-provider.

Throughput

24hours

P50 throughput on live AI Gateway traffic, in tokens per second (TPS).

Latency

24hours

P50 time to first token (TTFT) on live AI Gateway traffic, in milliseconds.

Activity

Token volume and request traffic to this model over time.

Token Consumption

Related Models

More models from MiniMax

MiniMax: MiniMax H3

2.74Ktokens

MiniMax H3 is a lightweight, open-weights video generation model from MiniMax. It is designed for precise multimodal editing and controlled content generation, including instruction-guided edits, text and brand rendering, and video-to-video motion transfer. The model is suited for commercial creative workflows across advertising, e-commerce, gaming, and interface design, with native audiovisual output for reference-driven generation.

MiniMax: MiniMax M2.7 highspeed

1.46Btokens

M2.7 highspeed: Same performance, faster, more agile

MiniMax: MiniMax M2.7

14.53Btokens

M2.7 delivers outstanding performance in real-world software engineering, including end-to-end complete project delivery, log analysis and bug triaging, code security, machine learning, and more. On the benchmark SWE-Pro, M2.7 scores 56.22%, nearly matching the level of Opus. This capability also extends to end-to-end complete project delivery scenarios (VIBE-Pro 55.6%) and deep understanding of complex engineering systems on Terminal Bench 2 (57.0%). In the professional office domain, we have improved the model's specialized knowledge and task delivery capabilities across various fields. On GDPval-AA, its ELO score is 1495, the highest among open-source models. M2.7's ability to perform complex editing in the Office suite (Excel/PPT/Word) has significantly improved, enabling better multi-round revisions and high-fidelity editing. M2.7 is capable of interacting with complex environments. Across 40 complex skills (> 2000 tokens) cases, M2.7 still maintains a 97% skill adherence rate. In OpenClaw usage, M2.7 has shown significant improvement compared to M2.5, scoring close to the latest Sonnet 4.6 in the MMClaw evaluation. M2.7 possesses excellent identity retention capabilities and emotional intelligence. Beyond productivity use cases, it also opens up space for innovation in interactive entertainment scenarios.