Model Detail
MiniMax-M3
▲ 7.5%MiniMax-M3 is a code generation model with 213.5B parameters released by MiniMaxAI. The model is registered under the image-text-to-text pipeline tag on Hugging Face, and supports text+image+video->text inputs, distributed under a other license.
MiniMax-M3 reports a Chatbot Arena ELO of 1,448 across 11,264 votes. Other benchmark slots are still empty in our dataset, so this single figure is best read as a partial picture rather than a full evaluation.
MiniMax-M3 is priced at $0.3/M input tokens and $1.2/M output tokens. Operationally the model offers a 1049K-token context window, which matters when sizing it for prompt-heavy or latency-sensitive workloads. At this input rate the model sits in the commodity tier and is suitable for high-volume workloads where per-call cost dominates the decision.
MiniMax-M3 ships with 213.5B parameters. Total weight footprint is approximately 427.0 GB, which is the relevant figure when planning local-inference VRAM. Distribution is governed by the other license — review the exact terms before commercial deployment.
Downloads of MiniMax-M3 have moved +7.5% over the past 24 hours, +113.0% over the trailing seven days. That puts the model in active uptrend territory; a sustained move of this size usually reflects a recent release, a viral integration, or a benchmark surprise rather than steady-state demand. These numbers are signal, not guarantee — week-over-week download counts on Hugging Face also reflect mirror traffic, CI scrapes, and one-off benchmarking runs.
MiniMax-M3 is best fit for code completion, repository-scale Q&A, and pair-programming integrations, high-volume batch jobs where per-call cost dominates the budget, and long-context tasks such as full-codebase analysis or book-length summarization (1049K tokens). It is a less obvious choice for one-shot generation of security-critical code without review. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
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