MiMo V2.6 Pro
MiMo's large multimodal model for long-context, tool-using agent workloads.
- Published size
- 1.02T+
- Active parameters
- 42B
- Context window
- 1M
- Architecture
- MoE · multimodal
- License
- MIT
Deployment considerations
Treat this as a large-scale deployment project. Validate parallelism, interconnect, encoder support, and memory before choosing instances.
The model summary lists a 1.02T backbone and 42B active parameters, with additional vision, audio, and speculative-decoding modules.
GPU memory estimates and minimum / recommended configurations are pending. This profile does not contain measured deployment results.
GPU requirements
- Minimum
- —GPU / VRAM · pending
- Recommended
- —GPU / VRAM · pending
Memory estimates and tested configurations will appear here after checkpoint review and deployment testing.
No invented leaderboards.
Latency, throughput, and cost per token will appear here after a reproducible run. Until then, this page helps you understand the model—not predict its performance.
MiMo V2.6 Pro deployment FAQ
How much GPU memory does MiMo V2.6 Pro need?
The full checkpoint weight footprint is pending review. Minimum and recommended GPU configurations will be added after testing; the model name or active parameter count alone is not a memory requirement.
Has BenchGrid benchmarked MiMo V2.6 Pro?
Not yet. This profile contains publisher specifications and calculated weight-memory estimates. We do not currently publish measured latency, throughput, or cost per token for this model.
Where do these specifications come from?
The specifications are based on the official Xiaomi model card linked on this page. Memory estimates use the stated total parameter count, including inactive experts for MoE models. Nominal model sizes are labeled with ~.
Explore your compute options.
Check available hardware, quotas, and current pricing with the provider. These links are not verified deployments or performance recommendations.
