Inference platform for hosted open-weight models, surfaced in Idapt as a serving provider when an endpoint routes through Parasail.
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DeepSeek's cost-efficient V4.1 tier and the first model built on the Causal Encoder-Decoder (CED) architecture: a 552B MoE activating 8B parameters on input and 16B on output.
Z.ai's efficient multimodal model for fast coding and long-horizon agent tasks.
Z.ai's large-scale reasoning flagship for complex software engineering and long-horizon agent tasks.
Open-weight dense 27B vision-language model from Qwen for long-running agent tasks.
The GA release of DeepSeek's open 1.6T-parameter MoE flagship for advanced reasoning and long-horizon agents.
The GA release of DeepSeek's efficiency-optimized V4 Flash, re-post-trained for coding, reasoning, and agent workflows.
MoonshotAI's 2.8T-parameter flagship for complex coding, knowledge work, and long-horizon agent runs.
Z.ai's frontier reasoning model for long-context agents and software engineering.
MiniMax's multimodal foundation model for long-horizon agentic work.
Open-source 1.6T-parameter MoE with 49B active, built for advanced reasoning and long-horizon agents.
Efficiency-optimized DeepSeek V4 at 284B total / 13B active for fast, high-throughput inference.
MoonshotAI's next-generation multimodal model built for long-horizon coding and multi-agent orchestration.
Efficient Mixture-of-Experts Gemma 4 with only 4B active parameters per token.
Google's fourth-generation open dense model with native multimodal understanding.
Compact 9B vision-language model from the Qwen3.5 family.
Efficient vision-language MoE with linear attention and 3B active parameters.
Qwen's largest MoE model with 397B total parameters, 17B active.
Efficient coding agent with 80B parameters, only 3B active per token.
Qwen's largest vision-language model with 235B MoE architecture.
OpenAI's budget-friendly reasoning model — fast and surprisingly capable.
OpenAI's compact open-weights model for efficient inference.
July 2025 update to Qwen3 235B A22B with improved capabilities.
Latest Mistral Small — efficient 24B model for fast, cost-effective tasks.
Meta's high-capacity multimodal MoE model with 128 experts.
Google's open multimodal model with 128K context and 140+ language support.
Large 72B vision-language model from Qwen 2.5 generation.
Meta's efficient 70B model matching Llama 3.1 405B on key benchmarks.
Tiny 3B edge model from Meta optimized for on-device deployment.
12B model from Mistral and NVIDIA — efficient and multilingual.
