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Step 3.7 Flash

StepFun

Step 3.7 Flash is an open-weight vision-language model for high-throughput agent execution. Its 198B total parameters combine a 196B sparse language backbone with a 1.8B vision encoder, activating about 11B parameters per token. It accepts images and text, supports a 256K context, and offers low, medium, and high reasoning levels. Training targets visual search, browser and terminal tool use, coding, and professional document workflows. API, web chat, and downloadable weights are available. Advisor-mode results depend on an additional larger model and should not be interpreted as standalone checkpoint performance.

Trinity Large Thinking

Arcee AI

Trinity Large Thinking is Arcee AI’s reasoning-focused post-training of Trinity Large Base. The sparse mixture-of-experts model has 398B total parameters and approximately 13B active per token, with documented context extension to 512K. It generates explicit thinking traces and is trained for long-running agents, multi-turn tool calls, and coherent instruction following. Thinking tokens must remain in conversation history for supported agent behavior. API access and downloadable weights are available. The current model card specifies OpenMDW-1.1, despite Apache 2.0 wording in the original announcement; this record follows the current weight license.

Step 3.5 Flash

StepFun

Step 3.5 Flash is StepFun’s sparse language model optimized for reasoning, coding, and tool-driven agents. A 3:1 sliding-window/full-attention pattern reduces long-context inference cost while multi-token prediction increases throughput. The model activates 11B of 196B parameters and supports 256K context. Public access predates the February 12 technical-blog update; repositories and contemporaneous API catalogs establish the early-February release. The full weight footprint, context length, and selected reasoning mode affect deployment cost and latency.