Model Detail
Step-3.7-Flash
—Step-3.7-Flash is a code generation model with 100.7B parameters released by stepfun-ai. The model is registered under the image-text-to-text pipeline tag on Hugging Face, and supports text+image+video->text inputs, distributed under the permissive apache-2.0 license.
Step-3.7-Flash is priced at $0.2/M input tokens and $1.15/M output tokens. Operationally the model offers a 256K-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.
Step-3.7-Flash ships with 100.7B parameters. Total weight footprint is approximately 201.4 GB, which is the relevant figure when planning local-inference VRAM. The apache-2.0 license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
Step-3.7-Flash 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 (256K 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.
Scaling Limits of Constant-Stepsize SGD at Flat Minima
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One-step lowest-variance selection in a Gaussian random-field model motivated by masked diffusion: Total correlation and a square root collision threshold
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Tensor-Train Joint Modeling for Few-Step Discrete Diffusion
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CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters
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Adaptive Multi-Step Lookahead Decoding for Diffusion Language Models
arXiv:2607.15655v1 Announce Type: new Abstract: Masked diffusion language models (DLMs) enable parallel text generation by iteratively refining masked tokens, offering a promising alternative to autoregressive decoding. Recent lookahead-based decoding methods improve the accuracy--efficiency trade-o
Agent Step Value: Auditing Evaluator-Channel Reversals in Black-Box Agent Traces
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