Model Detail
Qwen3.5-35B-A3B-GGUF
—Qwen3.5-35B-A3B-GGUF is an AI model with 35B parameters released by AesSedai. And supports text+image+video->text inputs.
Qwen3.5-35B-A3B-GGUF is priced at $0.1625/M input tokens and $1.3/M output tokens. Operationally the model offers a 262K-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.
Qwen3.5-35B-A3B-GGUF ships with 35B parameters, distributed as a quantized weight variant for lower-VRAM inference.
Qwen3.5-35B-A3B-GGUF is best fit for general-purpose AI workloads, high-volume batch jobs where per-call cost dominates the budget, and long-context tasks such as full-codebase analysis or book-length summarization (262K tokens). 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.
TUDUM: A Turkish-Thinking Reasoning Pipeline for Qwen3.5-27B
arXiv:2607.01927v1 Announce Type: cross Abstract: This paper presents TUDUM (T\"urk\c{c}e D\"u\c{s}\"unen \"Uretken Model), a project pipeline for adapting a Qwen-family 27B thinking model toward Turkish reasoning. The central problem is not only to answer Turkish prompts in Turkish, but to make the
Procedural-skill SFT across capacity tiers: A W-Shaped pre-SFT Trajectory and Regime-Asymmetric Mechanism on 0.8B-4B Qwen3.5 Models
arXiv:2605.11907v2 Announce Type: replace Abstract: We measure procedural-skill SFT contribution across three Qwen3.5 dense scales (0.8B, 2B, 4B) on a 200-task / 40-skill holdout, with Claude Haiku 4.5 as a frontier reference. The corpus is 353 rows of (task + procedural-skill block, Opus chain-of-t
Qwen3.5-Omni Technical Report
arXiv:2604.15804v2 Announce Type: replace Abstract: In this work, we present Qwen3.5-Omni, the latest advancement in the Qwen-Omni model family. Representing a significant evolution over its predecessor, Qwen3.5-Omni scales to hundreds of billions of parameters and supports a 256k context length. By
