Model Detail
Qwen: Qwen-Turbo
—Qwen: Qwen-Turbo is a large language model released by Qwen. And supports text->text inputs.
Qwen: Qwen-Turbo is priced at $0.05/M input tokens and $0.2/M output tokens. Operationally the model offers a 129K-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.
The published knowledge cutoff is 2025-03-31, so newer events will not be reflected in zero-shot answers without retrieval.
Qwen: Qwen-Turbo is best fit for general-purpose chat and instruction-following workloads, and high-volume batch jobs where per-call cost dominates the budget. 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.
Diarization-Guided Qwen-ASR Adaptation for Multilingual Two-Speaker Conversational Speech
arXiv:2607.08208v2 Announce Type: replace Abstract: This paper describes our self-designed system for Task 1 of the MLC-SLM 2026 Challenge for multilingual two-speaker conversational speech. The system combines a modular speaker diarization front end with a challenge-adapted Qwen3-ASR-1.7B recognize
QwenPaw-Data: Bridging Facts, Methodology, and Execution for Autonomous Enterprise Data Analytics
arXiv:2607.11019v2 Announce Type: replace Abstract: Enterprise data analysis is emerging as a distinct frontier for autonomous agents. Compared with general-purpose interaction and software engineering, it operates in an open, ambiguous, and continuously evolving environment. These characteristics c
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
Fine-Tuning General-Purpose Large Language Models for Agricultural Applications:A Reproducible Framework and Evaluation Protocol Based on Qwen3-8B
arXiv:2606.28992v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) have demonstrated strong abilities in opendomain question answering, information extraction, and text generation. Agricultural applications, however, are domain-specific, region-dependent, time-sensitive,
