This model was retired on 30 October 2026 and no longer accepts API requests. Recommended replacement: DeepSeek V4 Flash 0731.
Qwen3-14B-FP8
Retired
A highly efficient 14.8B parameter dense language model optimized for high-volume text tasks and dual-mode reasoning.
Total Parameters
14.8B
Context Window
131K
Tokens
Modalities
Text Only
Max Output
16,384
Tokens
Efficient Text Generation with Dual-Mode Reasoning
Meet Qwen3-14B, a dense 14.8B parameter causal language model from the Qwen3 release. Designed for both complex reasoning and efficient dialogue, it supports seamless switching between a "thinking" mode for rigorous logic, math, and programming tasks, and a "non-thinking" mode for general-purpose conversation. Trained on 36 trillion multilingual tokens across 100+ languages, it serves as an excellent foundation for high-volume workloads like classification, extraction, and summarization where maximum frontier performance is not strictly required.
Dense 14.8B — Dual-Mode Reasoning
Built for efficient text intelligence
High-Volume Processing
Perfectly suited for tasks that do not require massive frontier intelligence, such as document classification, data extraction, and large-scale summarization.
Dual-Mode Execution
Toggle between specialized "thinking" mode for robust logical inference and creative coding, or "non-thinking" mode for rapid, general-purpose conversation.
Business Customization
A strong baseline for medium-scale enterprise custom model training, allowing for efficient adaptation to domain-specific professional services.
Academic & Research
Provides an optimal, budget-friendly foundation for natural language processing methodologies, educational AI, and fine-tuning experiments.
Capable & Cost-Effective
Proven baseline performance across reasoning, coding, and agentic workflows for the 14B weight class.
Intelligence Index
Better than 32% of models
GPQA Diamond
Better than 50% of models
τ²-Bench Telecom
Better than 42% of models
| Category | Benchmark | Score | Description |
|---|---|---|---|
| Reasoning | GPQA Diamond | 60% | Graduate-level scientific reasoning |
| Reasoning | τ²-Bench Telecom | 35% | AI agents in dual-control scenarios |
| Reasoning | IFBench | 36% | Instruction-following accuracy |
| Coding | SciCode | 29% | Python for scientific computing |
| Knowledge | AA-Omniscience Accuracy | 15% | Proportion of correctly answered questions |
Metrics sourced from Artificial Analysis. Hallucination Rate: 24.5%