Qwen3-8B-Base

The /think mode generates chain-of-thought reasoning wrapped in <think> tags before final outputs, typically adding 300-500ms latency for complex queries but improving accuracy by 15-20% on reasoning tasks like MATH where it scores 60.8%. Use /no_think for direct responses in production APIs where sub-100ms latency matters more than reasoning transparency. The model seamlessly switches modes mid-conversation, making it ideal for applications that need both quick factual retrieval and complex problem-solving.

At $0.18 input/$0.20 output per million tokens, Qwen3-8B costs approximately $190 for processing 1M tokens (assuming 50/50 input/output split), compared to GPT-4o's $5,000 input/$15,000 output pricing - making it 52-78x cheaper. For a startup processing 100M tokens monthly, this translates to $19,000 with Qwen3-8B versus $1M+ with GPT-4o. The 89.84% GSM8K score proves competitive reasoning capability at this price point.

GQA reduces the number of query heads from 32 to 8 while maintaining 32 key-value heads, cutting KV cache memory by 75% and enabling the full 32K context window on consumer GPUs with 24GB VRAM. This architectural choice, combined with QK-Norm for training stability and YaRN position embeddings, allows linear scaling to 128K tokens with minimal performance degradation. The model maintains 81.07% on BBH despite the optimization, only 3.41 points behind the 4x larger Qwen2.5-32B.

Qwen3-8B achieves 67.65% on HumanEval+ and 72.23% on EvalPlus, outperforming Qwen2.5-32B by 6.95 and 5.98 percentage points respectively despite being 75% smaller. The model supports 61.69% accuracy across MultiPL-E's language suite including Python, JavaScript, Java, C++, and Rust. Unlike pure coding models, it maintains strong general reasoning (76.89% MMLU) making it suitable for full-stack development tasks that require both code generation and system design discussions.