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5 of 40 open-source models (filtered).

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Qwen's reasoning-focused 'thinking' model. Generates long chains-of-thought before answering, similar to OpenAI's o1 and DeepSeek R1 lineage. Optimised for math and competition-style problem solving. The Preview tag means Qwen is iterating quickly; later versions may obsolete this one. Useful today for math-heavy workloads where a slow, careful answer is preferred to a fast wrong one.

Context: 33K
License: apache-2-0
VRAM Q4: 19.2 GB

Qwen2.5 32B Instruct

32B

32B sweet-spot model: strong reasoning, fits on one H100 in fp16, on a 4090 at Q4. The 32B size in particular hits a quality/cost knee — quality scales with parameters faster than cost up to ~32B, and slower afterwards. Favoured for production chat where 7B isn't sharp enough and where 70B+ would over-spec the hardware budget. Apache 2.0 licence.

Context: 128K
License: apache-2-0
VRAM Q4: 19.2 GB

Qwen2.5 Coder 32B

32B

Coding-specialised Qwen2.5 32B fine-tune. GPT-4o-class on HumanEval and BigCodeBench at the time of release. Trained on additional code-heavy data with extended pre-training. Apache 2.0. Natural pick for self-hosted coding assistants, code-review automation, and any agent loop that primarily writes code.

Context: 128K
License: apache-2-0
VRAM Q4: 19.2 GB

Qwen2.5 14B Instruct

14B

Mid-size Qwen2.5 with broad task coverage. The sweet spot for users who want noticeably better quality than 7B but can't justify the hardware footprint of 32B or 72B.

Context: 128K
License: apache-2-0
VRAM Q4: 8.4 GB

Qwen2.5 7B Instruct

Apache-2.0-licensed 7B model with surprisingly strong reasoning and multilingual chops. Qwen 2.5 trains on a larger and more carefully filtered corpus than the original Qwen series, and the 7B variant punches well above its weight on coding and math benchmarks. A strong default for cost-sensitive chat workloads and for fine-tuning experiments where the Apache licence simplifies downstream redistribution.

Context: 128K
License: apache-2-0
VRAM Q4: 4.2 GB