Size

13B – 30B

6 open-source models in this size bucket.

Flagship Gemma 2 release. Uses logit-distillation from a larger teacher model, which is how Google delivers near-70B quality from a 27B student. A solid choice when the Llama community licence doesn't fit and you need quality at the 27B–40B size range.

Context: 8K
License: gemma
VRAM Q4: 16.2 GB

Mistral Small 3

24B

24B dense model from Mistral's January 2025 release that competes with Llama 3.3 70B on many tasks at a third of the parameter count. Apache 2.0 licensed and small enough to run on a single 4090 at Q4. Good pick when you want Llama-3.3-70B-class chat quality but at a friendlier hardware budget, or when the licence matters and Llama's community terms don't fit.

Context: 33K
License: apache-2-0
VRAM Q4: 14.4 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

Phi-4 14B

14B

14B model trained primarily on synthetic data. Punches above its weight on reasoning, especially MATH and GPQA. MIT licensed. A standout choice when you want strong reasoning quality without paying 70B-tier hardware costs. Phi-4 in particular demonstrated that careful synthetic-data curation can extract frontier-class reasoning from a relatively small dense model.

Context: 16K
License: mit
VRAM Q4: 8.4 GB

Phi-3 Medium 14B

14B

Phi-3's mid-tier model with extended 128K context. MIT licence. Strong reasoning relative to its parameter count thanks to Microsoft's heavy investment in synthetic training data.

Context: 128K
License: mit
VRAM Q4: 8.4 GB

OLMo 2 13B

13B

Larger OLMo 2 release. Same fully-open philosophy as the 7B variant. The 13B size makes it more competitive with mainstream production-grade chat models.

Context: 4K
License: apache-2-0
VRAM Q4: 7.8 GB