Reference
Choosing a Model in 2026
The "best" model changes constantly as new weights are released — treat any specific recommendation as a snapshot, not a permanent answer.
As of mid-2026, notable open-weight families worth knowing by name:
| Family | Notes |
|---|---|
| Qwen3 | Broad size range, strong multilingual support (100+ languages), Apache 2.0 license — a common default "start here" choice |
| Gemma 3 / 4 | Google's open family; strong for laptops/edge devices, vision-capable variants |
| Llama 3.3 / 4 | Meta's family; Llama 4 Scout notable for very long context, needs serious hardware |
| DeepSeek V4 | Strong for coding/reasoning at the high end, larger hardware requirements |
| GLM-5.1 | Consistently strong on coding benchmarks |
| Phi-4-mini | Small, designed for weak/constrained hardware |
For coding specifically, Qwen3-Coder and GLM-5.1 variants are frequently cited as top open-weight performers on benchmarks like SWE-bench.
Practical approach
Pick a family, start with the largest size that fits your VRAM budget at Q4_K_M (see the memory formula in Sizing Your Hardware), and only move to a smaller/more aggressively quantized version if it doesn't fit or runs too slowly.