I benchmarked the 4 models I had pulled. The 1.1GB one beat the 2GB one at math and lost badly at extraction.

152 generations, deterministic grading (exact number/string/JSON/regex), no LLM judge on my 16GB laptop. task type | deepseek-r1:1.5b (1.1GB) | llama3.2:3b (2.0GB) | gemma:2b | codellama 7b arithmetic (12) | 10/12 | 2/12 | 2/12 | 3/12 extraction (9) | 4/9 | 9/9 | 7/9 | 8/9 classification (8) | 6/8 | 7/8 | 7/8 | 2/8 code output (6) | 5/6 | 3/6 | 1/6 | 4/6 median latency | 14.6s | 1.6s | 2.7s | 6.3s The 1.1GB reasoning model beat the 2GB general model 5x at arithmetic, then lost badly at extraction, at 9x the latency. Smallest model scored highest overall; size did not predict accuracy. Caveats: 3-12 prompts per category so one item swings the percentage a lot, only auto-gradable short-answer tasks, one machine, Q4 quants, uncontrolled OS cache. MIT, fully local, never pulls or deletes models: https://github.com/RitikPatill/routefoundry submitted by /u/Emergency_Subject702 [link] [comments]

Source: r/ollama | 2026-08-04