CamThink AI benchmarks small models on agent tasks
AI summary
CamThink AI evaluated six language models with 4 billion parameters or fewer on its own AI agent task set, according to a Reddit post from /u/KienShen. The models were Qwen3.5-4B, Spark-X2.6, LFM3-1.7B, LFM2.5-2.6B, Gemma 4 E2B and MiniCPM5-2B, but the submitted text provides no individual scores, methodology or full results. CamThink AI argues that the intelligence density of small models is improving rapidly, though that trend is a vendor claim.
In-depth insight
The benchmark targets the relatively under-covered evaluation of ultra-small models on agent tasks, offering an initial signal for developers seeking compact models.
| The community rarely sees the evaluation of ultra-small models. We have evaluated our own AI Agent tasks for Qwen3.5-4B, Spark-X2.6, LFM3-1.7B, LFM2.5-2.6B, Gemma 4 E2B, MiniCPM5-2B. These models have their own characteristics and advantages and disadvantages. The obvious trend is that the intelligent density of small models is developing rapidly. I look forward to seeing more small models. blog：https://www.camthink.ai/blog/small-language-model-benchmark/ project：https://github.com/camthink-ai/NeoMind submitted by /u/KienShen [link] [comments] |
#CamThink AI#Qwen3.5-4B#Spark-X2.6#LFM3-1.7B#LFM2.5-2.6B#Gemma 4 E2B#MiniCPM5-2B#NeoMind