Unverified50% confidenceBenchmarkExact time
On the GSM8K mathematical reasoning test, Qwen3-4B scores 0.915 and Gemma 4 E2B scores 0.911, a difference of +0.4 percentage points with a confidence interval crossing zero, making it the only model on par with Gemma.
1
Sources
50%
Confidence
Long-term
Relevance
10/13/2026
First Seen
Sources
单块TPU v5e实测：哪些小模型能与Gemma 4 E2B一战？
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UnverifiedQwen2.5-1.5B scores 0.729 on GSM8K, 18.2 percentage points lower than Gemma 4 E2B; Qwen3-0.6B scores 0.599, 31.2 percentage points lower; SmolLM2-1.7B scores 0.461, 45.0 percentage points lower.79% similarUnverifiedOn the BFCL tool-calling test, SmolLM3-3B scores 0.945, slightly above Gemma 4 E2B's 0.927, but the difference is not significant; Qwen3-4B (0.940), Qwen3-1.7B (0.935), Qwen2.5-1.5B (0.910), and Granite 3.3 2B (0.920) are all at the same level as Gemma.78% similarUnverifiedOn the math reasoning task GSM8K, OQ4E answered 954 questions correctly (95.4%), while 8-bit only answered 924 correctly (92.4%), with the 4-bit version coming out 3 percentage points higher.72% similarUnverifiedThe int4 build from Google's QAT checkpoint on Gemma 4 E2B is less than 0.5 points lower in accuracy than bf16 (0.678 vs 0.683), while the int8 build rounded directly from bf16 dropped 1.3 points.72% similarUnverifiedIn testing, all three models—qwen2.5:3b, qwen2.5:1.5b, and llama3.2:1b—kept model memory below 0.5 GB, and all produced 0 hallucinated place names.71% similar
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