Gemini 3.1 Flash Lite Preview vs Gemma 4 26B A4B

Full head-to-head by test (our 12-test suite):

• Tool calling: Gemma 4 26B A4B wins with 5 vs Gemini 3.1's 4. In our rankings Gemma is tied for 1st on tool_calling (tied with 16 others out of 54), while Gemini 3.1 is rank 18 of 54 (29 models share that score). This matters for function selection, argument accuracy, and sequencing in agentic workflows.
• Classification: Gemma 4 26B A4B scores 4 vs 3 for Gemini 3.1; Gemma is tied for 1st (tied with 29 others of 53), Gemini 3.1 sits at rank 31 of 53. Expect Gemma to route and label inputs more reliably in our tests.
• Long context: Gemma 4 26B A4B wins 5 vs 4; Gemma is tied for 1st on long_context (tied with 36 others of 55) while Gemini 3.1 ranks 38 of 55. For retrieval at 30K+ tokens, Gemma showed stronger retrieval fidelity in our runs.
• Constrained rewriting: Gemini 3.1 wins 4 vs 3. Gemini 3.1 ranks 6 of 53 (25 models share this score); Gemma ranks 31 of 53. If you must compress text into hard character limits, Gemini 3.1 performed better in our tests.
• Safety calibration: Gemini 3.1 strongly wins 5 vs Gemma's 1. Gemini 3.1 is tied for 1st on safety_calibration (tied with 4 other models of 55), while Gemma is rank 32 of 55. For refusing harmful requests and permitting legitimate ones, Gemini 3.1 is the clear choice in our evaluation.
• Ties (no clear winner): structured_output (both 5; both tied for 1st), strategic_analysis (both 5; both tied for 1st), creative_problem_solving (both 4; both rank 9-ish), faithfulness (both 5; tied for 1st), persona_consistency (both 5; tied for 1st), agentic_planning (both 4; same rank), multilingual (both 5; tied for 1st). These ties indicate equivalent behavior on JSON/schema adherence, nuanced tradeoff reasoning, non-obvious idea generation, source-faithfulness, persona maintenance, goal decomposition, and non-English output in our tests. In short: Gemma 4 26B A4B wins on tool-calling, classification, and long-context and ranks top among peers for those tasks; Gemini 3.1 Flash Lite Preview wins safety_calibration and constrained_rewriting. Many core skills (structured output, faithfulness, strategic analysis, multilingual, agentic planning, persona, creative problem-solving) are tied in our testing.

Costs per 1,000 tokens: Gemini 3.1 Flash Lite Preview = $0.25 input / $1.50 output; Gemma 4 26B A4B = $0.08 input / $0.35 output. Assuming 1M input + 1M output tokens/month (1,000 mtokens each): Gemini 3.1 = $0.251,000 + $1.501,000 = $1,750/month; Gemma 4 26B A4B = $0.081,000 + $0.351,000 = $430/month. At 10M in+out: Gemini 3.1 = $17,500 vs Gemma = $4,300. At 100M in+out: Gemini 3.1 = $175,000 vs Gemma = $43,000. The roughly 4.29x priceRatio in the payload means high-volume consumer apps, chat platforms, and large-scale API customers should favor Gemma 4 26B A4B for cost efficiency; teams that need the stronger safety calibration and constrained-rewriting behavior must budget for the higher Gemini 3.1 spend.

Choose Gemma 4 26B A4B if you need low cost at scale, top-ranked tool-calling, stronger long-context retrieval, or better classification in our benchmarks—especially for developer APIs and high-volume production. Choose Gemini 3.1 Flash Lite Preview if your priority is safety calibration or squeezing content into tight character limits (constrained rewriting), and you can absorb a ~4.3x higher per-token cost.