Gemini 3.1 Flash Lite Preview vs Llama 4 Maverick

Overview: Across our 12-test suite Gemini wins 9 tests, Llama wins 0, and 3 tests tie. Scores (Gemini vs Llama) and practical meaning:

• safety_calibration: 5 vs 2 — Gemini tied for 1st of 55 models on safety_calibration, so it will refuse harmful requests and permit legitimate ones more reliably in our tests.
• faithfulness: 5 vs 4 — Gemini tied for 1st of 55 on faithfulness, meaning fewer source hallucinations in source-grounded tasks.
• structured_output: 5 vs 4 — Gemini tied for 1st of 54; better JSON/schema compliance for API-driven pipelines.
• strategic_analysis: 5 vs 2 — Gemini tied for 1st of 54; stronger at nuanced tradeoff reasoning (useful for finance/decision support).
• constrained_rewriting: 4 vs 3 — Gemini ranks 6th of 53 (25 models share the score); better at fitting hard character limits.
• creative_problem_solving: 4 vs 3 — Gemini ranks 9th of 54; produces more feasible, specific ideas in our tests.
• tool_calling: 4 vs (rate-limited/transient) — Gemini scored 4 (rank 18 of 54); Llama’s tool_calling hit a 429 on OpenRouter during testing (quirk flagged), so results may be transient but Gemini was the reliable performer.
• agentic_planning: 4 vs 3 — Gemini rank 16 of 54; better at goal decomposition and recovery.
• multilingual: 5 vs 4 — Gemini tied for 1st of 55; stronger non‑English output quality in our tests. Ties: classification 3 vs 3, long_context 4 vs 4 (both models tied in long-context rank 38 of 55), and persona_consistency 5 vs 5 (both tied for 1st). Practical takeaway: Gemini is consistently stronger on safety, faithfulness, structured outputs, multilingual output, and high‑reliability planning; Llama matches Gemini on maintaining persona, basic classification, and long‑context retrieval but did not beat Gemini on any measured test in our suite.

Raw rates from the payload: Gemini input $0.25/mtok and output $1.50/mtok; Llama input $0.15/mtok and output $0.60/mtok (price ratio 2.5). If you assume a 50/50 split of input vs output tokens, per‑million-token costs are: Gemini $875 per 1M tokens, $8,750 per 10M, $87,500 per 100M; Llama $375 per 1M, $3,750 per 10M, $37,500 per 100M. If your workload is output-heavy (more generated text than prompt text) costs scale toward the higher output rates ($1.50/mtok vs $0.60/mtok). Teams doing single-digit‑dollar-per-1M experiments won’t notice much, but product deployments at 10M–100M tokens/month will see tens of thousands of dollars difference — ops, analytics, content platforms, and consumer-facing chatbots should care about the gap.

Choose Gemini 3.1 Flash Lite Preview if you need: best-in-class safety and faithfulness (safety_calibration 5, faithfulness 5), robust structured output (5) and multilingual quality, and you can accept higher per‑token spend. Choose Llama 4 Maverick if you need: a lower-cost multimodal model (input $0.15/output $0.60 per mtok) for high-volume, output-light workloads or cost-constrained production at 10M–100M tokens/month; it ties Gemini on persona consistency and long-context and may be sufficient where absolute safety/faithfulness is less critical. Also note Gemini’s max_output_tokens is 65,536 vs Llama’s 16,384, which matters for very long single outputs.