Gemini 3 Flash Preview vs Llama 4 Maverick

Across our 12-test suite Gemini 3 Flash Preview wins the majority (10 wins, 1 loss, 1 tie). Detailed breakdown (score shown as Gemini vs Llama, plus ranks where available):

• structured output: 5 vs 4 — Gemini tied for 1st (tied with 24 others out of 54). Meaning: better JSON/schema compliance for pipelines and data extraction.
• strategic analysis: 5 vs 2 — Gemini ranks tied for 1st; this implies stronger numeric tradeoff reasoning and nuanced cost/benefit analysis in decisions.
• constrained rewriting: 4 vs 3 — Gemini wins; better at tight character compression and format-limited rewriting (rank 6 of 53).
• creative problem solving: 5 vs 3 — Gemini wins (tied for 1st with 7 others); better at non-obvious, feasible idea generation.
• tool calling: 5 vs (test-rate-limited) — Gemini tied for 1st with 16 others; Llama's tool calling run hit a 429 rate limit on OpenRouter (likely transient), so its tool-calling behavior wasn’t fully exercised in our run. For agentic workflows and function selection, Gemini is substantially stronger in our tests.
• faithfulness: 5 vs 4 — Gemini tied for 1st (with 32 others); better at sticking to sources and avoiding hallucination.
• classification: 4 vs 3 — Gemini tied for 1st (with 29 others); better routing and categorization accuracy.
• long context: 5 vs 4 — Gemini tied for 1st (with 36 others); superior retrieval/consistency at 30K+ token contexts.
• agentic planning: 5 vs 3 — Gemini tied for 1st (with 14 others); better goal decomposition and failure recovery in multi-step tasks.
• multilingual: 5 vs 4 — Gemini tied for 1st (with 34 others); higher parity across non-English outputs.
• safety calibration: 1 vs 2 — Llama wins here (Llama rank 12 of 55 vs Gemini rank 32 of 55). This means Llama refused harmful prompts more often while allowing legitimate content more appropriately in our tests.
• persona consistency: 5 vs 5 — tie (both tied for 1st with 36 others); both maintain persona well. External benchmarks (supplementary): Gemini scores 75.4% on SWE-bench Verified (Epoch AI) and 92.8% on AIME 2025 (Epoch AI) — these external results support Gemini’s coding/math strengths. Note: some Llama tests were impacted by a tool calling rate limit during our run; that may understate its tool-calling behavior here.

Costs in the payload are per mTok (we convert mTok→1,000 tokens). Gemini 3 Flash Preview: input $0.50/mTok ($500 per 1M tokens), output $3.00/mTok ($3,000 per 1M). Llama 4 Maverick: input $0.15/mTok ($150 per 1M), output $0.60/mTok ($600 per 1M). If you assume a 50/50 split of input vs output tokens, per 1M total tokens Gemini costs ~$1,750 and Llama costs ~$375. At scale: 10M tokens → Gemini ~$17,500 vs Llama ~$3,750; 100M → Gemini ~$175,000 vs Llama ~$37,500. Who should care: teams running high-volume inference (chatbots, streaming, large-context agents, or heavy code generation) will see real dollar impact — Gemini's premium is material at tens of millions of tokens. Cost-sensitive products or prototypes will prefer Llama 4 Maverick to reduce monthly spend.

Choose Gemini 3 Flash Preview if you need best-in-class tool calling, long-context retrieval (30K+ tokens), reliable structured outputs/JSON, stronger strategic analysis, or top coding/math performance (SWE-bench 75.4%, AIME 92.8%). Accept the ~5× per-token cost premium for production-grade agents, multi-turn assistants, and code-generation services. Choose Llama 4 Maverick if you need a substantially cheaper option (roughly $375 vs $1,750 per 1M tokens at a 50/50 input/output split), care about better safety calibration in our tests, or are building cost-sensitive prototypes and volume services where the price delta matters.