Gemini 3.1 Flash Lite Preview vs GPT-4.1 Nano

Summary of our 12-test head-to-head (scores from our test suite): Wins for Gemini 3.1 Flash Lite Preview (modelA):

• Safety calibration: 5 (Gemini) vs 2 (GPT‑4.1 Nano). Gemini is tied for 1st in safety calibration across 55 models (tied with 4 others), meaning it more reliably refuses harmful requests while allowing legitimate ones in our testing.
• Strategic analysis: 5 vs 2. Gemini is tied for 1st of 54 models on strategic analysis, indicating stronger nuanced tradeoff reasoning for tasks like pricing, ROI, or multi-criteria decisions.
• Persona consistency: 5 vs 4. Gemini ties for 1st among 53 models on persona consistency, so it better maintains roleplay and resists injections in our tests.
• Multilingual: 5 vs 4. Gemini ties for 1st of 55 models, showing higher-quality non‑English outputs in our suite.
• Creative problem solving: 4 vs 2. Gemini ranks 9th of 54, producing more feasible, non-obvious ideas in our tests. Ties (no clear winner):
• Structured output: both 5 (tied for 1st) — both models adhere to JSON/schema style constraints equally well in our tests.
• Constrained rewriting: both 4 (rank 6 of 53) — both compress or rewrite to hard character limits similarly.
• Tool calling: both 4 (rank 18 of 54) — function selection and argument correctness are similar in our tests.
• Faithfulness: both 5 (tied for 1st) — both stick to source material without hallucinating in our prompts.
• Classification: both 3 — similar routing/labeling accuracy in our suite.
• Long context: both 4 — both maintain retrieval accuracy at 30k+ token tests similarly (rank 38 of 55 for each).
• Agentic planning: both 4 (rank 16 of 54) — decomposition and recovery behaviors are comparable. Notable ranking context (Gemini): safety tied for 1st (out of 55), strategic analysis tied for 1st (out of 54), multilingual and persona_consistency both tied for 1st in their cohorts. Notable ranking context (GPT‑4.1 Nano): strategic analysis (rank 44 of 54) and creative problem solving (rank 47 of 54) trail Gemini by large margins in our internal tests. External benchmarks (supplementary): GPT‑4.1 Nano scores 70% on MATH Level 5 and 28.9% on AIME 2025 according to Epoch AI — useful if you prioritize external math competition metrics. Gemini has no external math scores provided in the payload. These external numbers are Epoch AI results and are cited only as supplementary evidence.

Costs shown are per mTok (model input/output rates from the payload). Gemini 3.1 Flash Lite Preview: input $0.25 + output $1.50 = $1.75 per mTok combined. GPT‑4.1 Nano: input $0.10 + output $0.40 = $0.50 per mTok combined. At realistic volumes (per 1,000-token block = 1 mTok):

• 1M tokens (1,000 mTok): Gemini = $1,750; GPT‑4.1 Nano = $500.
• 10M tokens (10,000 mTok): Gemini = $17,500; GPT‑4.1 Nano = $5,000.
• 100M tokens (100,000 mTok): Gemini = $175,000; GPT‑4.1 Nano = $50,000. Who should care: any high-volume product (chat, summarization, ingestion pipelines) — the choice changes TCO by ~3.5× (Gemini combined cost 1.75/0.5 = 3.5×). Use GPT‑4.1 Nano when cost, latency, or scale are the priority; use Gemini when per-response quality, safety, multilingual fidelity, or larger single-response outputs matter enough to justify the higher spend.

Choose Gemini 3.1 Flash Lite Preview if: you need higher per-response quality on safety-sensitive flows, strategic tradeoffs, multilingual experiences, persona consistency, or larger single-response outputs (Gemini max_output_tokens 65,536 vs GPT‑4.1 Nano 32,768). Choose GPT‑4.1 Nano if: you must minimize cost and latency at scale (input $0.10/output $0.40 per mTok), need a fast, inexpensive model for high-volume chat or ingestion, or want the best price-performance for basic structured output and tool-calling workloads.