Gemini 3.1 Flash Lite Preview vs GPT-4.1

We tested 12 internal benchmark dimensions. Summary from our testing (scores shown are from the payload):

• GPT-4.1 wins (4 tests): constrained_rewriting 5 vs 4 (GPT-4.1 ranks tied for 1st on constrained_rewriting), tool_calling 5 vs 4 (GPT-4.1 tied for 1st on tool_calling), classification 4 vs 3 (GPT-4.1 ranks tied for 1st in classification), long_context 5 vs 4 (GPT-4.1 tied for 1st on long_context). These wins mean GPT-4.1 is measurably better at function selection/argument accuracy and retrieval over 30K+ tokens and is stronger for robust routing/classification tasks.
• Gemini 3.1 Flash Lite Preview wins (3 tests): structured_output 5 vs 4 (Gemini ties for 1st in structured_output), creative_problem_solving 4 vs 3, safety_calibration 5 vs 1 (Gemini ties for 1st in safety_calibration). In practice Gemini is more reliable for strict JSON/schema compliance and for safety-sensitive decisioning (refusing harmful requests while allowing legitimate ones).
• Ties (5 tests): strategic_analysis 5/5, faithfulness 5/5, persona_consistency 5/5, agentic_planning 4/4, multilingual 5/5 — both models perform equivalently on nuanced tradeoff reasoning, faithfulness to sources, multi-language output, persona maintenance, and goal decomposition in our suite. External benchmarks (Epoch AI): GPT-4.1 also has third-party scores: SWE-bench Verified 48.5%, MATH Level 5 83%, AIME 2025 38.3% (reported by Epoch AI). Gemini has no external scores in the payload. Note: the external SWE-bench/Math/AIME numbers are attributed to Epoch AI and should be used as supplementary context; internally, GPT-4.1’s tool_calling and long_context wins align with practical strengths for coding and long-doc workflows despite mixed external SWE-bench placement (rank 11 of 12 on SWE-bench Verified per the payload).

Pricing per mTok (from the payload): Gemini 3.1 Flash Lite Preview input $0.25 / output $1.50 (combined $1.75 per mTok). GPT-4.1 input $2 / output $8 (combined $10 per mTok). At scale this matters: 1M tokens (1,000 mTok) costs $1,750 on Gemini vs $10,000 on GPT-4.1; 10M tokens costs $17,500 vs $100,000; 100M tokens costs $175,000 vs $1,000,000. Teams with heavy throughput (many millions of tokens/month), chatbots, or SaaS integrations should care about the gap — Gemini reduces operational inference spend by roughly an order of magnitude in our math above. Buyers who prioritize the best tool calling, long-context, and classification performance may justify GPT-4.1’s higher cost for those specific gains.

Choose Gemini 3.1 Flash Lite Preview if: you need maximum cost-efficiency at scale (1M–100M+ tokens), strict structured outputs/JSON, strong safety calibration, or multilingual persona consistency — it costs $0.25 input / $1.50 output per mTok and wins safety_calibration and structured_output in our tests. Choose GPT-4.1 if: you need the best tool calling, long-context retrieval, constrained-rewriting, or higher classification accuracy (GPT-4.1 scores 5 on tool_calling and long_context vs Gemini’s 4), and you can absorb higher inference costs ($2 input / $8 output per mTok).