Gemini 3.1 Flash Lite Preview vs GPT-5

Across our 12-test benchmark suite (scored 1–5), GPT-5 wins 4 categories, Gemini 3.1 Flash Lite Preview wins 1, and 7 are tied.

Where GPT-5 wins:

• Tool calling: GPT-5 scores 5 vs Flash Lite Preview's 4 — tied for 1st among 54 models vs rank 18 of 54. This covers function selection, argument accuracy, and sequencing. For agentic pipelines that depend on reliable tool use, GPT-5 has a clear edge.
• Classification: GPT-5 scores 4 vs Flash Lite Preview's 3 — tied for 1st among 53 models vs rank 31 of 53. Flash Lite Preview sits below the median (p50 = 4) on this test. Routing and categorization tasks suffer meaningfully at a score of 3.
• Long context: GPT-5 scores 5 vs Flash Lite Preview's 4 — tied for 1st among 55 models vs rank 38 of 55. Flash Lite Preview has a 1M-token context window vs GPT-5's 400K, but our test (retrieval accuracy at 30K+ tokens) favors GPT-5 for precision. Larger window doesn't automatically mean better retrieval.
• Agentic planning: GPT-5 scores 5 vs Flash Lite Preview's 4 — tied for 1st among 54 models vs rank 16 of 54. Goal decomposition and failure recovery are stronger with GPT-5, which matters for autonomous workflows.

Where Gemini 3.1 Flash Lite Preview wins:

• Safety calibration: Flash Lite Preview scores 5 vs GPT-5's 2 — tied for 1st among 5 models out of 55 tested, while GPT-5 ranks 12th of 55. The p75 for this benchmark is only 2 across the field, making Flash Lite Preview's 5 a genuine standout. GPT-5 scores well below the median on refusing harmful requests while permitting legitimate ones — a significant liability for public-facing deployments.

Tied categories (7 of 12):

• Structured output (both 5/5): Both tied for 1st of 54 — JSON schema compliance is equally strong.
• Strategic analysis (both 5/5): Both tied for 1st of 54 — nuanced tradeoff reasoning is equivalent.
• Creative problem solving (both 4/5): Both rank 9 of 54.
• Faithfulness (both 5/5): Both tied for 1st of 55.
• Persona consistency (both 5/5): Both tied for 1st of 53.
• Multilingual (both 5/5): Both tied for 1st of 55.
• Constrained rewriting (both 4/5): Both rank 6 of 53.

External benchmarks (GPT-5 only, sourced from Epoch AI): GPT-5 scores 73.6% on SWE-bench Verified (rank 6 of 12 models with data), 98.1% on MATH Level 5 (rank 1 of 14 — sole holder of the top score), and 91.4% on AIME 2025 (rank 6 of 23). The MATH Level 5 score of 98.1% places GPT-5 above the field median of 94.15%, and it is the sole top scorer. On AIME 2025, 91.4% exceeds the p50 of 83.9%. No external benchmark data is available for Gemini 3.1 Flash Lite Preview in the payload. These scores independently confirm GPT-5's strength in complex reasoning tasks — particularly math — and reinforce our internal findings.

Gemini 3.1 Flash Lite Preview costs $0.25 per million input tokens and $1.50 per million output tokens. GPT-5 costs $1.25 input and $10.00 output — 5x more on input and 6.7x more on output. At 1M output tokens/month, that's $1.50 vs $10.00 — an $8.50 difference you might not notice. At 10M output tokens, it's $15 vs $100 — $85/month in savings. At 100M output tokens, Flash Lite Preview saves $850/month versus GPT-5. Note that GPT-5 uses reasoning tokens (flagged in the payload), which may add further cost depending on your usage pattern. High-volume applications — content pipelines, classification at scale, customer support bots — will feel this gap acutely. Teams running low-volume, high-stakes workflows (legal analysis, complex agentic tasks) may find GPT-5's capability edge worth the premium. Developers price-sensitive enough to route to a lite model in the first place should treat GPT-5's output cost as a hard constraint to evaluate before committing.

Choose Gemini 3.1 Flash Lite Preview if:

• Cost is a primary constraint — it costs 6.7x less on output tokens ($1.50 vs $10.00/MTok)
• Your use case is consumer-facing and safety calibration matters — Flash Lite Preview scores 5/5 vs GPT-5's 2/5 in our testing
• You're running high-volume pipelines (content generation, summarization, multilingual output) where the 7 tied benchmarks cover your core needs
• You need a 1M-token context window (vs GPT-5's 400K) for very large document ingestion
• You require audio or video input modality (Flash Lite Preview supports text+image+file+audio+video; GPT-5 supports text+image+file)

Choose GPT-5 if:

• You're building agentic or tool-calling workflows — GPT-5 scores 5/5 on both agentic planning and tool calling vs Flash Lite Preview's 4/4
• Classification accuracy is critical — GPT-5 scores 4 vs Flash Lite Preview's 3, which sits below the field median
• Long-context retrieval precision matters — GPT-5 scores 5 vs 4 at 30K+ tokens
• You need top-tier math or coding capability — GPT-5 scores 98.1% on MATH Level 5 (rank 1 of 14) and 73.6% on SWE-bench Verified (Epoch AI)
• You're using reasoning tokens and can absorb the additional cost for complex, multi-step tasks
• Volume is low enough that the price difference ($8.50/1M output tokens) is not a budget constraint