Gemini 2.5 Flash Lite vs GPT-5.2

Neither model has been through our full 12-test benchmark suite with averaged scores yet — the payload carries individual test scores but no composite bench_avg_score for either model. Here's what our per-test results show:

Where GPT-5.2 wins (5 tests):

• Strategic analysis: GPT-5.2 scores 5/5 (tied for 1st with 25 others out of 54 tested); Flash Lite scores 3/5 (rank 36 of 54). This measures nuanced tradeoff reasoning with real numbers — a meaningful gap for business analysis, product strategy, and decision-support tools.
• Creative problem solving: GPT-5.2 scores 5/5 (tied for 1st with 7 others out of 54); Flash Lite scores 3/5 (rank 30 of 54). Non-obvious, specific, feasible ideation is noticeably stronger in GPT-5.2 in our tests.
• Classification: GPT-5.2 scores 4/5 (tied for 1st with 29 others out of 53); Flash Lite scores 3/5 (rank 31 of 53). For routing, categorization, and triage workloads, GPT-5.2 has a one-point edge.
• Safety calibration: GPT-5.2 scores 5/5 (tied for 1st with 4 others out of 55 — a much more exclusive group); Flash Lite scores 1/5 (rank 32 of 55). This is the starkest gap in the dataset. Flash Lite's score of 1 sits at the 25th percentile of models we've tested; GPT-5.2's 5 sits above the 75th percentile. For applications handling sensitive content or requiring reliable refusal behavior, this is a significant finding.
• Agentic planning: GPT-5.2 scores 5/5 (tied for 1st with 14 others out of 54); Flash Lite scores 4/5 (rank 16 of 54). Both are above the median, but GPT-5.2's top-tier score matters for multi-step autonomous workflows.

Where Gemini 2.5 Flash Lite wins (1 test):

• Tool calling: Flash Lite scores 5/5 (tied for 1st with 16 others out of 54); GPT-5.2 scores 4/5 (rank 18 of 54). Flash Lite edges ahead on function selection, argument accuracy, and sequencing — relevant for structured API integrations and function-calling pipelines.

Where they tie (6 tests, all scored equally):

• Structured output (both 4/5), constrained rewriting (both 4/5), faithfulness (both 5/5), long context (both 5/5), persona consistency (both 5/5), multilingual (both 5/5).

Notably, both models achieve the top score of 5/5 on faithfulness, long context, persona consistency, and multilingual — all tied for 1st in their respective categories. For retrieval accuracy at 30K+ tokens, sticking to source material, maintaining character, and non-English output quality, these models are indistinguishable in our testing.

External benchmarks (GPT-5.2 only):

GPT-5.2 has external benchmark data from Epoch AI that Flash Lite lacks. On AIME 2025 (math olympiad), GPT-5.2 scores 96.1% — ranked 1st of 23 models in that dataset, sole holder of that score. The median across models with AIME 2025 data is 83.9%, placing GPT-5.2 well above the midpoint. On SWE-bench Verified (real GitHub issue resolution), GPT-5.2 scores 73.8% — ranked 5th of 12 models in that dataset. The median is 70.8%, so GPT-5.2 sits above the midpoint but not at the top. Flash Lite has no external benchmark data in the payload, so direct comparison on these dimensions isn't possible.

The pricing gap here is dramatic. Gemini 2.5 Flash Lite costs $0.10/M input and $0.40/M output tokens. GPT-5.2 costs $1.75/M input and $14.00/M output tokens — 17.5x more expensive on input and 35x more expensive on output.

At real-world volumes, assuming a typical 1:3 input-to-output token ratio:

• 1M output tokens/month: Flash Lite costs ~$0.40; GPT-5.2 costs ~$14.00. A $13.60 difference — negligible for most teams.
• 10M output tokens/month: Flash Lite costs ~$4.00; GPT-5.2 costs ~$140.00. The gap becomes meaningful for startups watching margins.
• 100M output tokens/month: Flash Lite costs ~$40.00; GPT-5.2 costs ~$1,400.00. At this scale, the $1,360 monthly difference is a genuine infrastructure cost decision.

Who should care: Any developer building a product with sustained user traffic — chatbots, document processing pipelines, content generation tools — should run the numbers carefully. If your workload leans on the benchmarks where these models tie (long context retrieval, structured output, faithfulness, multilingual), you're paying a 35x premium for GPT-5.2 with no measurable return in our testing. If your workload specifically requires strong agentic planning, strategic analysis, or creative problem solving, GPT-5.2's wins in those areas may justify the cost depending on how central those capabilities are to your product.

Choose Gemini 2.5 Flash Lite if:

• Cost efficiency is a priority — you're running high token volumes where the 35x output price difference ($0.40 vs $14.00/M tokens) materially affects your budget.
• Your workload is heavily tool-calling-oriented — Flash Lite scores 5/5 vs GPT-5.2's 4/5 in our testing.
• Your tasks fall in the tie-zone: long context retrieval, multilingual output, faithfulness to source material, structured output, persona consistency, or constrained rewriting — Flash Lite matches GPT-5.2 on all six at a fraction of the cost.
• You're building pipelines that process large volumes of documents, translate content, or generate structured data at scale.
• You need audio or video input modality — Flash Lite supports text+image+file+audio+video input; GPT-5.2 supports text+image+file only.

Choose GPT-5.2 if:

• Safety calibration is non-negotiable — GPT-5.2 scores 5/5 (among a very exclusive group of 5 models at that tier) vs Flash Lite's 1/5 in our testing. This is the single most important differentiator for sensitive or regulated applications.
• Your product depends on agentic workflows — GPT-5.2 scores 5/5 on agentic planning vs 4/5 for Flash Lite, and its 96.1% AIME 2025 score (rank 1 of 23, per Epoch AI) suggests strong underlying reasoning.
• Creative problem solving and strategic analysis are core to your use case — GPT-5.2 scores 5/5 on both vs Flash Lite's 3/5.
• You need higher max output tokens — GPT-5.2 supports 128,000 max output tokens vs Flash Lite's 65,535.
• Volume is low enough that the price premium doesn't compound into a real budget problem.