Gemini 1.5 Pro (Sep '24) vs GPT-5.5 Pro (xhigh): The Ultimate Performance & Pricing Comparison

Gemini 1.5 Pro (Sep '24) is the only model with usable evaluation evidence, but neither model is ready for a low-risk production choice based on the supplied sources. The dataset records Gemini 1.5 Pro (Sep '24) with a 23.6 coding index, a 9.9 intelligence index, and a 0.75 MMLU-Pro score. GPT-5.5 Pro (xhigh) has no evaluation values in the dataset, so this comparison cannot establish a capability winner. Data provided by https://artificialanalysis.ai/.\n\nThe larger issue is availability. Google's current Gemini API model documentation does not show an active model card for Gemini 1.5 Pro (Sep '24). OpenAI's current model documentation does not list gpt-5-5-pro either. Developers should therefore treat this as an evidence and migration-risk comparison, not as a normal head-to-head product recommendation.

Gemini 1.5 Pro (Sep '24) is the stronger documented candidate because it has benchmark evidence, while GPT-5.5 Pro (xhigh) remains unevaluable from the supplied data. Gemini records 23.6 on the Artificial Analysis coding index and 0.316 on LiveCodeBench, while GPT-5.5 Pro (xhigh) has null values for those measures. That does not prove Gemini is more capable overall. It proves only that Gemini has a measurable record in this dataset.\n\nGemini also has a clearer historical product identity. Google's official materials describe the Gemini 1.5 Pro family as a multimodal model for complex tasks, accepting text, images, video, and audio while producing text. The same official source historically associated the family with very large context handling, but the current page does not preserve a verified context-window record for this specific Sep '24 entry. Google's model documentation is therefore useful for product history, not for confirming today's production contract.\n\nGPT-5.5 Pro (xhigh) is harder to select because the supplied OpenAI sources do not provide a model card, dedicated alias, benchmark record, context limit, output limit, or failure profile. The current catalog lists gpt-5.6-sol, gpt-5.6-terra, and gpt-5.6-luna, but not gpt-5-5-pro. OpenAI's model documentation supports that catalog observation, but it does not explain whether GPT-5.5 Pro was retired, renamed, private, or never generally available.\n\nThe practical summary is simple: choose Gemini for a controlled compatibility investigation, and choose neither for an immediate production commitment without first confirming a live endpoint, current terms, and a repeatable task-level test.

Gemini 1.5 Pro (Sep '24) is the only model whose supplied benchmark record can inform a developer's initial testing plan. Its recorded scores include 0.75 on MMLU-Pro, 0.589 on GPQA, 0.046 on HLE, 0.316 on LiveCodeBench, 0.295 on SciCode, 0.876 on Math 500, and 0.23 on AIME. These figures suggest that Gemini can serve as a baseline across general knowledge, coding, scientific reasoning, and mathematics. They do not reveal how the model behaves on your repository, tool chain, prompts, or latency budget.\n\nGPT-5.5 Pro (xhigh) cannot be ranked against those results because every corresponding evaluation field is null in the supplied data. The missing values are not low scores. They are absent measurements. Developers should not convert that absence into either optimism or pessimism. OpenAI's current model documentation provides broad statements about text and image input, text output, multilingual ability, vision, the Responses API, and client SDK access, but it does not confirm that those statements apply specifically to gpt-5-5-pro.\n\nThe speed chart is also inconclusive. Both models are recorded at 0 median output tokens per second and 0 seconds latency. Those values cannot distinguish a fast model from a missing or non-comparable measurement. A real selection test should measure time to first useful answer, total completion time, retry frequency, tool-call success, and the amount of human correction required. The supplied evidence does not answer those questions.\n\nFor developers, the benchmark difference changes the next action, not the final architecture. Gemini can start a documented baseline test. GPT-5.5 Pro requires endpoint verification and fresh testing before any capability claim is safe.

Gemini 1.5 Pro (Sep '24) and GPT-5.5 Pro (xhigh) are tied at $0 in the supplied pricing fields, but that tie must not be interpreted as free production access. The dataset records both models at $0 per 1M blended tokens, $0 per 1M input tokens, and $0 per 1M output tokens. The same dataset records 0 median output tokens per second and 0 seconds latency for both models. These entries are useful as data points, but they do not establish a billable API offer.\n\nThe official pages point in the same operational direction: neither model has a currently verifiable public price in the supplied material. Google's pricing documentation does not list Gemini 1.5 Pro (Sep '24) with an active free, paid, or batch price. OpenAI's pricing documentation does not list GPT-5.5 Pro or gpt-5-5-pro; it lists pricing for the GPT-5.6 family instead.\n\nThat means the cheaper choice is not knowable. A model with a nominal $0 data value can still be more expensive if it cannot be called, requires migration work, forces a different SDK, or produces more review and retry work. The reverse is also possible: a newer replacement could cost more per request but reduce engineering effort and operational failures. The supplied sources do not provide request pricing, token accounting rules, caching prices, batch prices, rate limits, or migration effort.\n\nBefore approving a budget, confirm the exact model identifier, input and output billing, service availability, rate limits, and replacement path. Until then, both cost figures should be treated as unavailable rather than as a purchasing advantage.

Gemini 1.5 Pro (Sep '24) is the better starting point for an evidence-led evaluation, but it is not a safe production recommendation without endpoint confirmation. Gemini has a release date of 2024-09-24 and a visible evaluation record. That makes it possible to build an initial test set around coding, mathematical reasoning, scientific tasks, and general knowledge. The result should be treated as a historical baseline because Google's current model documentation no longer presents this specific version as an active model entry.\n\nGPT-5.5 Pro (xhigh) should remain an unselected candidate until OpenAI confirms what it is. The supplied data gives it a release date of 2026-04-23, but it provides no benchmark values, context window, pricing, model card, dedicated alias, or verified endpoint. The current OpenAI catalog instead shows gpt-5.6-sol, gpt-5.6-terra, and gpt-5.6-luna. OpenAI's model documentation does not state whether gpt-5-5-pro is unavailable, renamed, restricted, or superseded.\n\nThe selection rule should follow the project risk.\n\n- For a research comparison, start with Gemini because its 23.6 coding index and 0.876 Math 500 score provide measurable anchors.\n- For a new production integration, select neither until each vendor confirms a live identifier, current pricing, supported inputs, and service terms.\n- For a migration decision, test the exact replacement model rather than assuming that a current GPT-5.6 listing or a historical Gemini capability automatically carries over.\n\nThe evidence is insufficient to claim that Gemini is more capable than GPT-5.5 Pro. It is sufficient to say that Gemini is currently easier to evaluate, while both candidates carry material availability uncertainty.

Gemini 1.5 Pro (Sep '24) is easier to investigate because the supplied dataset contains benchmark values, while GPT-5.5 Pro (xhigh) has no corresponding measurements. The absence of GPT-5.5 Pro results prevents a fair capability ranking and should trigger a verification task before implementation.