Gemini 3.5 Flash (medium) vs GPT-5 nano (high): The Ultimate Performance & Pricing Comparison

Gemini 3.5 Flash (medium) is the stronger quality candidate, while GPT-5 nano (high) is the clear cost candidate for developers. The supplied benchmark data gives Gemini an Artificial Analysis Intelligence Index of 45.4, compared with 19.9 for GPT-5 nano. GPT-5 nano costs $0.1375 per 1M blended tokens, compared with $3.375 for Gemini. The selection is therefore a tradeoff between measured general intelligence, uncertain product availability, and radically different economics. Data provided by Artificial Analysis.

Gemini 3.5 Flash (medium) is the better default for quality-sensitive developer workflows, but GPT-5 nano (high) is the safer economic choice for high-volume automation. The benchmark gap is substantial: Gemini scores 45.4 on the Artificial Analysis Intelligence Index, while GPT-5 nano scores 19.9. That evidence supports Gemini for tasks where broader reasoning quality, coding assistance, or agentic behavior determines whether a workflow succeeds.

GPT-5 nano remains attractive because its blended price is $0.1375 per 1M tokens, versus $3.375 for Gemini. Its input price is $0.05 per 1M tokens and its output price is $0.4 per 1M tokens. Those figures make GPT-5 nano suitable for workloads that process large volumes of routine requests, provided the model can be called reliably.

Availability changes the decision. Google lists gemini-3.5-flash as Stable and provides an official API alias, although the official model list does not separately identify Gemini 3.5 Flash (medium). OpenAI's current model directory and pricing page do not list GPT-5 nano or gpt-5-nano. Developers should therefore verify the actual endpoint and model behavior before committing either model to production. See Google's model documentation, Google's pricing documentation, OpenAI's model directory, and OpenAI's pricing page.

Gemini 3.5 Flash (medium) has the stronger measured general-intelligence result and the only reported output-speed figure in this comparison. Gemini records 45.4 on the Artificial Analysis Intelligence Index, compared with 19.9 for GPT-5 nano, a difference of 25.5. That gap is large enough to make Gemini the more credible starting point for complex coding prompts, multi-step agent workflows, and tasks where weak intermediate reasoning can create downstream repair work.

The benchmark does not prove that Gemini wins every developer task. GPT-5 nano has a Math Index of 83.7, while no comparable Gemini math result is present in the supplied data. The available evidence therefore supports a quality advantage for Gemini on the reported intelligence measure, but it does not establish a universal capability ranking. Developers with mathematical workloads should test representative problems rather than infer the result from the general index.

Gemini's median output speed is 276.619 tokens per second. GPT-5 nano has no reported value for this metric, so the data cannot establish a speed winner. Both models show latency of 0.3 seconds in the supplied comparison. That tie suggests similar measured request latency in this dataset, but it says nothing about streaming behavior, queueing, rate limits, tool-call overhead, or production variance.

The official evidence also leaves important performance questions unanswered. Google's verified pages do not provide benchmark scores for this model, while OpenAI's current directory does not provide GPT-5 nano-specific limits or capabilities. The benchmark source is therefore useful for comparative signals, but developers still need task-level evaluation for code edits, structured output, tool use, long prompts, and failure recovery.

GPT-5 nano (high) is the clear cost winner, and the price difference is large enough to dominate most high-volume token budgets. Its blended price is $0.1375 per 1M tokens, compared with $3.375 for Gemini 3.5 Flash (medium). GPT-5 nano also costs $0.05 per 1M input tokens and $0.4 per 1M output tokens, compared with Gemini at $1.5 and $9 respectively.

The chart cannot show whether cheaper tokens reduce or increase total system cost. A weaker model may require retries, stricter validation, additional repair prompts, or a second model for difficult cases. Those costs are not included in the supplied pricing comparison. GPT-5 nano is therefore most compelling when requests are routine, outputs are short or easily validated, and the application can tolerate uncertain availability.

Gemini's higher price can still be rational when one successful response replaces multiple attempts or human review. The available evidence does not measure retry rates, task completion, or engineering maintenance cost, so no universal break-even point can be established. Developers should compare total workflow cost, not token price alone.

Google documents additional pricing conditions for gemini-3.5-flash. Paid usage supports context caching, and Google Search and Google Maps grounding each have 5,000 shared free queries per month before charges of $14 per 1,000 queries. The free tier does not provide Search or Maps grounding. These rules matter when Gemini is selected for grounded applications. They are documented on Google's pricing page. OpenAI's current pricing page lists gpt-5.4-nano, not GPT-5 nano, so its listed price must not be substituted for this comparison. See the OpenAI pricing documentation.

Gemini 3.5 Flash (medium) is the better first choice for quality-sensitive agents, coding assistants, and workflows where failure recovery is expensive. Its measured Intelligence Index is 45.4, and Google describes gemini-3.5-flash as a model focused on sustained frontier performance, agentic tasks, coding, speed, intelligence, Search, and grounding. Google also lists the official alias as Stable in its Gemini API model documentation.

GPT-5 nano (high) is the better first candidate for large-scale classification, extraction, routing, and other routine operations where cost dominates and the endpoint is verified. Its blended price is $0.1375 per 1M tokens, and its Math Index is 83.7. Those facts support an economical evaluation path, but they do not confirm current API availability. OpenAI's current model directory does not list GPT-5 nano.

Choose Gemini when the application needs a currently documented Google endpoint, grounding integration, or stronger evidence on the reported general-intelligence measure. Choose GPT-5 nano only after confirming that the intended model identifier resolves to a live, supported endpoint. Do not use gpt-5.4-nano information as a proxy for GPT-5 nano. The supplied research found no reliable community posts that establish either model's coding feel, speed perception, or behavioral quirks.

A staged architecture can reduce risk: use the cheaper model for low-risk requests, route difficult cases to Gemini, and measure completion quality on real tasks. The available data does not say whether such routing will outperform a single-model design. That result must be established through application-specific testing.

Gemini 3.5 Flash (medium) requires an identifier check because Google's official list names gemini-3.5-flash, not the supplied medium variant. Developers should confirm that their platform's slug maps to the documented Stable alias before writing production configuration. Google's verified pages do not state the context window, maximum output tokens, detailed API parameters, or multimodal limits for the specific variant.

GPT-5 nano (high) requires an even stronger availability check because OpenAI's current model directory and pricing page do not list GPT-5 nano or gpt-5-nano. The supplied release date is 2025-08-07, but the verified current pages do not provide a matching release record or current price. Developers should treat the benchmark and pricing snapshot as comparison inputs, not as proof of present endpoint availability.

Neither source set provides reliable community evidence or model-specific failure cases. A production decision should therefore include endpoint verification, structured-output tests, coding tasks, math tasks, retry measurements, and grounding tests where relevant.