Which is cheaper, Gemini 3.7 Flash or GPT-5 nano?
As of August 20, 2026, GPT-5 nano is 90% cheaper on blended LLM API pricing. GPT-5 nano costs $0.05 per million input tokens and $0.40 per million output tokens ($0.45 blended 1M-in + 1M-out). Gemini 3.7 Flash costs $0.75 / $3.75 per million tokens ($4.50 blended). Sources: https://ai.google.dev/gemini-api/docs/pricing and https://developers.openai.com/api/docs/pricing. The cheapest LLM for your app still depends on how many output tokens you generate.
How much does Gemini 3.7 Flash vs GPT-5 nano cost per million tokens?
Gemini 3.7 Flash Gemini API pricing is $0.75 input and $3.75 output per million tokens via Google. GPT-5 nano OpenAI API pricing is $0.05 input and $0.40 output per million tokens via OpenAI. Use those four numbers, not ChatGPT Plus or Claude Pro subscription prices, when you are comparing APIs.
Which is better for coding, Gemini 3.7 Flash or GPT-5 nano?
Gemini 3.7 Flash leads on SWE-bench Verified: Gemini 3.7 Flash at 80.8% vs GPT-5 nano at 34.8%. SWE-bench measures real GitHub fixes; LiveCodeBench measures contest programming.
What is the context window for Gemini 3.7 Flash vs GPT-5 nano?
Gemini 3.7 Flash supports a 1M token context window, while GPT-5 nano supports 400K tokens. Gemini 3.7 Flash offers a larger context window. Filling a larger window bills more input tokens, so the cheaper-per-million model can still cost more on long documents.
Which model performs better on benchmarks, Gemini 3.7 Flash or GPT-5 nano?
Gemini 3.7 Flash leads on 12 of 12 shared benchmarks versus GPT-5 nano's 0 wins. Check the comparison table for GPQA Diamond, SWE-bench, MMLU, HLE, and the other evals we track. Token price and benchmark score together are the usual LLM comparison, not either number alone.
Is Gemini 3.7 Flash or GPT-5 nano better for production use?
Both Gemini 3.7 Flash and GPT-5 nano are production API models. For cost-sensitive production traffic, GPT-5 nano has better economics on the blended 1M-in + 1M-out scale. For maximum capability, weight the benchmark table for your domain. Many production stacks route cheap models for drafts and a frontier model for the hard turn, which is usually cheaper than sending everything to the expensive API.
Can I switch between Gemini 3.7 Flash and GPT-5 nano in my app?
Yes. Call Gemini 3.7 Flash through Google and GPT-5 nano through OpenAI with separate API keys, or through a gateway that already wraps both. Keep prompts in tokens, not characters, when you estimate the invoice. AnotherWrapper templates can swap providers without rewriting auth and billing.
How accurate is this Gemini 3.7 Flash vs GPT-5 nano pricing data?
List rates are the latest published Gemini API pricing and OpenAI API pricing, quoted per million tokens. Official rate sheets: https://ai.google.dev/gemini-api/docs/pricing and https://developers.openai.com/api/docs/pricing. Prompt caching, batch APIs, and committed-use discounts can change the invoice. Benchmark scores are from official publications and independent evals, updated as new numbers land.
What is the output token limit for Gemini 3.7 Flash and GPT-5 nano?
Gemini 3.7 Flash supports up to 66K output tokens per request, while GPT-5 nano supports up to 128K output tokens. Output tokens are usually the expensive half of API pricing, so a higher max output cap is a capability, not a discount.
Which model has better throughput, Gemini 3.7 Flash or GPT-5 nano?
Gemini 3.7 Flash runs at approximately 12.65240051066044 tokens/second while GPT-5 nano runs at 500 tokens/second. GPT-5 nano is faster in raw throughput. Throughput (tokens per second) and time-to-first-token are why two models with the same per-million-token price can still feel different in chat and agents.
How do I switch the models in this Gemini 3.7 Flash vs GPT-5 nano comparison?
Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open Gemini 3.7 Flash or GPT-5 nano from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.
