Which is cheaper, Gemini 1.5 Flash 8B or GPT-4.1 nano?
As of August 25, 2026, Gemini 1.5 Flash 8B is 26% cheaper on blended LLM API pricing. Gemini 1.5 Flash 8B costs $0.07 per million input tokens and $0.30 per million output tokens ($0.37 blended 1M-in + 1M-out). GPT-4.1 nano costs $0.10 / $0.40 per million tokens ($0.50 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 1.5 Flash 8B vs GPT-4.1 nano cost per million tokens?
Gemini 1.5 Flash 8B Gemini API pricing is $0.07 input and $0.30 output per million tokens via Google. GPT-4.1 nano OpenAI API pricing is $0.10 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 1.5 Flash 8B or GPT-4.1 nano?
GPT-4.1 nano has a published LiveCodeBench score of 42.7%. Gemini 1.5 Flash 8B does not have that eval on this page yet.
What is the context window for Gemini 1.5 Flash 8B vs GPT-4.1 nano?
Gemini 1.5 Flash 8B supports a 1M token context window, while GPT-4.1 nano supports 1M tokens. Gemini 1.5 Flash 8B 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 1.5 Flash 8B or GPT-4.1 nano?
GPT-4.1 nano leads on 6 of 6 shared benchmarks versus Gemini 1.5 Flash 8B'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 1.5 Flash 8B or GPT-4.1 nano better for production use?
Both Gemini 1.5 Flash 8B and GPT-4.1 nano are production API models. For cost-sensitive production traffic, Gemini 1.5 Flash 8B 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 1.5 Flash 8B and GPT-4.1 nano in my app?
Yes. Call Gemini 1.5 Flash 8B through Google and GPT-4.1 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 1.5 Flash 8B vs GPT-4.1 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 1.5 Flash 8B and GPT-4.1 nano?
Gemini 1.5 Flash 8B supports up to 8K output tokens per request, while GPT-4.1 nano supports up to 33K 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 1.5 Flash 8B or GPT-4.1 nano?
Gemini 1.5 Flash 8B runs at approximately 150 tokens/second while GPT-4.1 nano runs at 200 tokens/second. GPT-4.1 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 1.5 Flash 8B vs GPT-4.1 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 1.5 Flash 8B or GPT-4.1 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.
