Which is cheaper, GPT-4.1 nano or LongCat-Flash-Lite?
As of August 25, 2026, GPT-4.1 nano is 0% cheaper on blended LLM API pricing. GPT-4.1 nano costs $0.10 per million input tokens and $0.40 per million output tokens ($0.50 blended 1M-in + 1M-out). LongCat-Flash-Lite costs $0.10 / $0.40 per million tokens ($0.50 blended). Sources: https://developers.openai.com/api/docs/pricing and https://anotherwrapper.com/tools/llm-pricing. The cheapest LLM for your app still depends on how many output tokens you generate.
How much does GPT-4.1 nano vs LongCat-Flash-Lite cost per million tokens?
GPT-4.1 nano OpenAI API pricing is $0.10 input and $0.40 output per million tokens via OpenAI. LongCat-Flash-Lite Meituan API pricing is $0.10 input and $0.40 output per million tokens via Meituan. Use those four numbers, not ChatGPT Plus or Claude Pro subscription prices, when you are comparing APIs.
Which is better for coding, GPT-4.1 nano or LongCat-Flash-Lite?
LongCat-Flash-Lite has a published SWE-bench Verified score of 54.4%. GPT-4.1 nano does not have that eval on this page yet.
What is the context window for GPT-4.1 nano vs LongCat-Flash-Lite?
GPT-4.1 nano supports a 1M token context window, while LongCat-Flash-Lite supports 256K tokens. GPT-4.1 nano 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, GPT-4.1 nano or LongCat-Flash-Lite?
LongCat-Flash-Lite leads on 7 of 7 shared benchmarks versus GPT-4.1 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 GPT-4.1 nano or LongCat-Flash-Lite better for production use?
Both GPT-4.1 nano and LongCat-Flash-Lite are production API models. For cost-sensitive production traffic, GPT-4.1 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 GPT-4.1 nano and LongCat-Flash-Lite in my app?
Yes. Call GPT-4.1 nano through OpenAI and LongCat-Flash-Lite through Meituan 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 GPT-4.1 nano vs LongCat-Flash-Lite pricing data?
List rates are the latest published OpenAI API pricing and Meituan API pricing, quoted per million tokens. Official rate sheets: https://developers.openai.com/api/docs/pricing and https://anotherwrapper.com/tools/llm-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 GPT-4.1 nano and LongCat-Flash-Lite?
GPT-4.1 nano supports up to 33K output tokens per request, while LongCat-Flash-Lite 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, GPT-4.1 nano or LongCat-Flash-Lite?
GPT-4.1 nano runs at approximately 200 tokens/second while LongCat-Flash-Lite runs at 500 tokens/second. LongCat-Flash-Lite 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 GPT-4.1 nano vs LongCat-Flash-Lite comparison?
Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open GPT-4.1 nano or LongCat-Flash-Lite from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.
