Which is cheaper, GPT-4.1 nano or Qwen3.8-2.4T-A95B?
As of August 20, 2026, GPT-4.1 nano is 94% 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). Qwen3.8-2.4T-A95B costs $2.00 / $6.00 per million tokens ($8.00 blended). Sources: https://developers.openai.com/api/docs/pricing and https://www.alibabacloud.com/help/en/model-studio/pricing. The cheapest LLM for your app still depends on how many output tokens you generate.
How much does GPT-4.1 nano vs Qwen3.8-2.4T-A95B cost per million tokens?
GPT-4.1 nano OpenAI API pricing is $0.10 input and $0.40 output per million tokens via OpenAI. Qwen3.8-2.4T-A95B Qwen API pricing is $2.00 input and $6.00 output per million tokens via Qwen. 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 Qwen3.8-2.4T-A95B?
GPT-4.1 nano has a published LiveCodeBench score of 42.7%. Qwen3.8-2.4T-A95B does not have that eval on this page yet.
What is the context window for GPT-4.1 nano vs Qwen3.8-2.4T-A95B?
GPT-4.1 nano supports a 1M token context window, while Qwen3.8-2.4T-A95B supports 1M tokens. Qwen3.8-2.4T-A95B 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 Qwen3.8-2.4T-A95B?
Shared benchmark scores for GPT-4.1 nano and Qwen3.8-2.4T-A95B are still limited. Check the comparison table for the evals each model has published. 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 Qwen3.8-2.4T-A95B better for production use?
Both GPT-4.1 nano and Qwen3.8-2.4T-A95B 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 Qwen3.8-2.4T-A95B in my app?
Yes. Call GPT-4.1 nano through OpenAI and Qwen3.8-2.4T-A95B through Qwen 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 Qwen3.8-2.4T-A95B pricing data?
List rates are the latest published OpenAI API pricing and Qwen API pricing, quoted per million tokens. Official rate sheets: https://developers.openai.com/api/docs/pricing and https://www.alibabacloud.com/help/en/model-studio/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 Qwen3.8-2.4T-A95B?
GPT-4.1 nano supports up to 33K output tokens per request, while Qwen3.8-2.4T-A95B supports up to an unspecified number of 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 Qwen3.8-2.4T-A95B?
GPT-4.1 nano has a measured throughput of approximately 200 tokens/second. Throughput data for Qwen3.8-2.4T-A95B is not yet available. 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 Qwen3.8-2.4T-A95B 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 Qwen3.8-2.4T-A95B from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.
