Which is cheaper, DeepSeek-R1-0528 or GPT-5.6 Luna?
As of August 20, 2026, GPT-5.6 Luna is 49% cheaper on blended LLM API pricing. GPT-5.6 Luna costs $0.20 per million input tokens and $1.20 per million output tokens ($1.40 blended 1M-in + 1M-out). DeepSeek-R1-0528 costs $0.55 / $2.19 per million tokens ($2.74 blended). Sources: https://api-docs.deepseek.com/quick_start/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 DeepSeek-R1-0528 vs GPT-5.6 Luna cost per million tokens?
DeepSeek-R1-0528 DeepSeek API pricing is $0.55 input and $2.19 output per million tokens via DeepSeek. GPT-5.6 Luna OpenAI API pricing is $0.20 input and $1.20 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, DeepSeek-R1-0528 or GPT-5.6 Luna?
GPT-5.6 Luna leads on SWE-bench Verified: DeepSeek-R1-0528 at 44.6% vs GPT-5.6 Luna at 93%. SWE-bench measures real GitHub fixes; LiveCodeBench measures contest programming.
What is the context window for DeepSeek-R1-0528 vs GPT-5.6 Luna?
DeepSeek-R1-0528 supports a 131K token context window, while GPT-5.6 Luna supports 1M tokens. GPT-5.6 Luna 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, DeepSeek-R1-0528 or GPT-5.6 Luna?
GPT-5.6 Luna leads on 7 of 10 shared benchmarks versus DeepSeek-R1-0528's 3 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 DeepSeek-R1-0528 or GPT-5.6 Luna better for production use?
Both DeepSeek-R1-0528 and GPT-5.6 Luna are production API models. For cost-sensitive production traffic, GPT-5.6 Luna 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 DeepSeek-R1-0528 and GPT-5.6 Luna in my app?
Yes. Call DeepSeek-R1-0528 through DeepSeek and GPT-5.6 Luna 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 DeepSeek-R1-0528 vs GPT-5.6 Luna pricing data?
List rates are the latest published DeepSeek API pricing and OpenAI API pricing, quoted per million tokens. Official rate sheets: https://api-docs.deepseek.com/quick_start/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 DeepSeek-R1-0528 and GPT-5.6 Luna?
DeepSeek-R1-0528 supports up to 131K output tokens per request, while GPT-5.6 Luna 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, DeepSeek-R1-0528 or GPT-5.6 Luna?
DeepSeek-R1-0528 runs at approximately 9 tokens/second while GPT-5.6 Luna runs at 48.45216448188169 tokens/second. GPT-5.6 Luna 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 DeepSeek-R1-0528 vs GPT-5.6 Luna comparison?
Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open DeepSeek-R1-0528 or GPT-5.6 Luna from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.
