Which is cheaper, Llama 3.1 405B Instruct or o1-preview?
As of August 25, 2026, Llama 3.1 405B Instruct is 98% cheaper on blended LLM API pricing. Llama 3.1 405B Instruct costs $0.89 per million input tokens and $0.89 per million output tokens ($1.78 blended 1M-in + 1M-out). o1-preview costs $15.00 / $60.00 per million tokens ($75.00 blended). Sources: https://llama.meta.com/ 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 Llama 3.1 405B Instruct vs o1-preview cost per million tokens?
Llama 3.1 405B Instruct Meta API pricing is $0.89 input and $0.89 output per million tokens via Meta. o1-preview OpenAI API pricing is $15.00 input and $60.00 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, Llama 3.1 405B Instruct or o1-preview?
o1-preview has a published SWE-bench Verified score of 41.3%. Llama 3.1 405B Instruct does not have that eval on this page yet.
What is the context window for Llama 3.1 405B Instruct vs o1-preview?
Llama 3.1 405B Instruct supports a 128K token context window, while o1-preview supports 128K tokens. Both models have the same context window size. 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, Llama 3.1 405B Instruct or o1-preview?
o1-preview leads on 4 of 5 shared benchmarks versus Llama 3.1 405B Instruct's 1 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 Llama 3.1 405B Instruct or o1-preview better for production use?
Both Llama 3.1 405B Instruct and o1-preview are production API models. For cost-sensitive production traffic, Llama 3.1 405B Instruct 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 Llama 3.1 405B Instruct and o1-preview in my app?
Yes. Call Llama 3.1 405B Instruct through Meta and o1-preview 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 Llama 3.1 405B Instruct vs o1-preview pricing data?
List rates are the latest published Meta API pricing and OpenAI API pricing, quoted per million tokens. Official rate sheets: https://llama.meta.com/ 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 Llama 3.1 405B Instruct and o1-preview?
Llama 3.1 405B Instruct supports up to 128K output tokens per request, while o1-preview 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, Llama 3.1 405B Instruct or o1-preview?
Llama 3.1 405B Instruct runs at approximately 42 tokens/second while o1-preview runs at 66 tokens/second. o1-preview 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 Llama 3.1 405B Instruct vs o1-preview comparison?
Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open Llama 3.1 405B Instruct or o1-preview from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.
