Which is cheaper, K-EXAONE-236B-A23B or o1-preview?
As of August 21, 2026, K-EXAONE-236B-A23B is 98% cheaper on blended LLM API pricing. K-EXAONE-236B-A23B costs $0.60 per million input tokens and $1.00 per million output tokens ($1.60 blended 1M-in + 1M-out). o1-preview costs $15.00 / $60.00 per million tokens ($75.00 blended). Sources: https://anotherwrapper.com/tools/llm-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 K-EXAONE-236B-A23B vs o1-preview cost per million tokens?
K-EXAONE-236B-A23B LG AI Research API pricing is $0.60 input and $1.00 output per million tokens via LG AI Research. 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, K-EXAONE-236B-A23B or o1-preview?
o1-preview has a published SWE-bench Verified score of 41.3%. K-EXAONE-236B-A23B does not have that eval on this page yet.
What is the context window for K-EXAONE-236B-A23B vs o1-preview?
K-EXAONE-236B-A23B supports a 33K token context window, while o1-preview supports 128K tokens. o1-preview 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, K-EXAONE-236B-A23B or o1-preview?
K-EXAONE-236B-A23B leads on 1 of 1 shared benchmarks versus o1-preview'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 K-EXAONE-236B-A23B or o1-preview better for production use?
Both K-EXAONE-236B-A23B and o1-preview are production API models. For cost-sensitive production traffic, K-EXAONE-236B-A23B 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 K-EXAONE-236B-A23B and o1-preview in my app?
Yes. Call K-EXAONE-236B-A23B through LG AI Research 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 K-EXAONE-236B-A23B vs o1-preview pricing data?
List rates are the latest published LG AI Research API pricing and OpenAI API pricing, quoted per million tokens. Official rate sheets: https://anotherwrapper.com/tools/llm-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 K-EXAONE-236B-A23B and o1-preview?
K-EXAONE-236B-A23B supports up to 33K 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, K-EXAONE-236B-A23B or o1-preview?
K-EXAONE-236B-A23B runs at approximately 50 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 K-EXAONE-236B-A23B 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 K-EXAONE-236B-A23B 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.
