Which is cheaper, K-EXAONE-236B-A23B or Phi-4-multimodal-instruct?
As of August 21, 2026, Phi-4-multimodal-instruct is 91% cheaper on blended LLM API pricing. Phi-4-multimodal-instruct costs $0.05 per million input tokens and $0.10 per million output tokens ($0.15 blended 1M-in + 1M-out). K-EXAONE-236B-A23B costs $0.60 / $1.00 per million tokens ($1.60 blended). Sources: https://anotherwrapper.com/tools/llm-pricing and https://azure.microsoft.com/en-us/pricing/details/cognitive-services/openai-service/. The cheapest LLM for your app still depends on how many output tokens you generate.
How much does K-EXAONE-236B-A23B vs Phi-4-multimodal-instruct 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. Phi-4-multimodal-instruct Microsoft API pricing is $0.05 input and $0.10 output per million tokens via Microsoft. 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 Phi-4-multimodal-instruct?
Neither K-EXAONE-236B-A23B nor Phi-4-multimodal-instruct has a published SWE-bench or LiveCodeBench score on this page yet. Use the comparison table for the evals that do exist.
What is the context window for K-EXAONE-236B-A23B vs Phi-4-multimodal-instruct?
K-EXAONE-236B-A23B supports a 33K token context window, while Phi-4-multimodal-instruct supports 128K tokens. Phi-4-multimodal-instruct 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 Phi-4-multimodal-instruct?
Shared benchmark scores for K-EXAONE-236B-A23B and Phi-4-multimodal-instruct 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 K-EXAONE-236B-A23B or Phi-4-multimodal-instruct better for production use?
Both K-EXAONE-236B-A23B and Phi-4-multimodal-instruct are production API models. For cost-sensitive production traffic, Phi-4-multimodal-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 K-EXAONE-236B-A23B and Phi-4-multimodal-instruct in my app?
Yes. Call K-EXAONE-236B-A23B through LG AI Research and Phi-4-multimodal-instruct through Microsoft 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 Phi-4-multimodal-instruct pricing data?
List rates are the latest published LG AI Research API pricing and Microsoft API pricing, quoted per million tokens. Official rate sheets: https://anotherwrapper.com/tools/llm-pricing and https://azure.microsoft.com/en-us/pricing/details/cognitive-services/openai-service/. 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 Phi-4-multimodal-instruct?
K-EXAONE-236B-A23B supports up to 33K output tokens per request, while Phi-4-multimodal-instruct 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, K-EXAONE-236B-A23B or Phi-4-multimodal-instruct?
K-EXAONE-236B-A23B runs at approximately 50 tokens/second while Phi-4-multimodal-instruct runs at 25 tokens/second. K-EXAONE-236B-A23B 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 Phi-4-multimodal-instruct 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 Phi-4-multimodal-instruct from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.
