Which is cheaper, LongCat-Flash-Thinking-2601 or Phi-4-multimodal-instruct?
As of August 21, 2026, Phi-4-multimodal-instruct is 90% 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). LongCat-Flash-Thinking-2601 costs $0.30 / $1.20 per million tokens ($1.50 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 LongCat-Flash-Thinking-2601 vs Phi-4-multimodal-instruct cost per million tokens?
LongCat-Flash-Thinking-2601 Meituan API pricing is $0.30 input and $1.20 output per million tokens via Meituan. 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, LongCat-Flash-Thinking-2601 or Phi-4-multimodal-instruct?
LongCat-Flash-Thinking-2601 has a published SWE-bench Verified score of 70%. Phi-4-multimodal-instruct does not have that eval on this page yet.
What is the context window for LongCat-Flash-Thinking-2601 vs Phi-4-multimodal-instruct?
LongCat-Flash-Thinking-2601 supports a 128K token context window, while Phi-4-multimodal-instruct 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, LongCat-Flash-Thinking-2601 or Phi-4-multimodal-instruct?
Shared benchmark scores for LongCat-Flash-Thinking-2601 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 LongCat-Flash-Thinking-2601 or Phi-4-multimodal-instruct better for production use?
Both LongCat-Flash-Thinking-2601 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 LongCat-Flash-Thinking-2601 and Phi-4-multimodal-instruct in my app?
Yes. Call LongCat-Flash-Thinking-2601 through Meituan 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 LongCat-Flash-Thinking-2601 vs Phi-4-multimodal-instruct pricing data?
List rates are the latest published Meituan 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 LongCat-Flash-Thinking-2601 and Phi-4-multimodal-instruct?
LongCat-Flash-Thinking-2601 supports up to 128K 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, LongCat-Flash-Thinking-2601 or Phi-4-multimodal-instruct?
LongCat-Flash-Thinking-2601 runs at approximately 100 tokens/second while Phi-4-multimodal-instruct runs at 25 tokens/second. LongCat-Flash-Thinking-2601 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 LongCat-Flash-Thinking-2601 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 LongCat-Flash-Thinking-2601 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.
