Which is cheaper, Claude 3.5 Sonnet or Phi 4?
As of August 20, 2026, Phi 4 is 99% cheaper on blended LLM API pricing. Phi 4 costs $0.07 per million input tokens and $0.14 per million output tokens ($0.21 blended 1M-in + 1M-out). Claude 3.5 Sonnet costs $3.00 / $15.00 per million tokens ($18.00 blended). Sources: https://platform.claude.com/docs/en/about-claude/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 Claude 3.5 Sonnet vs Phi 4 cost per million tokens?
Claude 3.5 Sonnet Claude API pricing from Anthropic is $3.00 input and $15.00 output per million tokens via Anthropic. Phi 4 Microsoft API pricing is $0.07 input and $0.14 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, Claude 3.5 Sonnet or Phi 4?
Claude 3.5 Sonnet has a published SWE-bench Verified score of 49%. Phi 4 does not have that eval on this page yet.
What is the context window for Claude 3.5 Sonnet vs Phi 4?
Claude 3.5 Sonnet supports a 200K token context window, while Phi 4 supports 16K tokens. Claude 3.5 Sonnet 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, Claude 3.5 Sonnet or Phi 4?
Claude 3.5 Sonnet leads on 6 of 9 shared benchmarks versus Phi 4'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 Claude 3.5 Sonnet or Phi 4 better for production use?
Both Claude 3.5 Sonnet and Phi 4 are production API models. For cost-sensitive production traffic, Phi 4 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 Claude 3.5 Sonnet and Phi 4 in my app?
Yes. Call Claude 3.5 Sonnet through Anthropic and Phi 4 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 Claude 3.5 Sonnet vs Phi 4 pricing data?
List rates are the latest published Claude API pricing from Anthropic and Microsoft API pricing, quoted per million tokens. Official rate sheets: https://platform.claude.com/docs/en/about-claude/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 Claude 3.5 Sonnet and Phi 4?
Claude 3.5 Sonnet supports up to 200K output tokens per request, while Phi 4 supports up to 16K 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, Claude 3.5 Sonnet or Phi 4?
Claude 3.5 Sonnet runs at approximately 78 tokens/second while Phi 4 runs at 33 tokens/second. Claude 3.5 Sonnet 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 Claude 3.5 Sonnet vs Phi 4 comparison?
Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open Claude 3.5 Sonnet or Phi 4 from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.
