Claude Model Picker

How the Model Picker Works

The Claude Model Picker is an interactive wizard that guides you through four decision factors to recommend the best Claude model for your specific use case. Rather than reading through comparison tables and trying to map features to your requirements, the wizard asks targeted questions about what you are building and produces a personalized recommendation with clear reasoning.

The four factors are: primary use case (coding, analysis, creative, or general), speed requirements (fast, balanced, or thorough), budget (low, medium, or high), and context length needs (short, medium, or long). Each combination produces a weighted score across the three available Claude models. The recommendation is not a simple lookup table but a scored calculation that considers how each factor interacts with model capabilities.

Understanding the Three Claude Models

Claude Opus 4 is the flagship model designed for the most demanding tasks. It excels at complex multi-step reasoning, research synthesis, nuanced analysis, and agentic workflows where the model needs to make decisions and take actions autonomously. Opus is the best choice when the quality and depth of the response is the top priority and you are willing to pay a premium for it. Typical use cases include: architectural design decisions, legal document analysis, scientific research review, complex debugging of multi-file codebases, and strategic planning tasks. The model ID is claude-opus-4-20250514.

Claude Sonnet 4 is the general-purpose workhorse. It balances capability, speed, and cost in a way that makes it the default choice for most production applications. Sonnet handles code generation, content creation, document summarization, conversational AI, and data extraction with high quality and reasonable latency. It is noticeably faster than Opus while retaining strong reasoning capabilities. For the majority of API integrations, Sonnet is the correct starting point. You should only switch to Opus if you find Sonnet's quality insufficient for your specific task, or to Haiku if Sonnet's latency or cost is too high for your volume. The model ID is claude-sonnet-4-20250514. To experiment with Sonnet in an interactive environment, try the ClaudKit API playground.

Claude Haiku 3.5 is the speed and cost champion. It delivers responses with the lowest latency and the lowest per-token cost of any Claude model. Haiku is the right choice for high-volume applications where you are making hundreds or thousands of API calls per minute and need fast, affordable responses. Typical use cases include: text classification, named entity extraction, short-form Q&A, real-time chat, content moderation, and any task where a concise, correct answer matters more than nuanced reasoning. The model ID is claude-haiku-3.5-20241022.

Cost Analysis Deep Dive

The cost difference between models is substantial and scales linearly with usage. At 100 requests per day with a typical 500-token input and 1,000-token output, monthly costs are approximately $13 for Haiku, $50 for Sonnet, and $248 for Opus. At 1,000 requests per day, those numbers become $132, $495, and $2,475. For high-volume applications, the model choice is often the single biggest factor in your API budget.

A common pattern is to use different models for different tasks within the same application. For example, a coding assistant might use Haiku for autocompletion suggestions (fast, cheap, high volume), Sonnet for code review and generation (balanced quality and speed), and Opus for architectural analysis of entire codebases (maximum quality, lower volume). The Claude API makes this straightforward because all models share the same request format, so switching is as simple as changing the model parameter string. For building these kinds of multi-model workflows visually, ClaudFlow provides a workflow designer that routes requests to different models based on task type.

Prompt caching can significantly reduce costs for any model. When you send the same system prompt repeatedly (which is common in production), Anthropic caches the tokenized version and charges reduced rates for cached input tokens. This is especially valuable for applications with long system prompts that include detailed instructions, few-shot examples, or reference data. The savings compound with higher request volumes. For detailed cost projections that factor in caching, see the ClaudKit API Request Builder.

Speed and Latency Characteristics

Latency has two components: time-to-first-token (TTFT) and tokens-per-second (TPS) throughput. Haiku leads on both metrics, with TTFT typically under 200 milliseconds and throughput exceeding 100 tokens per second. Sonnet sits in the middle with TTFT of 300 to 500 milliseconds and throughput of 50 to 80 tokens per second. Opus is the slowest with TTFT of 500 milliseconds to 2 seconds and throughput of 30 to 50 tokens per second, though these numbers vary with prompt complexity.

For user-facing applications, TTFT is usually more important than total generation time because streaming delivers tokens incrementally. A 500ms TTFT with streaming feels responsive, while a 3-second wait before any content appears feels sluggish. If your application uses streaming (and it should for user-facing features), Sonnet's TTFT is fast enough for most interactive applications. Haiku is necessary only when you need sub-200ms TTFT for real-time features like autocomplete or live suggestions. To see streaming in action and learn how to implement it, try the Streaming Responses Guide.

Context Length Considerations

All three Claude models support a 200,000-token context window, which is approximately 150,000 words or 500 pages of text. However, the models differ in how effectively they utilize long contexts. Opus has the strongest performance on long-context tasks, maintaining accuracy and recall even when relevant information is buried deep in a 200K-token prompt. Sonnet performs well up to about 100K tokens with graceful degradation beyond that. Haiku is best suited for shorter contexts under 20K tokens where it can leverage its speed advantage without sacrificing quality.

Cost is an important consideration for long-context requests. A 100K-token input on Opus costs $1.50 per request (input only), while the same input on Haiku costs $0.08. If you are processing many documents through long-context prompts, the model choice has a massive impact on total cost. Consider whether you can preprocess or chunk documents to use a cheaper model, or whether the task genuinely requires the full document in context. For formatting and processing JSON data before sending it to the API, Kappafy provides JSON formatting and validation tools.

Decision Framework for Teams

For teams evaluating Claude models for a new project, start with Sonnet. It provides the best balance of quality, speed, and cost for the widest range of tasks. Run your test suite or evaluation prompts against Sonnet first and establish a quality baseline. Then test the same prompts against Haiku: if the quality difference is negligible for your use case, switch to Haiku and save 80% on API costs. If Sonnet's quality is insufficient, test against Opus: if Opus produces meaningfully better results, the premium may be justified for your application.

This bottom-up evaluation approach prevents the common mistake of defaulting to the most expensive model and never testing cheaper alternatives. In many production applications, Haiku performs surprisingly well on tasks that developers assume require a more capable model. Classification, extraction, translation, and short-form Q&A are areas where Haiku typically matches Sonnet's quality while being dramatically faster and cheaper. For A/B testing different models in production, ABWex provides experimentation tools that can help you measure the quality-cost tradeoff empirically.