AI Model Comparisons

Pricing changes frequently so check Anthropic's current pricing page for exact numbers, but as of early 2026, Claude Haiku 3.5 is approximately 5–8× cheaper per million tokens than Claude Sonnet for both input and output tokens. For applications processing millions of tokens monthly, this cost difference is significant — a workload costing $1,000/month on Sonnet would cost $125–$200 on Haiku 3.5 for the same volume. The actual savings in a mixed routing strategy depend on the proportion of requests routed to each model.

Yes — both Claude Haiku 3.5 and Claude Sonnet support 200K token context windows, enabling both models to process documents of equivalent length. Context window size is not a meaningful differentiator between these models. The quality difference on long-context tasks is more relevant: for very long documents requiring complex inference across the full context (legal contract analysis, technical specification review), Sonnet's stronger reasoning capabilities matter more than for short-context tasks even though both models technically support the full context length.

Claude Haiku 3.5 supports tool use (function calling) and performs well for tool use in straightforward single-tool scenarios — calling a specific tool based on clear user intent, extracting structured data using a well-defined schema. Its performance declines relative to Sonnet for complex multi-step agentic workflows that require selecting between multiple tools based on ambiguous context, chaining tool calls with conditional logic, and recovering from tool errors through alternate approaches. For agentic applications, benchmark the specific tool use patterns you need before defaulting to Haiku 3.5 — the routing decision is more nuanced for agentic versus simple API call use cases.

Configure failover policies in your model gateway that escalate to Sonnet when Haiku 3.5 is unavailable (acceptable quality degradation direction) and optionally queue requests when Sonnet is unavailable for tasks that absolutely require Sonnet quality. Avoid silent model substitution that swaps Sonnet for Haiku 3.5 during Sonnet unavailability — if your routing logic selected Sonnet for quality reasons, downgrading silently may produce visibly inferior outputs that damage user trust. Make failover behaviour explicit in your routing configuration and test failover scenarios during load testing rather than discovering them in production incidents.

Both Claude Haiku 3.5 and Claude Sonnet support vision — processing images as part of prompts. As with text tasks, there is a quality gap on complex vision tasks (detailed visual analysis, understanding complex diagrams, OCR of degraded documents) where Sonnet performs noticeably better. For high-volume vision tasks with moderate complexity — classifying product images, extracting structured data from clearly formatted tables, identifying objects in standard photographs — Haiku 3.5 provides adequate quality at substantially lower cost. The same benchmark-your-specific-task approach applies to vision as to text routing decisions.

Time-to-first-token for Claude Haiku 3.5 is typically 2–3× lower than Sonnet for equivalent prompt lengths, with Haiku 3.5 often returning first tokens in under 500ms for short prompts versus 1–2 seconds for Sonnet. Output generation speed (tokens per second) is similarly faster for Haiku 3.5. These differences are significant for user-facing chat interfaces where perceived responsiveness affects satisfaction, and relatively unimportant for background batch processing where jobs complete in seconds regardless of model speed. For real-time voice or multimodal applications, Haiku 3.5's lower latency is often a hard requirement rather than a cost consideration.

Define your quality acceptance criteria before testing — what does "sufficient quality" mean for this task? For classification, it might be accuracy above 92%. For extraction, it might be field accuracy above 95%. For generation, it might require human raters to rate outputs as "acceptable" more than 85% of the time. Then test both models on representative task samples, measure against your criteria, and make the routing decision based on whether Haiku 3.5 meets the threshold. This structured approach prevents both over-paying for Sonnet where Haiku 3.5 suffices and under-investing in quality where the task consequence justifies Sonnet's premium.

Yes — there is no technical restriction on using different Claude models for different turns within a user session. A common pattern is using Haiku 3.5 for intent classification and simple responses, then switching to Sonnet for turns that require complex reasoning or generation while maintaining conversation history as the messages array passed to both model calls. The user experience is seamless since both models share the same API interface and response format. Document this routing behaviour internally so your team understands why some turns in session logs use different model identifiers, and ensure conversation history is correctly threaded through routing logic regardless of which model handled previous turns.