OpenAI to Claude Migration Calculator

Calculate exactly what you'd save (or lose) switching from GPT-5.5, GPT-5.4, or any OpenAI model to Claude Sonnet 4.6, Claude Opus 4.7, or Claude Haiku 4.5. Live pricing, real API differences, and an honest ROI breakdown — not vendor marketing.

OpenAI → Claude is the most-asked migration in 2026. Both vendors ship frontier models at comparable benchmark scores, both expose stable JSON-mode APIs, and both honor batch discounts. The differences that decide whether migration is worth your engineering hours come down to three things: cache pricing, prompt portability, and tool-use semantics. The calculator below handles the cost math; the sections after it cover everything else.

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The four cost dimensions that don't show up in a per-token comparison

A flat $/1M tokens lookup misses where the real money lives. Use the dimensions below as a sanity check on what the calculator shows.

1. Anthropic's prompt caching is aggressive

Claude's prompt caching gives you a 10× discount on cached input tokens (1-hour TTL standard, 5-minute on-demand). If your workload re-sends a large system prompt or document context on every turn — RAG agents, long-running chat sessions, document Q&A — Claude's effective input price can drop below $0.30/1M for cached portions. OpenAI's cache discount is more modest (50% off on automatic cache hits at the same $5/1M). For prompt-heavy workloads, factor a 30-50% effective price reduction on the Claude side before comparing.

2. Tool-use overhead differs by 15-30%

Claude's tool-use format returns slightly more verbose JSON than OpenAI's function-calling. In production tests, the same agent loop using identical tools generates ~20% more output tokens on Claude than on OpenAI when measured end-to-end. This narrows the migration gap on agent-heavy workloads. The calculator assumes a clean 50/50 input/output ratio by default — drag the slider toward output-heavy if you're shipping agents.

3. Batch API caps and discount tiers

Both OpenAI and Anthropic offer a ~50% batch discount with 24-hour async processing. OpenAI's batch queue tolerates higher concurrent throughput; Anthropic enforces stricter per-account caps but offers a 10,000-token minimum that's friendlier for short-context jobs. If you're running embarrassingly parallel evals or PDF-to-structured-data extractions, the savings are real on either side.

4. Context window pricing cliffs

GPT-5.5 prices a flat $5/$30 up to 272K context, then jumps. Claude Sonnet 4.6 and Opus 4.7 price flat to 200K context. If your prompts routinely exceed 200K tokens, factor the cliff into the math — the calculator above uses the flat rate; for cliff-exposed workloads, the savings figure is conservative.

What actually breaks when you switch from OpenAI to Anthropic

Things that port cleanly:

  • Plain text chat completions, streaming and non-streaming
  • System prompts (Anthropic uses a dedicated system parameter; OpenAI uses a system message — trivially convertible)
  • JSON mode output (both support it, with comparable reliability)
  • Vision inputs (both accept base64 or URL image references)

Things that need rewriting:

  • Function calling → Tool use: The schemas are near-identical but the response envelope differs. Expect 2-4 hours per agent surface to port.
  • Logprobs: OpenAI exposes logprobs on completions. Anthropic does not. If you depend on token probabilities (eval scoring, classifier confidence), migration is gated on this.
  • Fine-tuning: OpenAI's fine-tuning produces a custom model checkpoint. Anthropic offers no equivalent — you'd lose the fine-tune entirely. Heavy lift to replace with prompt engineering or RAG.
  • Assistants API: If you've built on OpenAI's Assistants framework (threads, files, code interpreter), there's no Anthropic-side analog. You'd rebuild on the Messages API + a vector store.

The honest call on when migration is worth it

If the calculator shows annual savings under $5,000, your engineering hours are likely worth more than the migration. If it shows $15,000+ and you don't depend on logprobs, fine-tunes, or Assistants, the case is straightforward. The middle band ($5,000–$15,000) is where the soft factors matter: who's debugging at 3am, how much prompt drift you'll inherit, and whether your team has Claude experience.

If you want the full architecture map and migration timeline for your specific stack — built from your actual workload, not a generic spreadsheet — get the $39 Migration Audit. 47-second turnaround, Gemini 3.1 Pro analyzing your usage, delivered as a PDF blueprint.

Pricing data is live from YemHub's model registry, refreshed continuously. Last verified: May 29, 2026.