@akshay_pachaar: 50% cheaper Claude inference with just one line of code change! - Remove → model="claude-opus-4-8" - Add → model="ship-…

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Summary

Ship announces an endpoint that guarantees 50% cheaper Claude inference by dynamically selecting the cheapest execution path that preserves the reference model's quality, shifting cost variance away from the application.

50% cheaper Claude inference with just one line of code change! - Remove → model="claude-opus-4-8" - Add → model="ship-like/claude-opus-4-8" I verified the cost saving in my own terminal by invoking the same Anthropic model with the same prompt. The underlying engineering by Ship is actually interesting, and the patterns can be used in any production LLM stack. Essentially, a trained model is a frozen artifact. Every request performs the same forward-pass, whether it extracts a date or refactors a module, because the compute decision was made at training time, before the request existed. Ship makes that decision at inference time instead. After seeing a request, it searches over executions, involving single models, cascades, ensembles, or harnesses with tools, and serves the cheapest one that will match the reference model's quality. This is not a basic router, because picking a cheaper model per query doesn't ensure the cheaper model preserves the original's behavior, like output shape, tool-call patterns, and refusals. Ship measures this equivalence directly. Outputs stay distributionally indistinguishable from the reference model, not token-identical, since two calls to the same model already differ, but they are indistinguishable in capability and behavior. Of course, some requests execute cheaply and some cost Ship more than the customer pays, but the price per request is still a flat 50% off either way, so the execution-cost variance moves off the application's bill entirely. The video below depicts the cost savings and output in my real invocation, and I partnered with the team to put this together.
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Cached at: 07/22/26, 04:22 AM

50% cheaper Claude inference with just one line of code change!

  • Remove → model=“claude-opus-4-8”
  • Add → model=“ship-like/claude-opus-4-8”

I verified the cost saving in my own terminal by invoking the same Anthropic model with the same prompt.

The underlying engineering by Ship is actually interesting, and the patterns can be used in any production LLM stack.

Essentially, a trained model is a frozen artifact.

Every request performs the same forward-pass, whether it extracts a date or refactors a module, because the compute decision was made at training time, before the request existed.

Ship makes that decision at inference time instead.

After seeing a request, it searches over executions, involving single models, cascades, ensembles, or harnesses with tools, and serves the cheapest one that will match the reference model’s quality.

This is not a basic router, because picking a cheaper model per query doesn’t ensure the cheaper model preserves the original’s behavior, like output shape, tool-call patterns, and refusals.

Ship measures this equivalence directly.

Outputs stay distributionally indistinguishable from the reference model, not token-identical, since two calls to the same model already differ, but they are indistinguishable in capability and behavior.

Of course, some requests execute cheaply and some cost Ship more than the customer pays, but the price per request is still a flat 50% off either way, so the execution-cost variance moves off the application’s bill entirely.

The video below depicts the cost savings and output in my real invocation, and I partnered with the team to put this together.

Martian (@withmartian): Announcing Ship: an endpoint with the highest intelligence per dollar of any frontier model.

Today, Ship makes using Opus and GPT 5.6 Sol 50% cheaper guaranteed by a quality SLA.

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