Morph V3 Large applies code edit suggestions to source files with about 98% merge accuracy on complex multi-scope edits. It supports 81.9K tokens input and 16.4K tokens output. On AI Gateway, pay $0.9 per million input tokens and $1.9 per million output tokens.
import { streamText } from 'ai'
const result = streamText({ model: 'morph/morph-v3-large', prompt: 'Why is the sky blue?'})Morph V3 Large is the accuracy-optimized half of the Fast Apply pair. Both models share the same core concept: a model trained to merge AI-generated edit snippets into source files. v3 Large allocates more capacity to difficult cases. Morph's benchmarks cite about 98% merge accuracy on this task versus general-purpose code models they tested.
Versus v3 Fast, v3 Large runs at roughly half the speed and costs $0.9 per million input tokens and $1.9 per million output tokens on this page. v3 Fast lists lower per-token rates on AI Gateway; open that model page to compare. For simple single-scope edits, the extra cost buys little because Fast already handles them. For edits that cause merge failures (multi-scope changes, overlapping regions, refactors that redistribute code across functions, files with highly repetitive structures), the added accuracy reduces broken builds, failed tests, and rework.
Morph also ships an auto model that routes between Fast and Large from detected edit complexity. If you don't want to classify edits yourself, start with auto. Pick v3 Large when you know edits are complex, when v3 Fast failed on specific patterns, or when a failed merge is costlier than the extra per-token spend. See https://morphllm.com/ and https://morphllm.com/blog/what-is-morph-for for product context.