Why high: an exact fingerprint shared with 2 models that declare the same family, without a second signal of a different kind yet.
Morph: Morph V3 Large is Morph's model. Its measured tokenizer sits with the Qwen group, which is evidence that it builds on the Qwen tokenizer. Tokenizer reuse is normal engineering, open vocabularies travel between labs, and this says nothing against Morph's authorship of the model itself.
| We checked | What we found | Where it came from |
|---|---|---|
| How it counts tokens | Counts every one of the 50 test strings exactly like 2 other models | tk_c5c0a8ed |
| How its API is set up | A combination of settings no other listing in the catalogue uses | api_2d7f9940 |
| How much it can read and write | Reads up to 262,144 tokens at once, writes up to 131,072 | its own listing |
| Who served our requests | Morph | we measured it, 3 September 2026 |
Click a row to open the full comparison.
We sent Morph: Morph V3 Large fifty short pieces of text and recorded what each one cost it in tokens. The bars below are those costs. Two models built on the same tokenizer produce the same bars; a model built on a different one produces a different set, which is what makes this a fingerprint.
Overhead subtracted: 96 prompt tokens.
Morph's high-accuracy apply model for complex code edits. ~4,500 tokens/sec with 98% accuracy for precise code transformations. The model requires the prompt to be in the following format: <instruction>{instruction}</instruction> <code>{initial_code}</code>...
Supported parameters
logprobsmax_tokensresponse_formatstopstructured_outputstemperaturetop_logprobs
defaults: {"temperature":null,"top_p":null,"frequency_penalty":null}
