Morph V3 Fast
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Morph | 81,920 token context | $0.80/M input, $1.20/M output. Morph's fastest apply model for code edits. ~10,500 tokens/sec with 96% accuracy for rapid code transformations. The model requires the prompt to be in the following format: {instruction} {initial_code} {edit_snippet} Zero Data Retention is enabled for Morph. Learn more about this model in their documentationOpens in new tab
Guides for this model
Open weights vs open source vs API-only: what you are actually allowed to do
The 10 cheapest ChatGPT alternatives in 2026: free local models, free tiers and flat-rate plans
Captain's notes
Notes from 2026-10-13
Morph V3 Fast is a specialist apply model from Morph built for one job: taking an instruction, an existing file, and an edit snippet, then returning the transformed code as fast as possible. At roughly 10,500 tokens per second it is among the quickest options for code-edit pipelines, and Morph publishes a 96% accuracy figure for those transformations. It suits developers building agentic coding tools or diff-apply layers who need throughput over general-purpose reasoning.
Known for
- ~10,500 tokens/sec throughput, purpose-built for high-speed code transformations
- 96% accuracy on code edits, per Morph's own published figure
- Structured prompt format: instruction + initial code + edit snippet
- Zero data retention enabled by default
- Low token price for a specialised coding apply model
Watch out for
- Not a general-purpose chat or reasoning model; it expects a strict three-part prompt format or results suffer.
- Closed weights, API-only — no local deployment option.
- Context window is 81,920 tokens; large codebases may need chunking.
Best for
Prompts to try
Refactor a function
Instruction: Refactor the following function to use async/await instead of Promise chains and add JSDoc comments.
Initial code:
function fetchUser(id) {
return fetch('/api/users/' + id)
.then(res => res.json())
.then(data => data.user)
.catch(err => console.error(err));
}
Edit snippet: convert to async/await with JSDoc
Fix a bug in-place
Instruction: Fix the off-by-one error in the loop so it does not skip the last element.
Initial code:
def sum_list(items):
total = 0
for i in range(len(items) - 1):
total += items[i]
return total
Edit snippet: correct loop range to include final element
Add error handling
Instruction: Add try/catch error handling to the database call and throw a typed DatabaseError if the query fails.
Initial code:
async function getRecord(db, id) {
const row = await db.query('SELECT * FROM records WHERE id = ?', [id]);
return row[0];
}
Edit snippet: wrap query in try/catch, throw DatabaseError on failure
Compare with
- Morph V3 LargeAPI-only model262,144 token context · $0.90/M input, $1.90/M output
- gpt-oss-20bOpen-weights model131,072 token context · $0.02/M input, $0.09/M output
- DeepSeek V4.1 FlashOpen-weights model1,048,576 token context · $0.04/M input, $0.68/M output
- GLM 5.3 FlashOpen-weights model1,048,576 token context · $0.02/M input, $0.72/M output
More text models from morph
Questions people ask
- Can I run Morph V3 Fast locally?
- No — the weights are not public and there is no Hugging Face repository. It is available via Morph's API only.
- What does it cost and how large is the context?
- Pricing is $0.80 per million input tokens and $1.20 per million output tokens, with an 81,920-token context window.
- How is this different from a general coding model like GPT or Claude?
- Morph V3 Fast is not a chat or reasoning model — it is an apply model that expects a fixed prompt structure (instruction, initial code, edit snippet) and is optimised purely for fast, accurate code transformations rather than open-ended generation.
Captain's verdictIf you are building a coding agent and need a dedicated, fast apply layer rather than another general model, this is the obvious first stop.
Captain writes these notes from the model's own description and model card. Prices and context sizes are the provider's at the time of writing and can change.
Ranking badge
Updated Oct 22, 2026
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Morph V3 Fast is a text model for the Text / LLM family listed on ModelTrends.ai, a read-only catalog of open source AI image, video and text models. Compare it with other Text / LLM models and check its heat score to see how it is trending.
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