Morph V3 Large
4979°
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Morph | 262,144 token context | $0.90/M input, $1.90/M output. 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} {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 Large is a specialist apply model from Morph, built for one job: taking an instruction, existing code, and an edit snippet and producing a precise transformation. It claims roughly 4,500 tokens per second at 98% accuracy, which makes it fast enough to sit inside an agentic coding pipeline without becoming the bottleneck. If you need a general-purpose chat or reasoning model, look elsewhere.
Known for
- High-speed code transformations at roughly 4,500 tokens per second
- 98% accuracy on precise, instruction-driven code edits
- 262,144-token context window handles large codebases comfortably
- Zero data retention policy — your code does not train the model
- Structured prompt format keeps edits deterministic and pipeline-friendly
Watch out for
- Requires a strict three-part prompt format: instruction, initial code, edit snippet — freeform prompts will not work well.
- Closed weights, API-only; you cannot run this locally or inspect the model.
- Narrow specialisation means it is not a substitute for a general coding assistant.
Prompts to try
Refactor a function
Refactor the function below to use early returns instead of nested conditionals.
def process_order(order):
if order is not None:
if order['status'] == 'pending':
if order['items']:
return finalize(order)
return None
def process_order(order):
Add error handling
Add try/except error handling to the database call below, logging any exception and returning None on failure.
import logging
def fetch_user(db, user_id):
result = db.query('SELECT * FROM users WHERE id = ?', user_id)
return result
def fetch_user(db, user_id):
Convert to async
Convert the function below from synchronous to async/await, updating the HTTP call to use aiohttp.
import requests
def get_weather(city):
response = requests.get(f'https://api.weather.example.com/current?city={city}')
return response.json()
def get_weather(city):
Compare with
- Morph V3 FastAPI-only model81,920 token context · $0.80/M input, $1.20/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
- Hy4 previewOpen-weights model1,048,576 token context · $0.83/M input, $2.50/M output
More text models from morph
Questions people ask
- Can I run Morph V3 Large locally?
- No. Morph has not released open weights, and there is no Hugging Face repository. It is API-only.
- What does it cost and how large is the context window?
- Morph charges $0.90 per million input tokens and $1.90 per million output tokens, with a 262,144-token context window.
- How is this different from a general coding assistant like Claude or GPT?
- Morph V3 Large is not a chat model — it is an apply model designed specifically to execute code edits given a structured instruction, original code, and a diff snippet, optimised for speed and precision in that narrow task.
Captain's verdictA purpose-built code-edit engine: fast, accurate and narrow — useful inside a pipeline, not as a standalone assistant.
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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How this score adds up
This model has no popularity data from any source yet, so it carries a stand-in number below 7,000° that keeps the lists complete. It never ranks above a model with data. The breakdown appears here as soon as a source reports it.
Glossary: the terms used here
- Heat score
- One number for how much a model is used and wanted right now, from 0° to 9,999°. Every list on this site that says hottest is sorted by it and nothing else.
- Floor
- The 7,000° every model with real data starts from. The scale runs from 0° to 9,999°; the part below the floor is reserved for stand-ins (models without data), so that a model with data always ranks above one without. The floor is the same for everyone and decides nothing; the degrees above it are the measurement.
- Download-equivalents
- Everything the score treats as a download, added up: downloads themselves, each torrent seeder as 1,000, each AI Horde image and each run on a generation site as one.
- Log scale
- Counting by tens, not by ones: going from 1,000 to 10,000 downloads adds the same degrees as going from 10,000 to 100,000. It keeps giants from crowding out everything else.
- Likes
- Thumbs up and likes from every source, worth half a download each on the log scale.
- Adoption rate
- How many downloads a day a model gets right now, from the difference between two readings of a source. A quarter of the weight of the download term.
- Measured and estimated
- Measured means two readings of a source exist and the difference is real. Estimated means only today's total is known and the past is spread over the model's life; the site says which one it is showing.
- Seeder
- Someone who keeps a copy of the weights online for others to download over a torrent, or a hosted mirror doing the same. Each one counts as 1,000 downloads.
- AI Horde
- A volunteer network that generates images for free on donated computers. The number of images it has made with a model is public, and each counts as one download.
- Generation sites
- Sites that host their own copies of a model and let people generate with it, such as PixAI, SeaArt and Yodayo. Their downloads count like Civitai's and each generation run as one download.
- OpenRouter tokens
- A text model served through an API, such as Claude or GPT, has no weights to download, so its usage is measured in tokens: the pieces of text it read and wrote through OpenRouter, a gateway many apps use, over the last 30 days. Every 10,000,000 tokens count as one download. Source: OpenRouter (openrouter.ai/rankings), CC BY 4.0.
- Full model (checkpoint)
- The weights themselves, the file that generates on its own. It gets a bonus because add-ons depend on it.
- LoRA and embedding
- Small add-on files that steer someone else's weights toward a style, a character or a concept. They cannot run alone.
- Family and weights
- The base a model is built on, such as SD 1.5, SDXL, Flux or Wan. Every model inherits the release date of its family's weights.
- Age
- Degrees taken off for how long ago the family's weights came out: 150° a year, up to 750°. It keeps old downloads that had years to pile up from freezing the top of the list.
- Family boost
- A small, bounded nudge for LoRAs of a family this site is pushing forward right now, always listed in the breakdown.
- Stand-in score
- A stable number below the floor given to a model no source reports on yet, so lists stay complete. It never ranks above a model with data.
- New! (grace period)
- A model less than 7 days old shows New! instead of a score and holds no place in the rankings. Its score is computed from day one, but a model one day old grows faster than anything, so without the wait the week's #1 would always be whatever came out yesterday rather than what held up for a week.
- Trend
- How a model's growth over a period compares with the typical model's growth over the same period. Faster than typical reads as a rise, slower as a fall.
- Typical model
- The middle of the catalog: half the models with a figure grew faster over the period, half slower.
Morph V3 Large 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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