Morph V3 Large Batch
Neural Network
Morph V3 Large Batch is a Morph model for complex code edits: applies instructions to source code with 98% accuracy.
Specifications
- Input
- text, documents
- Output
- text
- Context size
- 262 144 tokens
- Max answer length
- 131 072 tokens
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works Morph V3 Large Batch?
Morph V3 Large Batch is a highly specialized model from Morph, designed not for free-form dialogue, but for applying edits to existing code: you provide an instruction and a source file, and receive code with precise changes. It claims 98% accuracy on complex transformations and a speed of about 4500 tokens per second, making it suitable for scenarios with many predictable edits. The request format is fixed: instructions and code are separated by tags, simplifying pipeline integration. Via BotHub, the model is accessed from the same interface as over 250 other models, allowing you to compare results with a general-purpose assistant without rewriting integration. No VPN or foreign cards required; pay in rubles based on usage. Tokens do not expire, and data is protected by AES-GCM encryption and not stored. Contracts, invoices, EDI, admin panels, and limits are available for teams. Typical tasks: mass refactoring, library migrations, automated code review fixes, CI linting steps, and batch file processing.Frequently asked questions about Morph V3 Large Batch
You can use generated results for commercial purposes. You own all rights to the content you create. The only restriction: make sure your prompt does not include copyrighted third-party material. You are responsible for respecting the rights to any input data.
This is a text model with a 262,144-token context that also accepts documents. Its strength is accurately applying prepared edits to existing code or text: merging a fragment with the source file. It is useful in pipelines for mass document processing, automated edits, and batch tasks where result predictability is important.
The model outputs up to 131,072 tokens per response, as stated in its card. The input context reaches 262,144 tokens, allowing a large file or collection of documents to fit into a single request along with the task. For long materials, this significantly reduces the number of separate requests.
A separate reasoning mode is not noted in our data: the model answers the essence of the request without a dedicated thought step. If a step-by-step chain of thought is important for your task, test the behavior on your scenario and, if necessary, switch to another model in BotHub — the integration remains the same.
Function calling and structured JSON output are not noted in our data, so do not rely on them as a standard mechanism. The response format is usually set by instructions in the prompt, and the need for strict JSON should be verified with a test request before going into production.
The model accepts text and documents as input, as stated in its card. Images, audio, and video are not noted in the data, so do not build them into your architecture without verification. For scans, audio recordings, and videos, choose a model with the appropriate modality in the same interface.
There is no quality assessment for Russian in our data, and the prompt language is not specified in the card. The practical approach is: run the model on your texts, formulate the task briefly and unambiguously, and compare the result with a benchmark — then the conclusion about suitability will be based on facts, not promises.