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Favicon for morph

Morph: Morph V3 Fast (batch)

morph/morph-v3-fast:batch

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Morph V3 Fast (batch) is served by 1 provider.

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>{instruction}</instruction> <code>{initial_code}</code> <update>{edit_snippet}</update>

Zero Data Retention is enabled for Morph. Learn more about this model in their documentationOpens in new tab

Modalities
In / out price
$0.40 / $0.60per 1M
Context
82K
Released
Jul 7, 2025
Compare
ProvidersPricingPerformanceUptimeAppsActivityFAQExplore

Providers

This model is hosted by one provider. OpenRouter forwards every request to it directly — no routing decisions to make.

Pricing

The average price customers actually pay for this model, next to the prices providers post. Caching and discounts mean the price actually paid is often well below the listed one.

Performance

Throughput is how fast the model writes (tokens per second — higher is better). Latency is total round-trip time (lower is better). TTFT is time-to-first-token — how long before you see anything appear (lower is better).

Uptime

Uptime is the percentage of the past 3 days that at least one provider was responding to requests. Availability is the percentage of time that inference was successfully served. OpenRouter continuously monitors and uses the next-best provider when one returns an error.

Apps

Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for.

Activity

Token volume and request traffic to this model over time.

API

Drop-in code to call this model. OpenRouter's API is OpenAI-compatible — most SDKs work by just swapping the base URL. The only thing that changes between models is the model slug below.

Not currently available through EU in-region routing. In-region routing docs

Explore more models

AI Model RankingsRanking
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ProviderInput / Output /M
ThroughputUptime
  • Favicon for Morph
    Input $0.40 / Output $0.60
    Throughput --Uptime --

Not enough uptime data to display yet.

When an error occurs in an upstream provider, we can recover by routing to another healthy provider, if your request filters allow it. You can access per-provider uptime data programmatically through the Endpoints API. Learn more about our load balancing and provider routing options.

Not enough data for apps using this model yet.

Frequently asked questions

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>{instruction}</instruction> <code>{initial_code}</code> <update>{edit_snippet}</update> Zero Data Retention is enabled for Morph. Learn more about this model in their documentation

Morph V3 Fast (batch) costs $0.40/M input tokens and $0.60/M output tokens.

Morph V3 Fast (batch) has a 81,920 token context window. It supports up to 38,000 completion tokens.

No. Morph V3 Fast (batch) does not accept tools, so function calling is unavailable. It does not support response_format, so JSON output is not enforced.

Reflex and Morph V3 Large are other text models from Morph.

Morph V3 Fast (batch) was released on July 7, 2025.

More models from morph

Reflex

Morph's decision model. Answers typed choice, noul and score questions about a state. The state is prefix-cached across requests, so repeat questions on the same state bill at the cached-input rate. 65k context window.

Decisions$0.042 / $0
Morph V3 Large

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> <update>{edit_snippet}</update>

Zero Data Retention is enabled for Morph. Learn more about this model in their documentation

Text262K context$0.90 / $1.90
Morph V3 Large (batch)

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> <update>{edit_snippet}</update>

Zero Data Retention is enabled for Morph. Learn more about this model in their documentation

Text262K context$0.45 / $0.95
Morph V3 Fast

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>{instruction}</instruction> <code>{initial_code}</code> <update>{edit_snippet}</update>

Zero Data Retention is enabled for Morph. Learn more about this model in their documentation

Text262K context$0.80 / $1.20
Morph V3 Fast (batch)

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>{instruction}</instruction> <code>{initial_code}</code> <update>{edit_snippet}</update>

Zero Data Retention is enabled for Morph. Learn more about this model in their documentation

Text82K context$0.40 / $0.60
Fast Apply

Morph Apply is a specialized code-patching LLM that merges AI-suggested edits straight into your source files. It can apply updates from GPT-4o, Claude, and others into your files at 4000+ tokens per second.

The model requires the prompt to be in the following format: <code>${originalCode}</code>\n<update>${updateSnippet}</update>

Learn more about this model in their documentation

Text32K context

Morph Apply is a specialized code-patching LLM that merges AI-suggested edits straight into your source files. It can apply updates from GPT-4o, Claude, and others into your files at 4000+ tokens per second.

The model requires the prompt to be in the following format: <code>${originalCode}</code>\n<update>${updateSnippet}</update>

Learn more about this model in their documentation