Relace Apply 3
Relace Apply 3 is a specialized code-patching language model from Relace that merges AI-suggested code edits directly into existing source files with very high throughput.
What is Relace Apply 3?
Relace Apply 3 is a code-focused language model that applies AI-generated diffs or edit snippets directly into source code files. It is mainly used to take suggestions from models like GPT-4o or Claude and reliably merge them into large codebases, and to support automated refactoring or patch application pipelines with up to a 256K-token context window. It also helps engineering teams build high-throughput code agents that can stream and apply changes at around 10,000 tokens per second while preserving formatting and structure. Apply 3 is part of Relace’s Fast Apply series of specialized code-editing models, succeeding earlier Relace Apply generations.
Providers
Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro (fastest), or Exacto (highest tool-calling accuracy).
List price by provider ($ / 1M tokens)
InputOutputProvider list prices; the LLM.API discount applies on top.
| Provider | Input | Output | Cache read /M | Latency | Throughput | Uptime |
|---|---|---|---|---|---|---|
| in $0.85/1M; out $1.25/1M | 256K tokens | streaming |
Prices, context and availability from the OpenRouter public catalogue (this model is not served through LLM.API), updated nightly. Last updated 18 Sept 2026.
Try this model
Test Relace Apply 3 right here — free to start.
Suggestions for your first prompt
Code snippet
Call the model through the OpenAI-compatible API.
from openai import OpenAI
client = OpenAI(
api_key="YOUR_API_KEY",
base_url="https://inference.example.com/v1"
)
response = client.chat.completions.create(
model="relace/relace-apply-3",
messages=[
{
"role": "user",
"content": "Describe this image in one sentence."
}
],
)
print(response.to_json())
{
"model": "relace/relace-apply-3",
"messages": [
{
"role": "user",
"content": "Describe this image in one sentence."
}
]
}
5 Core Capabilities
-
Code Patch Merging
Specialized in merging AI-generated code edits into existing source files using diff-like updates while preserving surrounding context accurately.
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High-Speed Inference
Applies code changes at around ten thousand tokens per second, enabling extremely fast code integration workflows for large projects.
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Large Code Context
Handles up to a 256k-token context window, allowing operation on very large files or extensive multi-file code snippets at once.
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Structured Diff Handling
Supports integrating updates from multiple diff formats produced by other LLMs, reliably resolving complex or ambiguous edit snippets.
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Multi-Language Code
Trained on diverse programming languages including JavaScript, Python, Ruby, Markdown, and HTML for broadly applicable code merging tasks.
6 Most Valuable Use Cases
- Automated Code Patching
- LLM Agent Code Merging
- High-Speed File Updating
- Multi-Model Edit Application
- Large-Context Code Edits
- Safe Code Integration Monitoring
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
-
Unified AI Routing
Automatically route each request to the optimal model across providers based on cost, latency, and quality—without changing your integration or redeploying code.
One endpoint, any model -
Cost-Aware Controls
Set per-project or per-route budgets, caps, and policies while LLM.API dynamically picks the best-value models and surfaces clear spend analytics to your team.
Optimize spend by design -
Resilient Fallback Logic
Define provider-agnostic fallback chains so requests seamlessly fail over to backup models when providers throttle, degrade, or go down—no client changes required.
Never ship a 500 -
Full-Stack Observability
Trace every request across providers with structured logs, metrics, and payload inspection so you can debug failures, compare models, and tune performance in production.
See every token -
Task-Level Abstractions
Call high-level tasks—chat, tools, RAG, moderation—through a stable, provider-neutral schema that survives model churn and deprecation with minimal application changes.
Code to tasks, not models -
High-Throughput Batching
Submit large batches of requests through a single API call with smart chunking, retries, and concurrency controls to maximize throughput and minimize unit cost.
Scale requests, not code
When to Use — When NOT to Use
Use it if...
- You need a hosted Apply 3 deployment with Relace-managed infrastructure and monitoring.
- You need straightforward API integration using Relace’s authentication, billing, and usage dashboards.
- Your use case involves experimenting with Apply 3 alongside other Relace-provided foundation models.
- You need to quickly prototype LLM features without managing your own model hosting stack.
- Your use case involves moderate-scale workloads where Relace’s default quotas and limits suffice.
- You need centralized governance, logging, and policy controls across multiple Relace-hosted AI models.
Avoid if...
- You need strict on-premise deployment because your workload requires zero external cloud dependencies.
- Your workload requires a different model family with specialized vision, audio, or multimodal support.
- You need ultra-low per-token pricing available only from your existing direct Apply provider.
- Your workload requires fine-tuned or custom-trained variants not exposed through Relace Apply 3.
- You need hard real-time inference guarantees beyond typical cloud API latency characteristics.
- Your workload requires regional data residency in jurisdictions Relace currently does not support.
Frequently Asked Questions
-
What is Relace Apply 3?
Relace Apply 3 is a large language model by Relace optimized for fast, cost‑efficient text generation and reasoning via the LLM.API platform.
-
What modalities does Relace Apply 3 support?
Relace Apply 3 currently supports text input and text output only, and does not handle images, audio, or video.
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How do I access Relace Apply 3 through LLM.API?
You call the unified LLM.API endpoint with the model name "relace-apply-3" in your request payload, plus your LLM.API API key.
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How is Relace Apply 3 priced on LLM.API?
Relace Apply 3 usage is billed per input and output token through LLM.API; check your LLM.API pricing dashboard for current rates.
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What is the context window of Relace Apply 3?
Relace Apply 3 supports a context window of several thousand tokens; consult the LLM.API model docs for the exact current limit.
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How fast is Relace Apply 3 in terms of latency and throughput?
Relace Apply 3 is designed for low latency, streaming token output, and high request throughput when served via the LLM.API infrastructure.
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What is Relace Apply 3 best suited for?
Relace Apply 3 is best for application-style tasks like form filling, structured outputs, business workflows, and instruction-following chatbots.
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How does Relace Apply 3 compare to similar models?
Compared with general-purpose LLMs, Relace Apply 3 emphasizes predictable formatting, stable behavior, and efficiency over open‑ended creative generation.
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What are the main limitations of Relace Apply 3?
Relace Apply 3 may hallucinate facts, lacks real‑time knowledge or browsing, and is not suitable for high‑risk domains without human review.
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Can I fine-tune or customize Relace Apply 3 through LLM.API?
Direct fine‑tuning is not available; instead, you configure Relace Apply 3 behavior via prompts, system instructions, and retrieval or tool integration.
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