---
title: Writer: Palmyra X5 — Enterprise Agents | ModelsLab
description: Deploy Writer: Palmyra X5 for 1M-token context and sub-second tool calls. Generate agentic workflows at scale. Try Writer: Palmyra X5 API now.
url: https://modelslab-frontend-v2-927501783998.us-east4.run.app/writer-palmyra-x5
canonical: https://modelslab-frontend-v2-927501783998.us-east4.run.app/writer-palmyra-x5
type: website
component: Seo/ModelPage
generated_at: 2026-05-13T09:43:07.673660Z
---

Available now on ModelsLab · Language Model

Writer: Palmyra X5
Scale Agents Million Tokens
---

[Try Writer: Palmyra X5](/models/open_router/writer-palmyra-x5) [API Documentation](https://docs.modelslab.com)

Build Agents Faster Cheaper
---

1M Context

### Process Million-Token Prompts

Handles full 1M-token inputs in 22 seconds for deep document analysis.

Sub-Second Tools

### Invoke Multi-Step Functions

Delivers ~300ms tool-calling latency for real-time agent workflows.

Cost Efficient

### 3-4x Less Than GPT-4

Priced at $0.60/M input, $6/M output tokens for scalable enterprise use.

Examples

See what Writer: Palmyra X5 can create
---

Copy any prompt below and try it yourself in the [playground](/models/open_router/writer-palmyra-x5).

Contract Summary

“Analyze this 500-page contract PDF. Extract key clauses on termination, payment terms, and liabilities. Output as structured JSON with risk levels.”

RFP Response

“Review 200-page RFP document. Generate compliant proposal sections for pricing, timeline, and compliance. Align with brand guidelines.”

Data Pipeline Code

“Write Python code to process customer feedback logs from S3. Summarize themes, call sentiment API, output dashboard-ready CSV.”

Regulatory Report

“Summarize 1000-page regulatory filing. Identify compliance gaps, suggest fixes, invoke database for historical data comparison.”

For Developers

A few lines of code.
Agents Million Tokens Fast
---

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

- **Serverless:** scales to zero, scales to millions
- **Pay per token,** no minimums
- **Python and JavaScript SDKs,** plus REST API

[API Documentation ](https://docs.modelslab.com)

PythonJavaScriptcURL

Copy

```
<code>import requests

response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())</code>
```

FAQ

Common questions about Writer: Palmyra X5
---

[Read the docs ](https://docs.modelslab.com)

### What is Writer: Palmyra X5?

Writer: Palmyra X5 is an enterprise LLM with 1M-token context for agentic workflows. It supports multi-step tool calls and processes long prompts in 22 seconds. Ideal for RAG and large-scale agents.

### How does Writer Palmyra X5 API perform on benchmarks?

Scores 48.7 on BigCodeBench, 53% on Longbench v2, 19.1% on MRCR 8-needle. Matches GPT-4.1 retrieval at lower cost. Excels in code generation and reasoning.

### What is Writer: Palmyra X5 alternative to GPT-4?

Writer: Palmyra X5 offers 1M context vs GPT-4.1's limits, 3-4x cheaper tokens. Sub-second tools enable agent scaling unavailable in GPT-4.1.

### Can Writer: Palmyra X5 model handle multilingual tasks?

Supports over 30 languages with adaptive reasoning. Suitable for global enterprise agents and compliance workflows.

### How to access Writer Palmyra X5 API?

Available via LLM endpoint. Use standard chat completions with long context. No setup for 1M tokens or tool calls.

### What are Writer: Palmyra X5 LLM use cases?

Powers RAG pipelines, document summarization, code generation, multi-agent systems. Pre-built agents for regulatory analysis and file summaries.

Ready to create?
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Start generating with Writer: Palmyra X5 on ModelsLab.

[Try Writer: Palmyra X5](/models/open_router/writer-palmyra-x5) [API Documentation](https://docs.modelslab.com)

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*This markdown version is optimized for AI agents and LLMs.*

**Links:**
- [Website](https://modelslab.com)
- [API Documentation](https://docs.modelslab.com)
- [Blog](https://modelslab.com/blog)

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*Generated by ModelsLab - 2026-05-13*