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Recraft V4.1 Vector
- Image Generation
Recraft V4.1 Vector is a text-to-vector image generation model from Recraft that converts prompts into fully editable SVG graphics with clean geometry and structured layers. It is optimized for professional design workflows such as logos, icons, and illustration systems.
About the model
What is Recraft V4.1 Vector?
Recraft V4.1 Vector is an AI model that generates editable SVG-style vector images from natural-language prompts. It is mainly used to create production-ready logos, icons, and brand assets that can be edited directly in tools like Figma or Illustrator. It is also suited to scalable illustration systems and marketing or product graphics where clean, resolution-independent output is required. It belongs to the Recraft V4.1 family of models, which extends the earlier Recraft V4 series with improved visual quality and specialized vector and raster variants.
Model capabilities
5 Core Capabilities
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Text-to-vector art
Generates clean, editable SVG-style vector graphics such as logos, icons, and illustrations directly from natural language prompts.
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Brand asset design
Creates scalable brand assets like logos and design-system elements suitable for professional design workflows and production environments.
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Icon and logo output
Produces precise vector icons and logo-style marks with strong structural clarity that remain crisp at any resolution or size.
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Prompt interpretation
Understands short or detailed prompts to generate vector images closely matching requested layout, style, and subject matter.
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Multilingual prompting
Accepts prompts written in multiple languages, enabling designers worldwide to generate vector graphics without changing their primary language.
Use cases
6 Most Valuable Use Cases
- Logo Vector Design
- Icon Set Generation
- Brand Asset Vectors
- Scalable UI Illustrations
- E-commerce Graphics
- Design System Vectors
Transparent pricing
Cost Comparison
Up to ~40% cheaper than Recraft for high-volume vector image generation
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~280ms | ~70 img/min | 99.99% | ~$0.35/1K images | ~$0.35/1K images | ~50 prompts/sec per project |
| Recraft | Global | ~350ms | ~40 img/min | 99.9% | ~$0.60/1K images | ~$0.60/1K images | ~20 prompts/sec per workspace |
| Replicate | US East | ~420ms | ~30 img/min | 99.5% | ~$0.80/1K images | ~$0.80/1K images | ~15 prompts/sec per model instance |
| Banana | US West | ~400ms | ~35 img/min | 99.5% | ~$0.75/1K images | ~$0.75/1K images | ~15 prompts/sec per deployment |
| RunPod | EU West | ~380ms | ~32 img/min | 99.5% | ~$0.70/1K images | ~$0.70/1K images | ~12 prompts/sec per pod |
Performance benchmarks
Technical Specifications
| Metric | Recraft V4.1 Vector | OpenAI DALL·E 3 | Midjourney V6 |
|---|---|---|---|
| Latency per Image | ~2.0s | ~2.5s | ~3.0s |
| Throughput | ~40 img/s | ~30 img/s | ~25 img/s |
| Max Resolution | ~4096×4096 | 1024×1024 | ~2048×2048 |
| Price per Image | ~$0.02 | ~$0.04 | ~$0.03 |
| Supported Formats | PNG, JPG, SVG | PNG, JPG | PNG, JPG |
| Uptime | 99.9% | 99.9% | 99.5% |
30-day usage via LLM API
- 2.8B
- Prompt tokens processed (30 days)
- 7.4M
- API image & vector requests served (30 days)
- 1.1B
- Vector embeddings generated (30 days)
- 99.8%
- Average API uptime (last 30 days)
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Unified AI Routing
Dynamically route each request to the optimal model across providers based on latency, cost, or performance—no client changes, just smarter traffic control.
One endpoint, every model. -
Cost-Aware Orchestration
Control spend with policy-based routing, price caps, and automatic downgrades to cheaper equivalents while preserving SLAs and output quality.
Lower cost, same impact. -
Automatic Fallback Logic
Recover from outages, rate limits, and slow models with built-in multi-provider failover—no custom retry code, no manual playbooks.
Resilience by default. -
Deep Observability
Get end-to-end traces, latency and error dashboards, and model-level usage breakdowns so you can debug, tune routing, and justify spend in minutes.
See every token flow. -
Task-Level Abstractions
Call `tasks` like classify, extract, or generate instead of vendor-specific APIs—LLM.API selects and configures the right model behind a stable interface.
Describe intent, not models. -
High-Throughput Batch
Run massive batch jobs across providers with automatic chunking, concurrency control, and retry semantics so you don’t build bespoke batch pipelines.
Millions of calls, one job.
Decision guide
When to Use — When NOT to Use
Use it if...
- You need editable SVG vector graphics like logos, icons, or UI elements from prompts.
- You need scalable brand assets where clean lines and consistent geometry remain crisp when resized.
- Your use case involves programmatically generating vector illustrations for dashboards, infographics, or presentations.
- You need text-to-vector output that slots directly into design tools without manual tracing.
- Your use case involves batch-generating many simple branded icons with controlled color palettes.
- You need vectors that preserve structure for later editing of paths, strokes, and fills.
- Your use case involves automating vector asset production for e-commerce, marketing, or product catalogs.
Avoid if...
- You need high-resolution photorealistic raster images rather than clean flat vector graphics.
- Your workload requires complex multi-step reasoning, planning, or textual analysis beyond image generation.
- You need real-time, low-latency image streaming for interactive applications like live drawing tools.
- Your workload requires detailed 3D meshes, CAD models, or volumetric assets instead of 2D vectors.
- You need pixel-perfect photo editing, retouching, or compositing on existing high-res raster photos.
- Your workload requires understanding and generating long-form text documents rather than visual assets.
- You need on-device or offline inference where external vector-generation APIs are unacceptable.
FAQ
Frequently Asked Questions
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What is Recraft V4.1 Vector?
Recraft V4.1 Vector is an image generation model by Recraft focused on producing high-quality vector-style graphics and illustrations.
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What is Recraft V4.1 Vector best suited for?
It is best for generating clean, scalable vector-like artwork, icons, logos, UI elements, and stylized illustrations from text prompts.
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What input and output modalities does Recraft V4.1 Vector support via LLM.API?
Via LLM.API, Recraft V4.1 Vector accepts text prompts as input and returns generated images as output.
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How is Recraft V4.1 Vector priced on LLM.API?
Pricing is usage-based per generated image; check your LLM.API dashboard or pricing docs for the current per-request and per-resolution costs.
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What is the typical latency of Recraft V4.1 Vector requests?
Most image generations complete within a few seconds, depending on image resolution, prompt complexity, and current LLM.API load.
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Does Recraft V4.1 Vector have a context window like text LLMs?
It does not use a token-based context window; it consumes each text prompt independently for image generation.
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How do I call Recraft V4.1 Vector through LLM.API?
You specify the Recraft V4.1 Vector model name in your LLM.API image generation request, include your API key, and pass a text prompt plus generation parameters.
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How does Recraft V4.1 Vector compare to general-purpose image models?
Compared to general image generators, it is more specialized for crisp, vector-like, design-oriented outputs rather than photorealistic images.
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What are the main limitations of Recraft V4.1 Vector?
It may struggle with highly photorealistic scenes, fine-grained text rendering, and complex multi-image consistency compared with some raster-focused models.
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Can I use Recraft V4.1 Vector for batch image generation on LLM.API?
Yes, you can send batched or parallel requests, but throughput and rate limits depend on your LLM.API plan and configured quotas.