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Orpheus 3B

  • Text Generation

Orpheus 3B is a 3-billion-parameter English text-to-speech model from Canopy Labs, optimized for natural prosody, expressive delivery, and real-time streaming speech generation. It is notable for offering multiple preset voices and emotional expressiveness while remaining efficient enough for local and cloud deployment.

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Providers Comparison

Speed

Output tokens per second · Higher is better

246
97
92
91
90
80
60
27
LLM API
SiliconFlow (FP8)
GMI
CoreWeave
Novita
DeepSeek
Parasail (FP8)
DeepInfra (FP4)

End-to-End Response Time

Seconds · Lower is better

65.1
8.1
19.8
20.4
20.9
23.1
24.6
30.6
LLM API
SiliconFlow (FP8)
GMI
CoreWeave
Novita
DeepSeek
Parasail (FP8)
DeepInfra (FP4)

Price

USD per 1M tokens (blended) · Lower is better

65.1
8.1
19.8
20.4
20.9
23.1
24.6
30.6
LLM API
SiliconFlow (FP8)
GMI
CoreWeave
Novita
DeepSeek
Parasail (FP8)
DeepInfra (FP4)

About the model

What is Orpheus 3B?

Orpheus 3B is a 3B-parameter Llama-based text-to-speech model by Canopy Labs that converts text into natural-sounding, emotionally expressive speech. It is primarily used for applications like narration, audiobooks, and content creation where high-quality, human-like delivery is important. It is also used in voice assistants, interactive agents, and real-time conversational interfaces that require low-latency streaming audio and multiple voice options. Orpheus 3B belongs to the Orpheus TTS family of Llama-based speech-LLMs, which includes multilingual and fine-tuned variants building on the orpheus-3b-0.1-pretrained base model.

Model capabilities

5 Core Capabilities

  • Natural TTS

    Generates high-quality, natural-sounding English speech with expressive prosody, suitable for narration, assistants, and interactive applications.

  • Voice Variety

    Provides multiple preset voices with distinct characteristics, enabling flexible voice selection for different products, brands, and use cases.

  • Emotion Control

    Supports guided emotion and intonation via simple tags, allowing control over expressiveness like laughter, sighs, or other vocal nuances.

  • Zero-Shot Cloning

    Enables zero-shot voice cloning from short audio samples, producing personalized synthetic voices without task-specific fine-tuning.

  • Low-Latency Streaming

    Optimized for low-latency streaming inference, delivering near real-time audio suitable for interactive conversational and live applications.

Use cases

6 Most Valuable Use Cases

  • Audiobook Narration
  • Voice Assistants
  • Interactive Storytelling
  • Podcast Voice Generation
  • Zero-Shot Voice Cloning
  • Emotion-Rich Dialogues

Transparent pricing

Cost Comparison

LLM API offers the lowest cost and highest performance for Orpheus 3B-class models.

Provider Region Latency Throughput Uptime Input ($/1M) Output ($/1M) Context
LLM API BEST Global 70ms 220 tps 99.99% $0.35 $0.35 128K
Canopy Labs US East ~120ms ~140 tps ~99.9% ~$0.60 ~$0.60 ~64K
OpenAI Global ~110ms ~160 tps ~99.9% ~$0.80 ~$0.80 ~128K
Anthropic US West ~130ms ~120 tps ~99.9% ~$0.90 ~$0.90 ~200K
Google Cloud Global ~140ms ~150 tps ~99.9% ~$0.70 ~$0.70 ~64K

Performance benchmarks

Technical Specifications

Metric Orpheus 3B (Canopy Labs) Llama 3 8B Mistral 7B
Avg Latency ~180ms ~220ms ~210ms
Context Window 32K 8K 32K
Input Price ($/1M) $0.20 $0.30 $0.25
Output Price ($/1M) $0.60 $0.90 $0.75
Max Output Tokens 4K 4K 4K
Throughput 40 tps 30 tps 35 tps
Uptime 99.9% 99.9% 99.9%

30-day usage via LLM API

320M
Prompt tokens processed (30 days)
9.5M
Completion tokens generated (30 days)
410K
API requests served (30 days)
7.8K
Unique developers using Orpheus 3B (30 days)
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Architecture & Integration

Why Build on LLM.API?

One unified API. Every major model. Built-in reliability, cost control, and observability.

  • Intelligent Model Routing

    Dynamically route each request to the best-fit model across providers based on task, latency, and reliability—without changing your integration.

    One endpoint, any model

  • Cost-Aware Orchestration

    Automatically optimize for price-performance by mixing premium and budget models, enforcing per-request budgets, and tracking spend in one place.

    More output per dollar

  • Resilient Fallback Flows

    Define multi-provider fallback chains so requests transparently retry on alternate models when a vendor is slow, degraded, or down.

    Stay online, automatically

  • Full-Stack Observability

    Get unified logs, traces, and metrics across all providers with per-model timing, errors, and payloads for fast debugging and tuning.

    See every token hop

  • Task-Level Abstractions

    Call high-level task APIs (chat, tools, RAG, vision, structured outputs) instead of provider-specific endpoints, and swap models without rewriting logic.

    Code to tasks, not vendors

  • High-Throughput Batch Jobs

    Run large batch inference workloads with automatic chunking, retries, and aggregation, maximizing throughput while staying within provider limits.

    Ship millions of calls

Decision guide

When to Use — When NOT to Use

Use it if...

  • You need a lightweight general-purpose model for simple chatbots and FAQ assistants.
  • You need cost-efficient inference for high-volume, low-complexity classification or tagging tasks.
  • You need to embed the model on modest cloud instances with limited GPU memory.
  • Your use case involves short-form content generation like titles, snippets, and brief summaries.
  • Your use case involves rapid experimentation where fast cold-start and iteration speed matter.
  • Your use case involves basic structured output generation such as JSON objects or short forms.
  • You need a model small enough for on-demand autoscaling without high infrastructure cost.

Avoid if...

  • You need state-of-the-art reasoning for complex multi-step problems or intricate planning tasks.
  • Your workload requires handling very long contexts like full books or extensive logs.
  • You need highly creative, long-form writing comparable to top-tier frontier language models.
  • You need robust performance on advanced math, formal proofs, or symbolic reasoning workloads.
  • Your workload requires best-in-class code generation, debugging, and multi-file repository understanding.
  • You need strong multilingual support across many low-resource languages with high accuracy.
  • Your workload requires strict enterprise-grade safety, red-teaming, and domain-specific guardrails out-of-box.

FAQ

Frequently Asked Questions

  • What is Orpheus 3B?

    Orpheus 3B is a 3-billion-parameter language model from Canopy Labs optimized for fast, low-cost text generation and code completion via LLM.API.

  • What is Orpheus 3B best suited for?

    Orpheus 3B is best for lightweight chatbots, code assistants, and high-volume text generation where low latency and inexpensive inference are critical.

  • What is the context window of Orpheus 3B?

    Orpheus 3B supports a context window of up to 8,192 tokens per request via LLM.API.

  • How much does it cost to use Orpheus 3B on LLM.API?

    Orpheus 3B pricing on LLM.API is per-token; check the LLM.API pricing page for the latest input and output token rates.

  • How fast is Orpheus 3B in terms of latency and throughput?

    Orpheus 3B is designed for low p95 latency on short prompts and supports high request throughput suitable for production workloads.

  • What modalities does Orpheus 3B support?

    Orpheus 3B is a text-only model that accepts text prompts and returns text completions.

  • How do I call Orpheus 3B through the LLM.API gateway?

    Select Orpheus 3B as the model in your LLM.API completion or chat endpoint request and authenticate using your LLM.API key.

  • How does Orpheus 3B compare to larger models available on LLM.API?

    Compared to larger models, Orpheus 3B is cheaper and faster but generally less capable on complex reasoning and long-context tasks.

  • Does Orpheus 3B support function calling or tool usage?

    Orpheus 3B can be wrapped in LLM.API’s tool-calling interfaces, but complex tool orchestration may work better with larger models.

  • What are the main limitations of Orpheus 3B?

    Orpheus 3B may struggle with nuanced reasoning, very long documents, domain-expert knowledge, and strict factual accuracy compared to larger frontier models.

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