Can I use tools or structured outputs with ZAYA1-8B?

Tool and/or structured-output flags appear on one or more providers for this model—confirm in the providers table.

What modalities does ZAYA1-8B support?

ZAYA1-8B accepts text and produces text according to its architecture metadata on LLM.API.

Is ZAYA1-8B a chat model?

Yes—ZAYA1-8B is exposed as a chat/completions-style endpoint on LLM.API.

What are limitations of ZAYA1-8B?

Like other API models, ZAYA1-8B can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.

How is ZAYA1-8B priced on LLM.API?

Listed pricing metadata shows: See LLM.API pricing. Confirm live rates in the LLM.API dashboard or docs before production budgeting.

Does ZAYA1-8B support streaming?

Yes—at least one listed provider advertises streaming.

What is the context length for ZAYA1-8B?

Reported context for ZAYA1-8B is 131K tokens. Always verify the active provider row if multiple providers are listed.

How do I call ZAYA1-8B via API?

Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "zaya1-8b" and your LLM.API key. See the code snippet on this page.

When should I choose ZAYA1-8B?

You want OpenAI-compatible chat completions through a single LLM.API key — especially when you specifically need ZAYA1-8B.

Which endpoint do I call for ZAYA1-8B?

POST https://api.llmapi.ai/v1/chat/completions with model "zaya1-8b" and your LLM.API key. The request and response bodies are OpenAI-compatible.

How do I switch from OpenAI to ZAYA1-8B on LLM.API?

Change two lines: set base_url to https://api.llmapi.ai/v1 and api_key to your LLM.API key. The rest of your OpenAI SDK code stays the same.

Does ZAYA1-8B support function calling and structured outputs?

Function calling: supported via tools. Structured outputs: supported via response_format.

ZAYA1-8B: Self-Hosting & Deployment Guide

Call ZAYA1-8B through LLM.API when you want Zyphra-family text generation with unified auth, provider choice, and production-friendly defaults.

Start Using API

What is ZAYA1-8B?

ZAYA1-8B belongs to the Zyphra family and is offered as a hosted chat endpoint. ZAYA1-8B provided by zyphra. It is wired for API access through LLM.API with OpenAI-compatible patterns. Through LLM.API you keep one base URL while selecting this model for assistants, tools, and content workflows.

How to run ZAYA1-8B yourself

Open weights (apache-2.0), 8.8B parameters. Below: how much memory the weights need at each quantization, which hardware fits them, and the commands to serve it.

ZAYA1-8B VRAM requirements

Precision / quantization	Weights	Figure	Source
BF16 (full precision)	17.7 GB	Estimate	8.8B params × 2 bytes
FP8	9.6 GB	File size	Zyphra/ZAYA1-8B-FP8-Experts
GGUF Q8_0	9.5 GB	File size	Abiray/ZAYA1-8B-GGUF
GGUF Q4_K_M	5.6 GB	File size	Abiray/ZAYA1-8B-GGUF

File size = actual download size on Hugging Face. Estimate = parameter count × bytes per weight. Weights only — the KV cache for long contexts needs extra memory; the fit tables below add 15% headroom as a rule of thumb.

GPU requirements: deploy ZAYA1-8B on your hardware

Datacenter GPUsConsumer GPUsApple SiliconCloud instances

Hardware	BF16 (full precision)	FP8	GGUF Q8_0	GGUF Q4_K_M
NVIDIA H200141 GB	1×	1×	1×	1×
NVIDIA H10080 GB	1×	1×	1×	1×
NVIDIA A100 80GB80 GB	1×	1×	1×	1×
NVIDIA L40S48 GB	1×	1×	1×	1×

GPUs needed per variant.

Hardware	GGUF Q8_0	GGUF Q4_K_M
RTX 509032 GB	1×	1×
RTX 409024 GB	1×	1×
RTX 309024 GB	1×	1×

GGUF via llama.cpp; more cards or CPU offload for larger files.

Hardware	GGUF Q8_0	GGUF Q4_K_M
Mac, 512 GBM3 Ultra	Fits	Fits
Mac, 192 GBM2 Ultra	Fits	Fits
Mac, 128 GBM4 Max / M3 Max	Fits	Fits
Mac, 64 GBM4 Pro / Max	Fits	Fits

Assumes ~75% of unified memory is usable by the GPU.

Hardware	BF16 (full precision)	FP8	GGUF Q8_0	GGUF Q4_K_M
AWS p5.48xlarge8× H100 · 640 GB	Fits	Fits	Fits	Fits
AWS p4de.24xlarge8× A100 80GB · 640 GB	Fits	Fits	Fits	Fits
AWS g6e.12xlarge4× L40S · 192 GB	Fits	Fits	Fits	Fits
Google Cloud a3-highgpu-8g8× H100 · 640 GB	Fits	Fits	Fits	Fits
Azure ND H100 v58× H100 · 640 GB	Fits	Fits	Fits	Fits

Whole-instance GPU memory; public instance specs.

Skip the deploy — use LLM.API as your endpoint

No GPUs, no quantization trade-offs. Same model, OpenAI-compatible, with custom volume discounts on request.

Get API key See API code ↓

Providers

LLM.API routes ZAYA1-8B to the providers below, with discounted effective rates versus list price.

List price by provider

InputOutput

zyphra$0 in

$0 out

Provider list prices; the LLM.API discount applies on top.

Provider	Pricing	Context	Capabilities
zyphra	in $0; out $0 per 1M tokens	131K tokens	tools, streaming, reasoning, JSON, structured

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 11 Oct 2026.

Try this model

Test ZAYA1-8B right here — free to start.

ZAYA1-8B

Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call ZAYA1-8B through the OpenAI-compatible API — POST /v1/chat/completions.

Switching from OpenAI? Change 2 lines.

- base_url="https://api.openai.com/v1"
- api_key="YOUR_OPENAI_KEY"
+ base_url="https://api.llmapi.ai/v1"
+ api_key="YOUR_LLMAPI_KEY"

Everything else stays the same — SDK, message format, tools, streaming. Set model to zaya1-8b and the call runs unchanged.

bash

curl https://api.llmapi.ai/v1/chat/completions \
  -H "Authorization: Bearer $LLMAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "zaya1-8b",
    "messages": [
      {"role": "user", "content": "Give me three crisp launch checklist items."}
    ]
  }'

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMAPI_KEY",
    base_url="https://api.llmapi.ai/v1",
)

resp = client.chat.completions.create(
    model="zaya1-8b",
    messages=[
        {"role": "system", "content": "You are a precise product assistant."},
        {"role": "user", "content": "Give me three crisp launch checklist items."},
    ],
)
print(resp.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.LLMAPI_KEY,
  baseURL: "https://api.llmapi.ai/v1",
});

const resp = await client.chat.completions.create({
  model: "zaya1-8b",
  messages: [
    { role: "user", content: "Give me three crisp launch checklist items." },
  ],
});

console.log(resp.choices[0].message.content);

stream = client.chat.completions.create(
    model="zaya1-8b",
    messages=[{"role": "user", "content": "Write a haiku about latency."}],
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta.content
    if delta:
        print(delta, end="", flush=True)

{
  "model": "zaya1-8b",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ]
}

ENDPOINTS

Supported endpoints & features

Base URL https://api.llmapi.ai/v1 · model ID zaya1-8b · OpenAI-compatible.

POST /v1/chat/completionsChat Completions — supported

Feature	Status	How to set it
Function calling (tools)	Supported	`tools, tool_choice`
Parallel tool calls	Not supported	`parallel_tool_calls`
JSON mode	Supported	`response_format: {"type": "json_object"}`
Structured outputs	Supported	`response_format: {"type": "json_schema", …}`
Streaming	Supported	`stream: true`
Vision (image input)	Not supported	`image_url content parts`
Web search	Not supported	`web_search`

ERRORS

Errors & fallback routing

Code	What it means	What to do
`401`	Missing or invalid API key.	Send `Authorization: Bearer YOUR_LLMAPI_KEY` and check the key is active.
`429`	Rate limited, or the upstream provider is throttling the request.	Back off and retry with jitter; the gateway also retries the request on another provider where one is available.
`5xx`	Upstream provider error or timeout.	Retry; fallback routing sends the retry to the next healthy provider for this model.

When a provider fails or throttles, the request is routed to the next provider serving this model — the ones listed in the providers table above.

ZAYA1-8B in production

Best workloadsLLM.API features

What the model is good at.

Multi-step reasoning
Breaks down complex problems into intermediate steps before answering. Reflects Zyphra positioning for this endpoint.
Safety-aware replies
Supports product policies with refusals and cautious handling of sensitive topics.
Structured outputs
Can produce JSON-friendly or schema-oriented responses when prompted carefully. Relevant for `zaya1-8b` workloads on LLM.API.
Long-context synthesis
Summarizes and cross-references information across large prompts when context allows. Relevant for `zaya1-8b` workloads on LLM.API.
Conversational UX
Maintains coherent multi-turn assistant behavior for product chat surfaces.

What you get when you call ZAYA1-8B through LLM.API.

Unified AI Routing
Reach ZAYA1-8B and sibling models through one OpenAI-compatible endpoint.
One endpoint, every model.
Cost Control
Production: Compare provider price points and keep spend visible as you scale ZAYA1-8B.
More performance, less spend.
Reliability Layer
Retry and route across configured providers when a single upstream blips.
Never go dark on users.
Observability
Production: Trace prompts, tokens, and errors for ZAYA1-8B alongside the rest of your stack.
See every token, everywhere.
Drop-in SDKs
Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.
Think tasks, not models.
Model Breadth
Practical: Swap ZAYA1-8B for chat, media, or embedding alternatives without rewriting auth.
Scale from 1 to millions.

When to Use — When NOT to Use

Use it if...

You need provider failover options exposed for this model id (ZAYA1-8B)
Your prompts benefit from the model's family strengths (reasoning, speed, or cost) (ZAYA1-8B)
You want OpenAI-compatible chat completions through a single LLM.API key (ZAYA1-8B)
You need a general-purpose text model for assistants, agents, or content workflows (ZAYA1-8B)

Avoid if...

You need pure embedding, OCR, or media generation instead of chat
You need guaranteed real-time hard latency SLAs without benchmarking the provider
You require on-prem only deployment with no cloud inference
Your use case depends on unpublished proprietary benchmarks not listed here

LLM API rating for ZAYA1-8B

★ 2.9/ 5.0LLM API rating
50% production adoption · 30% category benchmark · 20% tooling & valueEstimated from published specs

2.5Production adoptionNot yet tracked — no public usage data for this release

2.8Benchmark qualityNo public Arena rank — scored on declared reasoning support

4.0Developer ecosystemContext window, tool and JSON support, price against the category median

Reasoning & chat 2.9Coding 3.4Tool & function calling 3.4

How is this rating calculated?

Every model gets a separate 1.0–5.0 score for each category it can be used in, so a model can rate high for coding and lower for vision. The score combines three parts:

Production adoption — 50%. How widely the model is actually used in production: provider traffic, LiteLLM and gateway usage, open-weight downloads and its LMArena rank. Proven, widely used models score highest.
Category benchmark — 30%. The public benchmark that matters for that category — the Artificial Analysis Intelligence Index and LMArena Elo for reasoning, LMArena coding Elo for coding, image and document support plus vision Elo for vision, tool and JSON support for function calling, measured speed for voice.
Tooling & value — 20%. Context window, support for tools, structured outputs and streaming, and price compared with the category median.

Scores of 4.0 and above are shown in green, 3.0–3.9 in yellow, below 3.0 in red. When a model has no public data for a part, it gets a neutral value instead of an invented number.

A “Verified benchmark” mark (✓) means an independent lab — LMArena or Artificial Analysis — has actually tested that model, and the source figure is shown. Models without that mark are labelled “Estimated from published specs”: the score comes from the published context window, tool and schema support, price and adoption only, because no independent lab has tested them yet.

One exception: a brand-new top-tier model that scores very high on the independent benchmarks but has no usage history yet is rated on the benchmark (60%) and tooling & value (40%) alone, so a missing adoption figure can't hold it back. All inputs come from public sources and are refreshed weekly.

Adoption and benchmark inputs come from public sources — LMArena, Artificial Analysis Intelligence Index and published model specs. Updated weekly.

COMMUNITY

What developers say about text-to-speech models

LMArena—Speed—Sources: LMArena, Artificial Analysis

Summarised from publicly published developer and community reviews of this model family. Opinions are the sources’, not LLM.API’s, and may not be specific to ZAYA1-8B.

Community consensus still puts ElevenLabs-class voices at the top for realism and emotional range.
The debate has shifted from quality to value, as open-source voices mature and heavy usage pushes teams to higher tiers.
Developers advise designing for rate limits early — 429 handling on generation calls is a common production surprise.

SOURCES

Frequently Asked Questions

Can I use tools or structured outputs with ZAYA1-8B?
Tool and/or structured-output flags appear on one or more providers for this model—confirm in the providers table.
What modalities does ZAYA1-8B support?
ZAYA1-8B accepts text and produces text according to its architecture metadata on LLM.API.
Is ZAYA1-8B a chat model?
Yes—ZAYA1-8B is exposed as a chat/completions-style endpoint on LLM.API.
What are limitations of ZAYA1-8B?
Like other API models, ZAYA1-8B can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.
How is ZAYA1-8B priced on LLM.API?
Listed pricing metadata shows: See LLM.API pricing. Confirm live rates in the LLM.API dashboard or docs before production budgeting.
Does ZAYA1-8B support streaming?
Yes—at least one listed provider advertises streaming.
What is the context length for ZAYA1-8B?
Reported context for ZAYA1-8B is 131K tokens. Always verify the active provider row if multiple providers are listed.
What is ZAYA1-8B?
ZAYA1-8B provided by zyphra. It is wired for API access through LLM.API with OpenAI-compatible patterns. On LLM.API it is addressed as `zaya1-8b`.
How do I call ZAYA1-8B via API?
Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "zaya1-8b" and your LLM.API key. See the code snippet on this page.
When should I choose ZAYA1-8B?
You want OpenAI-compatible chat completions through a single LLM.API key — especially when you specifically need ZAYA1-8B.

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ZAYA1-8B: Self-Hosting & Deployment Guide

What is ZAYA1-8B?

How to run ZAYA1-8B yourself

ZAYA1-8B VRAM requirements

GPU requirements: deploy ZAYA1-8B on your hardware

Providers

Try this model

Code snippet

Supported endpoints & features

Errors & fallback routing

ZAYA1-8B in production

Multi-step reasoning

Safety-aware replies

Structured outputs

Long-context synthesis

Conversational UX

Unified AI Routing

Cost Control

Reliability Layer

Observability

Drop-in SDKs

Model Breadth