How is Qwen3 Next 80B A3B Instruct priced on LLM.API?

Listed pricing metadata shows: In $0.5 / 1M tokens · Out $2.00 / 1M tokens. LLM.API may offer discounted effective rates (illustrative ~30% callout vs list when available).. Confirm live rates in the LLM.API dashboard or docs before production budgeting.

What modalities does Qwen3 Next 80B A3B Instruct support?

Qwen3 Next 80B A3B Instruct accepts text and produces text according to its architecture metadata on LLM.API.

Can I use tools or structured outputs with Qwen3 Next 80B A3B Instruct?

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

Where is the canonical page for Qwen3 Next 80B A3B Instruct?

https://llmapi.ai/models/alibaba-qwen3-next-80b-a3b-instruct/

When should I choose Qwen3 Next 80B A3B Instruct?

You need a general-purpose text model for assistants, agents, or content workflows — especially when you specifically need Qwen3 Next 80B A3B Instruct.

How do I call Qwen3 Next 80B A3B Instruct via API?

Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "qwen3-next-80b-a3b-instruct" and your LLM.API key. See the code snippet on this page.

What are limitations of Qwen3 Next 80B A3B Instruct?

Like other API models, Qwen3 Next 80B A3B Instruct can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.

What is the context length for Qwen3 Next 80B A3B Instruct?

Reported context for Qwen3 Next 80B A3B Instruct is 131K tokens. Always verify the active provider row if multiple providers are listed.

Does Qwen3 Next 80B A3B Instruct support streaming?

Yes—at least one listed provider advertises streaming.

How much VRAM does Qwen3 Next 80B A3B Instruct need?

About 162.6 GB at BF16 (estimate), 82.1 GB for the official FP8 weights, about 49 GB for AWQ 4-bit or GGUF Q4_K_M, plus memory for the KV cache.

How do I run Qwen3 Next 80B A3B Instruct with vLLM?

Run: vllm serve Qwen/Qwen3-Next-80B-A3B-Instruct --tensor-parallel-size 4 --max-model-len 262144 (official model card command).

Is there a GGUF or AWQ version of Qwen3 Next 80B A3B Instruct?

Yes. Community GGUF files (bartowski) range from 28.2 GB (Q2_K) to 84.8 GB (Q8_0); an AWQ 4-bit build is 49.2 GB; an official FP8 build is 82.1 GB.

Can I run Qwen3 Next 80B A3B Instruct on an RTX 4090?

Not on one card: even Q2_K (28.2 GB) exceeds 24 GB. It takes 2–3 cards for low-bit GGUF, or CPU offload. Alternatively call it through LLM.API without GPUs.

Which endpoint do I call for Qwen3 Next 80B A3B Instruct?

POST https://api.llmapi.ai/v1/chat/completions with model "qwen3-next-80b-a3b-instruct" and your LLM.API key. The request and response bodies are OpenAI-compatible.

How do I switch from OpenAI to Qwen3 Next 80B A3B Instruct 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 Qwen3 Next 80B A3B Instruct support function calling and structured outputs?

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

Qwen3 Next 80B A3B Instruct: Self-Hosting & Deployment Guide

Call Qwen3 Next 80B A3B Instruct through LLM.API when you want Alibaba-family text generation with unified auth, provider choice, and production-friendly defaults.

Start Using API

What is Qwen3 Next 80B A3B Instruct?

Qwen3 Next 80B A3B Instruct belongs to the Alibaba family and is offered as a hosted chat endpoint. qwen3-next-80b-a3b-instruct provided by alibaba, novita. 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 Qwen3 Next 80B A3B Instruct yourself

Open weights (apache-2.0), 81.3B parameters. Below: how much memory the weights need at each quantization, which hardware fits them, and the commands to serve it.

Qwen3 Next 80B A3B Instruct VRAM requirements

Precision / quantization	Weights	Figure	Source
BF16 (full precision)	162.6 GB	Estimate	81.3B params × 2 bytes
FP8	82.1 GB	File size	Qwen/Qwen3-Next-80B-A3B-Instruct-FP8
AWQ 4-bit	49.2 GB	File size	cyankiwi/Qwen3-Next-80B-A3B-Instruct-AWQ-4bit
MLX 4-bit	44.8 GB	File size	lmstudio-community/Qwen3-Next-80B-A3B-Instruct-MLX-4bit
GGUF Q8_0	84.8 GB	File size	unsloth/Qwen3-Next-80B-A3B-Instruct-GGUF
GGUF Q4_K_M	48.5 GB	File size	unsloth/Qwen3-Next-80B-A3B-Instruct-GGUF
GGUF Q2_K	29.2 GB	File size	unsloth/Qwen3-Next-80B-A3B-Instruct-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 Qwen3 Next 80B A3B Instruct on your hardware

Datacenter GPUsConsumer GPUsApple SiliconCloud instances

Hardware	BF16 (full precision)	FP8	AWQ 4-bit	MLX 4-bit	GGUF Q8_0	GGUF Q4_K_M	GGUF Q2_K
NVIDIA H200141 GB	2×	1×	1×	1×	1×	1×	1×
NVIDIA H10080 GB	3×	2×	1×	1×	2×	1×	1×
NVIDIA A100 80GB80 GB	3×	2×	1×	1×	2×	1×	1×
NVIDIA L40S48 GB	4×	2×	2×	2×	3×	2×	1×

GPUs needed per variant.

Hardware	GGUF Q8_0	GGUF Q4_K_M	GGUF Q2_K
RTX 509032 GB	4×	2×	2×
RTX 409024 GB	5×	3×	2×
RTX 309024 GB	5×	3×	2×

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

Hardware	GGUF Q8_0	GGUF Q4_K_M	GGUF Q2_K	MLX 4-bit
Mac, 512 GBM3 Ultra	Fits	Fits	Fits	Fits
Mac, 192 GBM2 Ultra	Fits	Fits	Fits	Fits
Mac, 128 GBM4 Max / M3 Max	—	Fits	Fits	Fits
Mac, 64 GBM4 Pro / Max	—	—	Fits	—

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

Hardware	BF16 (full precision)	FP8	AWQ 4-bit	MLX 4-bit	GGUF Q8_0	GGUF Q4_K_M	GGUF Q2_K
AWS p5.48xlarge8× H100 · 640 GB	Fits	Fits	Fits	Fits	Fits	Fits	Fits
AWS p4de.24xlarge8× A100 80GB · 640 GB	Fits	Fits	Fits	Fits	Fits	Fits	Fits
AWS g6e.12xlarge4× L40S · 192 GB	Fits	Fits	Fits	Fits	Fits	Fits	Fits
Google Cloud a3-highgpu-8g8× H100 · 640 GB	Fits	Fits	Fits	Fits	Fits	Fits	Fits
Azure ND H100 v58× H100 · 640 GB	Fits	Fits	Fits	Fits	Fits	Fits	Fits

Whole-instance GPU memory; public instance specs.

Serve it yourself

vLLMSGLangllama.cpp (GGUF)MLX (Mac)

pip install -U vllm
vllm serve Qwen/Qwen3-Next-80B-A3B-Instruct

pip install 'sglang[all]'
python -m sglang.launch_server --model-path Qwen/Qwen3-Next-80B-A3B-Instruct

llama-server -hf unsloth/Qwen3-Next-80B-A3B-Instruct-GGUF:Q4_K_M \
  --ctx-size 32768 --n-gpu-layers 99

pip install -U mlx-lm
mlx_lm.generate --model lmstudio-community/Qwen3-Next-80B-A3B-Instruct-MLX-4bit --prompt "Hello"

Commands use the official repositories above; vLLM and SGLang expose an OpenAI-compatible endpoint.

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 Qwen3 Next 80B A3B Instruct to the providers below, with discounted effective rates versus list price.

List price by provider

InputOutput

alibaba$0.5 in

$2 out

novita$0.15 in

$1.5 out

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

Provider	Pricing	Context	Capabilities
alibaba	in $0.5; out $2 per 1M tokens	131K tokens	tools, streaming, JSON, structured
novita	in $0.15; out $1.5 per 1M tokens	131K tokens	tools, streaming, JSON

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

Try this model

Test Qwen3 Next 80B A3B Instruct right here — free to start.

Qwen3 Next 80B A3B Instruct

Hi! Want to test the model?

Suggestions for your first prompt

Skip the GPUs: call it via API

Call Qwen3 Next 80B A3B Instruct 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 qwen3-next-80b-a3b-instruct 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": "qwen3-next-80b-a3b-instruct",
    "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="qwen3-next-80b-a3b-instruct",
    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: "qwen3-next-80b-a3b-instruct",
  messages: [
    { role: "user", content: "Give me three crisp launch checklist items." },
  ],
});

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

stream = client.chat.completions.create(
    model="qwen3-next-80b-a3b-instruct",
    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": "qwen3-next-80b-a3b-instruct",
  "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 qwen3-next-80b-a3b-instruct · 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.

Qwen3 Next 80B A3B Instruct in production

Best workloadsLLM.API features

What the model is good at.

Structured outputs
Can produce JSON-friendly or schema-oriented responses when prompted carefully. Tuned to how teams typically call Qwen3 Next 80B A3B Instruct.
Conversational UX
Maintains coherent multi-turn assistant behavior for product chat surfaces.
Instruction following
Follows detailed system and user instructions with strong adherence to format and tone. Grounded in the model's chat role rather than generic chat claims.
Analytical writing
Produces clear analyses, comparisons, and decision memos from messy source material. Tuned to how teams typically call Qwen3 Next 80B A3B Instruct.
Long-context synthesis
Summarizes and cross-references information across large prompts when context allows. Tuned to how teams typically call Qwen3 Next 80B A3B Instruct.

What you get when you call Qwen3 Next 80B A3B Instruct through LLM.API.

Unified AI Routing
Practical: Reach Qwen3 Next 80B A3B Instruct and sibling models through one OpenAI-compatible endpoint.
One endpoint, every model.
Cost Control
Compare provider price points and keep spend visible as you scale Qwen3 Next 80B A3B Instruct.
More performance, less spend.
Reliability Layer
Practical: Retry and route across configured providers when a single upstream blips.
Never go dark on users.
Observability
Trace prompts, tokens, and errors for Qwen3 Next 80B A3B Instruct 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
Production: Swap Qwen3 Next 80B A3B Instruct 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 want OpenAI-compatible chat completions through a single LLM.API key (Qwen3 Next 80B A3B Instruct)
You need provider failover options exposed for this model id (Qwen3 Next 80B A3B Instruct)
You need a general-purpose text model for assistants, agents, or content workflows (Qwen3 Next 80B A3B Instruct)
Your prompts benefit from the model's family strengths (reasoning, speed, or cost) (Qwen3 Next 80B A3B Instruct)

Avoid if...

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

LLM API rating for Qwen3 Next 80B A3B Instruct

★ 3.6/ 5.0LLM API rating
50% production adoption · 30% category benchmark · 20% tooling & value✓ Verified benchmark · LMArena + Artificial Analysis

4.2Production adoptionStrong open-weight adoption

2.5Benchmark qualityArtificial Analysis Intelligence Index 11.2 · LMArena text rank #117, Elo 1417

3.7Developer ecosystemContext window, tool and JSON support, price against the category median

Reasoning & chat 3.6Coding 3.6Tool & function calling 4.2

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.

BENCHMARKS

Qwen3 Next 80B A3B Instruct benchmark scores

Intelligence index

This model10.

Tracked median7.

Scale: 0-100 index points

Output speed

This model181 t/s

Scale: 0-400 tokens per second

Reference price per 1M tokens

Input$0.15

Output$1.20

Bars compare input and output list prices for this model.

Intelligence index+43% vs median

Qwen3 Next 80B A3B Instruct: Self-Hosting & Deployment Guide10

Median tracked model7

Higher is better. The median covers every model tracked by Artificial Analysis.

Output speed

181tok/s

Measured throughput; the full dial is 300 tokens per second.

Reference price / 1M tokens

Input$0.15

Output$1.20

Provider list prices, before the LLM.API discount.

Independent scores published by Artificial Analysis (source). Reference prices are provider list prices, not LLM.API pricing.

COMMUNITY

What developers say about Qwen models

LMArena#117 · Elo 1417Arena coding#110 · Elo 1441Speed181 tokens/sSources: 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 Qwen3 Next 80B A3B Instruct.

Qwen is widely described as the strongest open-weight coding family, with reviewers testing whether local Qwen can replace hosted frontier assistants for agentic coding.
Benchmarks published by developer blogs show production-usable code and solid architectural reasoning, especially in the Coder variants.
The honest verdict in most write-ups: excellent value and privacy, still behind the top proprietary models on the hardest long-horizon tasks.

SOURCES

Frequently Asked Questions

How is Qwen3 Next 80B A3B Instruct priced on LLM.API?
Listed pricing metadata shows: In $0.5 / 1M tokens · Out $2.00 / 1M tokens. LLM.API may offer discounted effective rates (illustrative ~30% callout vs list when available).. Confirm live rates in the LLM.API dashboard or docs before production budgeting.
What modalities does Qwen3 Next 80B A3B Instruct support?
Qwen3 Next 80B A3B Instruct accepts text and produces text according to its architecture metadata on LLM.API.
Can I use tools or structured outputs with Qwen3 Next 80B A3B Instruct?
Tool and/or structured-output flags appear on one or more providers for this model—confirm in the providers table.
What is Qwen3 Next 80B A3B Instruct?
qwen3-next-80b-a3b-instruct provided by alibaba, novita. It is wired for API access through LLM.API with OpenAI-compatible patterns. On LLM.API it is addressed as `qwen3-next-80b-a3b-instruct`.
Where is the canonical page for Qwen3 Next 80B A3B Instruct?
https://llmapi.ai/models/alibaba-qwen3-next-80b-a3b-instruct/
When should I choose Qwen3 Next 80B A3B Instruct?
You need a general-purpose text model for assistants, agents, or content workflows — especially when you specifically need Qwen3 Next 80B A3B Instruct.
How do I call Qwen3 Next 80B A3B Instruct via API?
Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "qwen3-next-80b-a3b-instruct" and your LLM.API key. See the code snippet on this page.
What are limitations of Qwen3 Next 80B A3B Instruct?
Like other API models, Qwen3 Next 80B A3B Instruct can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.
What is the context length for Qwen3 Next 80B A3B Instruct?
Reported context for Qwen3 Next 80B A3B Instruct is 131K tokens. Always verify the active provider row if multiple providers are listed.
Does Qwen3 Next 80B A3B Instruct support streaming?
Yes—at least one listed provider advertises streaming.

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Qwen3 Next 80B A3B Instruct: Self-Hosting & Deployment Guide

What is Qwen3 Next 80B A3B Instruct?

How to run Qwen3 Next 80B A3B Instruct yourself

Qwen3 Next 80B A3B Instruct VRAM requirements

GPU requirements: deploy Qwen3 Next 80B A3B Instruct on your hardware

Serve it yourself

Providers

Try this model

Skip the GPUs: call it via API

Supported endpoints & features

Errors & fallback routing

Qwen3 Next 80B A3B Instruct in production

Structured outputs

Conversational UX

Instruction following

Analytical writing

Long-context synthesis

Unified AI Routing

Cost Control

Reliability Layer

Observability

Drop-in SDKs

Model Breadth

When to Use — When NOT to Use

Use it if...

Avoid if...

LLM API rating for Qwen3 Next 80B A3B Instruct

Qwen3 Next 80B A3B Instruct benchmark scores

Intelligence index

Output speed

Reference price per 1M tokens

What developers say about Qwen models

Frequently Asked Questions

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