Does Qwen3 Next 80B A3B Thinking support streaming?

Yes—at least one listed provider advertises streaming.

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

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

Which providers serve Qwen3 Next 80B A3B Thinking?

LLM.API currently lists: alibaba, novita. Availability can vary by region and account.

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

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

What are limitations of Qwen3 Next 80B A3B Thinking?

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

Is Qwen3 Next 80B A3B Thinking a chat model?

Yes—Qwen3 Next 80B A3B Thinking is exposed as a chat/completions-style endpoint on LLM.API.

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

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

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

Listed pricing metadata shows: In $0.5 / 1M tokens · Out $6.00 / 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.

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

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

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

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

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

Qwen3 Next 80B A3B Thinking brings Alibaba conversational intelligence to LLM.API so teams can ship assistants and agents without juggling multiple vendor SDKs.

Start Using API

What is Qwen3 Next 80B A3B Thinking?

On LLM.API, Qwen3 Next 80B A3B Thinking (qwen3-next-80b-a3b-thinking) serves as a Alibaba conversational model for products that need 131K tokens context windows and predictable token pricing. qwen3-next-80b-a3b-thinking provided by alibaba, novita. It is wired for API access through LLM.API with OpenAI-compatible patterns.

How to run Qwen3 Next 80B A3B Thinking 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 Thinking 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-Thinking-FP8
AWQ 4-bit	49.2 GB	File size	cyankiwi/Qwen3-Next-80B-A3B-Thinking-AWQ-4bit
MLX 4-bit	44.8 GB	File size	mlx-community/Qwen3-Next-80B-A3B-Thinking-4bit
GGUF Q8_0	84.8 GB	File size	unsloth/Qwen3-Next-80B-A3B-Thinking-GGUF
GGUF Q4_K_M	48.5 GB	File size	unsloth/Qwen3-Next-80B-A3B-Thinking-GGUF
GGUF Q2_K	29.2 GB	File size	unsloth/Qwen3-Next-80B-A3B-Thinking-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 Thinking 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-Thinking

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

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

pip install -U mlx-lm
mlx_lm.generate --model mlx-community/Qwen3-Next-80B-A3B-Thinking-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 Thinking to the providers below, with discounted effective rates versus list price.

List price by provider

InputOutput

alibaba$0.5 in

$6 out

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

Provider	Pricing	Context	Capabilities
alibaba	in $0.5; out $6 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 Qwen3 Next 80B A3B Thinking right here — free to start.

Qwen3 Next 80B A3B Thinking

Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call Qwen3 Next 80B A3B Thinking 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-thinking 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-thinking",
    "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-thinking",
    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-thinking",
  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-thinking",
    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-thinking",
  "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-thinking · 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 Thinking in production

Best workloadsLLM.API features

What the model is good at.

Safety-aware replies
Supports product policies with refusals and cautious handling of sensitive topics. Relevant for `qwen3-next-80b-a3b-thinking` workloads on LLM.API.
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.
Multi-step reasoning
Breaks down complex problems into intermediate steps before answering. Tuned to how teams typically call Qwen3 Next 80B A3B Thinking.
Tool-ready dialogue
Works well in agent loops that call functions, browsers, or retrieval APIs. Tuned to how teams typically call Qwen3 Next 80B A3B Thinking.
Multilingual drafting
Drafts and translates professional content across major business languages. Tuned to how teams typically call Qwen3 Next 80B A3B Thinking.

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

Unified AI Routing
Reach Qwen3 Next 80B A3B Thinking 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 Thinking.
More performance, less spend.
Reliability Layer
Retry and route across configured providers when a single upstream blips.
Never go dark on users.
Observability
Practical: Trace prompts, tokens, and errors for Qwen3 Next 80B A3B Thinking alongside the rest of your stack.
See every token, everywhere.
Drop-in SDKs
Production: Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.
Think tasks, not models.
Model Breadth
Practical: Swap Qwen3 Next 80B A3B Thinking for chat, media, or embedding alternatives without rewriting auth.
Scale from 1 to millions.

When to Use — When NOT to Use

Use it if...

Your prompts benefit from the model's family strengths (reasoning, speed, or cost) (Qwen3 Next 80B A3B Thinking)
You want OpenAI-compatible chat completions through a single LLM.API key (Qwen3 Next 80B A3B Thinking)
You need provider failover options exposed for this model id (Qwen3 Next 80B A3B Thinking)
You need a general-purpose text model for assistants, agents, or content workflows (Qwen3 Next 80B A3B Thinking)

Avoid if...

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

LLM API rating for Qwen3 Next 80B A3B Thinking

★ 3.7/ 5.0LLM API rating
50% production adoption · 30% category benchmark · 20% tooling & value✓ Verified benchmark · LMArena

4.2Production adoptionStrong open-weight adoption

3.1Benchmark qualityLMArena text rank #180, Elo 1368

3.5Developer ecosystemContext window, tool and JSON support, price against the category median

Reasoning & chat 3.7Coding 3.8Tool & 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.

COMMUNITY

What developers say about Qwen models

LMArena#180 · Elo 1368Arena coding#173 · Elo 1392Speed—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 Qwen3 Next 80B A3B Thinking.

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

Does Qwen3 Next 80B A3B Thinking support streaming?
Yes—at least one listed provider advertises streaming.
What is the context length for Qwen3 Next 80B A3B Thinking?
Reported context for Qwen3 Next 80B A3B Thinking is 131K tokens. Always verify the active provider row if multiple providers are listed.
Which providers serve Qwen3 Next 80B A3B Thinking?
LLM.API currently lists: alibaba, novita. Availability can vary by region and account.
Where is the canonical page for Qwen3 Next 80B A3B Thinking?
https://llmapi.ai/models/alibaba-qwen3-next-80b-a3b-thinking/
What are limitations of Qwen3 Next 80B A3B Thinking?
Like other API models, Qwen3 Next 80B A3B Thinking can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.
Is Qwen3 Next 80B A3B Thinking a chat model?
Yes—Qwen3 Next 80B A3B Thinking is exposed as a chat/completions-style endpoint on LLM.API.
Can I use tools or structured outputs with Qwen3 Next 80B A3B Thinking?
Tool and/or structured-output flags appear on one or more providers for this model—confirm in the providers table.
How is Qwen3 Next 80B A3B Thinking priced on LLM.API?
Listed pricing metadata shows: In $0.5 / 1M tokens · Out $6.00 / 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.

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

What is Qwen3 Next 80B A3B Thinking?

How to run Qwen3 Next 80B A3B Thinking yourself

Qwen3 Next 80B A3B Thinking VRAM requirements

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

Serve it yourself

Providers

Try this model

Code snippet

Supported endpoints & features

Errors & fallback routing

Qwen3 Next 80B A3B Thinking in production

Safety-aware replies

Instruction following

Multi-step reasoning

Tool-ready dialogue

Multilingual drafting

Unified AI Routing

Cost Control

Reliability Layer

Observability

Drop-in SDKs

Model Breadth