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Qwen3 Next 80B A3B Instruct

Up to 30%

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.

Providers

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

List price by provider ($ / 1M tokens)

InputOutput
alibaba$0.5 in
$2 out
novita$0.15 in
$1.5 out

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

ProviderPricingContextCapabilities
alibaba30% offin $500; out $2000 per 1M tokens131K tokenstools, streaming, JSON, structured
novita30% offin $150; out $1500 per 1M tokens131K tokenstools, streaming, JSON

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 21 Sept 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

Code snippet

Call Qwen3 Next 80B A3B Instruct through the OpenAI-compatible API — POST /v1/chat/completions.

python
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMAP_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)
{
  "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."}
  ]
}

5 Core Capabilities

  • 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.

6 Most Valuable Use Cases

  • Customer support copilots that draft accurate, on-brand replies with Qwen3 Next 80B A3B Instruct
  • Policy Q&A bots with careful refusal behavior
  • Data extraction into JSON for downstream systems with Qwen3 Next 80B A3B Instruct
  • Internal knowledge assistants grounded with your retrieval layer
  • Product analytics narration and anomaly explanations with Qwen3 Next 80B A3B Instruct
  • Meeting-note cleanup and action-item generation

Why Build on LLM.API?

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

  • Intelligent AI Routing

    Automatically route each request to the best model across providers based on latency, cost, and quality—without changing your integration or redeploying code.

    One endpoint, every model.

  • Cost-Aware Execution

    Control spend with per-request cost estimation, smart model selection, and centralized quotas so teams can experiment fast without runaway bills or manual tracking.

    More performance, less spend.

  • Resilient Fallback Flows

    Define automatic, provider-agnostic fallbacks to keep your app up during outages, rate limits, or timeouts—no brittle failover logic scattered through your codebase.

    Never go dark on users.

  • Deep LLM Observability

    Trace every call across providers with logs, metrics, and request replay so you can debug, tune prompts, and optimize model choices from one unified dashboard.

    See every token, everywhere.

  • Task-Level Orchestration

    Describe tasks, not models. LLM.API maps them to the right tools, models, and prompts so you ship complex AI workflows with minimal glue code.

    Think tasks, not models.

  • High-Throughput Batch APIs

    Process millions of inferences efficiently with optimized batch pipelines, concurrency controls, and retry logic—all behind the same simple interface you use for single calls.

    Scale from 1 to millions.

Why run Qwen3 Next 80B A3B Instruct on LLM.API?

  • Unified AI Routing

    Practical: Reach Qwen3 Next 80B A3B Instruct and sibling models through one OpenAI-compatible endpoint.

  • Cost Control

    Compare provider price points and keep spend visible as you scale Qwen3 Next 80B A3B Instruct.

  • Reliability Layer

    Practical: Retry and route across configured providers when a single upstream blips.

  • Observability

    Trace prompts, tokens, and errors for Qwen3 Next 80B A3B Instruct alongside the rest of your stack.

  • Drop-in SDKs

    Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.

  • Model Breadth

    Production: Swap Qwen3 Next 80B A3B Instruct for chat, media, or embedding alternatives without rewriting auth.

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

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.

Artificial Analysis Intelligence Index10
Median index across all tracked models7
Output speed181 tokens/s
Reference input price$0.15 / 1M tokens
Reference output price$1.20 / 1M tokens

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

COMMUNITY

What developers say about Qwen models

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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