Qwen3 Next 80B A3b Thinking

Alibaba DashScope chat

Qwen3 Next 80B A3b Thinking is an Alibaba DashScope chat model.It supports a 262,144-token context windowwith up to 65,536 output tokens.Input is priced at $0.150/M tokens and output at $1.20/M tokens. Capabilities include function calling, reasoning. Route Qwen3 Next 80B A3b Thinking via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.

Pricing source: litellm Last verified: Aug 6, 2026 View source ↗
Cost calculator

Estimate Qwen3 Next 80B A3b Thinking spend

Pick a workload, fine-tune the sliders, and see the monthly bill.

~3K in / ~400 out · 5K req/day
3,000
0262,144
400
065,536
5,000
01,000,000
Per request
$0.000930
in $0.000450 · out $0.000480
Per day
$4.65
5,000 requests
Per month
$142
152,188 requests

Estimate uses $0.1500/M input · $1.20/M output. Provider pricing changes. Production costs vary with retries, streaming overhead, and tool-call rounds.
Want this for free? Cache + route via Agent Command Center — first 100K requests and 100K cache hits free every month.

Pricing

Per-token rates, expressed in USD per 1M tokens. Verified Aug 6, 2026.

Input $0.150/M
Output $1.20/M
Blended (3:1 in:out) $0.413/M
Cross-model comparable price

Context & limits

Context window
262,144 tokens
Max input
262,144 tokens
Max output
65,536 tokens
Modalities
text

Capabilities

  • Function calling ✓ supported
  • Parallel tool calls — not advertised
  • Vision input — not advertised
  • Audio input — not advertised
  • Audio output — not advertised
  • PDF input — not advertised
  • Streaming ✓ supported
  • Structured output — not advertised
  • Prompt caching — not advertised
  • Reasoning ✓ supported

Strengths & caveats — when to pick Qwen3 Next 80B A3b Thinking

Data-driven from Qwen3 Next 80B A3b Thinking's percentile within chat peers.

Where it's strong

  • +multi-step reasoning and analysis tasks
  • +agentic workflows that depend on reliable tool calls

Watch out for

  • !strict structured output — no JSON-schema enforcement, expect retry loops

Benchmark scores

Reported public benchmark numbers. Each row links to the source.

Chatbot Arena ELOgeneral· overall
Captured Aug 8, 2026
Try it

Call Qwen3 Next 80B A3b Thinking via Agent Command Center

One OpenAI-compatible endpoint. Routing, fallback, semantic caching, guardrails, and cost tracking come along for the ride. First 100K requests + 100K cache hits free every month.

SDK
Native Future AGI client (agentcc / @agentcc/client). Per-call metadata — provider, cost, latency, cache hit, request id — is returned on x-agentcc-* response headers, so any HTTP client can read it.
# Qwen3 Next 80B A3b Thinking via the Agent Command Center Python SDK
# pip install agentcc
import os
from agentcc import AgentCC

client = AgentCC(
    api_key=os.environ["AGENTCC_API_KEY"],   # from app.futureagi.com → Settings → API Keys
    base_url="https://gateway.futureagi.com/v1",
)

resp = client.chat.completions.create(
    model="dashscope/qwen3-next-80b-a3b-thinking",
    messages=[{"role": "user", "content": "Hello, Qwen3 Next 80B A3b Thinking!"}],
)

print(resp.choices[0].message.content)
print(f"Tokens: {resp.usage.total_tokens}")

# Per-call gateway metadata is returned on x-agentcc-* response headers.
# When you need it programmatically, use .with_raw_response to get them:
raw = client.chat.completions.with_raw_response.create(
    model="dashscope/qwen3-next-80b-a3b-thinking",
    messages=[{"role": "user", "content": "Same call, but I want the headers."}],
)
print("Provider:", raw.headers.get("x-agentcc-provider"))
print("Latency:", raw.headers.get("x-agentcc-latency-ms"), "ms")
print("Cost:   ", raw.headers.get("x-agentcc-cost"), "USD")
print("Cache:  ", raw.headers.get("x-agentcc-cache"))
Set AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗
Advanced: fallback + cache config (YAML)
strategy: cost-optimized
targets:
  - model: qwen3-next-80b-a3b-thinking
    provider: dashscope
    weight: 80
fallbacks:
  - model: gpt-5-chat
    provider: azure-openai
  - model: mistral-medium-3-5
    provider: mistral
guardrails: [pii, prompt-injection, secrets]
cache: { exact: true, semantic: true }

Compare with similar models

Grouped by Chatbot Arena tier (Qwen3 Next 80B A3b Thinking sits at 1369 ELO).

FAQ

How much does Qwen3 Next 80B A3b Thinking cost?

Input is priced at $0.150 per 1M tokens and output at $1.20 per 1M tokens (Alibaba DashScope, last verified Aug 6, 2026).

What is the context window of Qwen3 Next 80B A3b Thinking?

Qwen3 Next 80B A3b Thinking supports a 262,144-token context window with up to 65,536 output tokens.

Does Qwen3 Next 80B A3b Thinking support function calling?

Yes — Qwen3 Next 80B A3b Thinking supports function (tool) calling.

Is Qwen3 Next 80B A3b Thinking good for production?

Qwen3 Next 80B A3b Thinking is well-suited for multi-step reasoning and analysis tasks and agentic workflows that depend on reliable tool calls. Consider alternatives if you need strict structured output — no JSON-schema enforcement, expect retry loops.

How can I route to Qwen3 Next 80B A3b Thinking with fallback?

Use Agent Command Center: a single OpenAI-compatible endpoint that supports cost-optimized routing, latency-aware retries, model fallback, and shadow traffic. Configure once, swap models without app changes.

Useful links for Qwen3 Next 80B A3b Thinking

Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.