Qwen3 Next 80B A3b Instruct
Alibaba DashScope chatQwen3 Next 80B A3b Instruct 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. Route Qwen3 Next 80B A3b Instruct via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate Qwen3 Next 80B A3b Instruct spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
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 — not advertised
Strengths & caveats — when to pick Qwen3 Next 80B A3b Instruct
Data-driven from Qwen3 Next 80B A3b Instruct's percentile within chat peers.
Where it's strong
- +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.
Call Qwen3 Next 80B A3b Instruct 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.
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 Instruct 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-instruct",
messages=[{"role": "user", "content": "Hello, Qwen3 Next 80B A3b Instruct!"}],
)
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-instruct",
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"))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-instruct
provider: dashscope
weight: 80
fallbacks:
- model: kimi-k2-6
provider: moonshot
- model: grok-4
provider: azure-ai-foundry
guardrails: [pii, prompt-injection, secrets]
cache: { exact: true, semantic: true } Compare with similar models
Grouped by Chatbot Arena tier (Qwen3 Next 80B A3b Instruct sits at 1401 ELO).
- Qwen-Plus (2025-07-14)Alibaba DashScope · $0.400/M in · $1.20/M out · 129,024 ctx
- Qwen-Turbo (2025-04-28)Alibaba DashScope · $0.0500/M in · $0.200/M out · 1,000,000 ctx
- Qwen3.7 MaxAlibaba DashScope · $2.50/M in · $7.50/M out · 991,808 ctx
- Qwen3.8 MaxAlibaba DashScope · $2.00/M in · $6.00/M out · 1,000,000 ctx
≥30 ELO higher
- Claude Fable 5Azure AI Foundry · $10.00/M in · $50.00/M out · 1,000,000 ctx
- Claude Opus 4.6Azure AI Foundry · $5.00/M in · $25.00/M out · 1,000,000 ctx
- Claude Opus 4.6 (2026-02-05)Anthropic · $5.00/M in · $25.00/M out · 1,000,000 ctx
- Qwen3.8 MaxAlibaba DashScope · $2.00/M in · $6.00/M out · 1,000,000 ctx
25–100 ELO lower, ≤50% of price
FAQ
How much does Qwen3 Next 80B A3b Instruct 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 Instruct?
Qwen3 Next 80B A3b Instruct supports a 262,144-token context window with up to 65,536 output tokens.
Does Qwen3 Next 80B A3b Instruct support function calling?
Yes — Qwen3 Next 80B A3b Instruct supports function (tool) calling.
Is Qwen3 Next 80B A3b Instruct good for production?
Qwen3 Next 80B A3b Instruct is well-suited for 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 Instruct 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 Instruct
Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.
Start here — vendor docs are the only authoritative spec.
Third-party evals — verify the marketing.
Cross-check our number against the rest of the ecosystem.