Qwen3 30B A3B
Alibaba DashScope chatQwen3 30B A3B is an Alibaba DashScope chat model.It supports a 129,024-token context windowwith up to 16,384 output tokens. Capabilities include function calling, reasoning. Route Qwen3 30B A3B via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
We don't have verified per-token pricing for Qwen3 30B A3B yet. If you have a source from Alibaba DashScope's documentation, help us add it — your submission gets reviewed within 48 hours.
Pricing
Per-token rates, expressed in USD per 1M tokens. Verified Oct 22, 2026.
| Input | — | |
| Output | — |
Context & limits
- Context window
- 129,024 tokens
- Max input
- 129,024 tokens
- Max output
- 16,384 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 30B A3B
Data-driven from Qwen3 30B A3B'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. Faded bar shows 6-peer average for context.
Call Qwen3 30B A3B 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 30B A3B 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/qwen3-30b-a3b",
messages=[{"role": "user", "content": "Hello, Qwen3 30B A3B!"}],
)
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/qwen3-30b-a3b",
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: qwen3-30b-a3b
provider: dashscope
weight: 80
fallbacks:
- model: mistral-medium-2505
provider: azure-ai-foundry
- model: gpt-4-1-mini-2025-04-14
provider: azure-openai
guardrails: [pii, prompt-injection, secrets]
cache: { exact: true, semantic: true } Explore similar models
Grouped by Chatbot Arena tier (Qwen3 30B A3B sits at 1327 ELO). Side-by-side links appear where a comparison page exists; otherwise, open the model profile.
- Qwen-Plus (2025-07-14)Alibaba DashScope · $0.400/M in · $1.20/M out · 129,024 ctxView model →
- Qwen-Turbo (2025-04-28)Alibaba DashScope · $0.0500/M in · $0.200/M out · 1,000,000 ctxView model →
- Qwen3.8 FlashAlibaba DashScope · $0.150/M in · $0.470/M out · 991,808 ctxView model →
- Qwen3.8 MaxAlibaba DashScope · $2.00/M in · $6.00/M out · 991,808 ctxView model →
- Mistral Medium 2505Azure AI Foundry · $0.400/M in · $2.00/M out · 131,072 ctxView model →
- GPT-4.1 mini (2025-04-14)Azure OpenAI · $0.400/M in · $1.60/M out · 1,047,576 ctxView model →
- Gemini 2.5 Flash LiteGoogle AI · $0.1000/M in · $0.400/M out · 1,048,576 ctxView model →
- Claude 3.5 Sonnet latestAnthropic · $3.00/M in · $15.00/M out · 200,000 ctxView model →
≥30 ELO higher
- Gemini 4 ArgonGoogle AI · $2.00/M in · $10.00/M out · 1,048,576 ctxView model →
- Claude Opus 5.5Azure AI Foundry · $4.00/M in · $20.00/M out · 1,000,000 ctxView model →
- Claude Fable 5Azure AI Foundry · $10.00/M in · $50.00/M out · 1,000,000 ctxView model →
- Claude Fable 5.1Azure AI Foundry · $10.00/M in · $50.00/M out · 1,000,000 ctxView model →
FAQ
How much does Qwen3 30B A3B cost?
Public per-token pricing for Qwen3 30B A3B is not yet published. Submit a source on this page to help us add it.
What is the context window of Qwen3 30B A3B?
Qwen3 30B A3B supports a 129,024-token context window with up to 16,384 output tokens.
Does Qwen3 30B A3B support function calling?
Yes — Qwen3 30B A3B supports function (tool) calling.
Is Qwen3 30B A3B good for production?
Qwen3 30B A3B 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 30B A3B 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 30B A3B
Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.
Third-party evals — verify the marketing.
Cross-check our number against the rest of the ecosystem.