Qwen3 VL Plus
Alibaba DashScope chatQwen3 VL Plus is an Alibaba DashScope chat model.It supports a 260,096-token context windowwith up to 32,768 output tokens. Capabilities include function calling, vision, reasoning. Route Qwen3 VL Plus 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 VL Plus 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 May 12, 2026.
| Input | — | |
| Output | — |
Limits
- Context window
- 260,096 tokens
- Max input
- 260,096 tokens
- Max output
- 32,768 tokens
- Modalities
- vision, text
Capabilities
- Function calling ✓ supported
- Parallel tool calls — not advertised
- Vision input ✓ supported
- Audio input — not advertised
- Audio output — not advertised
- PDF input — not advertised
- Streaming ✓ supported
- Structured output — not advertised
- Prompt caching — not advertised
- Reasoning ✓ supported
Where it's strong
- +multi-step reasoning and analysis tasks
- +agentic workflows that depend on reliable tool calls
- +document, chart, and screenshot understanding
Watch out for
- !strict structured output — no JSON-schema enforcement, expect retry loops
Benchmarks pending
We haven't logged public benchmark scores for Qwen3 VL Plus yet. Have one to contribute? Submit a source — citations help us prioritise.
Call Qwen3 VL Plus 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 VL Plus 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-vl-plus",
messages=[{"role": "user", "content": "Hello, Qwen3 VL Plus!"}],
)
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-vl-plus",
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 ↗Compare with similar models
Qwen3 VL Plus doesn't have a public Arena ELO score yet, so we group by provider only — quality-tier comparisons need a benchmark.
- 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 VL 235B A22b ThinkingAlibaba DashScope · $0.400/M in · $4.00/M out · 131,072 ctx
- Qwen3 VL 32B ThinkingAlibaba DashScope · $0.160/M in · $2.87/M out · 131,072 ctx
FAQ
How much does Qwen3 VL Plus cost?
Public per-token pricing for Qwen3 VL Plus is not yet published. Submit a source on this page to help us add it.
What is the context window of Qwen3 VL Plus?
Qwen3 VL Plus supports a 260,096-token context window with up to 32,768 output tokens.
Does Qwen3 VL Plus support function calling?
Yes — Qwen3 VL Plus supports function (tool) calling.
Is Qwen3 VL Plus good for production?
Qwen3 VL Plus 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 VL Plus 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 VL Plus
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.