Qwen-Plus (2025-07-28)
Alibaba DashScope chatQwen-Plus (2025-07-28) is an Alibaba DashScope chat model.It supports a 997,952-token context windowwith up to 32,768 output tokens. Capabilities include function calling, reasoning. Route Qwen-Plus (2025-07-28) 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 Qwen-Plus (2025-07-28) 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
- 997,952 tokens
- Max input
- 997,952 tokens
- Max output
- 32,768 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 Qwen-Plus (2025-07-28)
Data-driven from Qwen-Plus (2025-07-28)'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 Qwen-Plus (2025-07-28) 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.# Qwen-Plus (2025-07-28) 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/qwen-plus-2025-07-28",
messages=[{"role": "user", "content": "Hello, Qwen-Plus (2025-07-28)!"}],
)
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/qwen-plus-2025-07-28",
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: qwen-plus-2025-07-28
provider: dashscope
weight: 80
fallbacks:
- model: gemini-2-5-flash-preview-09-2025
provider: vertex-ai
- model: o1
provider: azure-openai
guardrails: [pii, prompt-injection, secrets]
cache: { exact: true, semantic: true } Explore similar models
Grouped by Chatbot Arena tier (Qwen-Plus (2025-07-28) sits at 1346 ELO). Side-by-side links appear where a comparison page exists; otherwise, open the model profile.
- 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 →
- Qwen3.8 Omni FlashAlibaba DashScope · $0.150/M in · $0.470/M out · 991,808 ctxView model →
- Gemini 2.5 Flash preview 09 2025Google Vertex AI · $0.300/M in · $2.50/M out · 1,048,576 ctxView model →
- o1Azure OpenAI · $15.00/M in · $60.00/M out · 200,000 ctxView model →
- o1 (2024-12-17)Azure OpenAI · $15.00/M in · $60.00/M out · 200,000 ctxView model →
- GPT-5.4 nano (2026-03-17)Azure AI Foundry · $0.200/M in · $1.25/M out · 272,000 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 Qwen-Plus (2025-07-28) cost?
Public per-token pricing for Qwen-Plus (2025-07-28) is not yet published. Submit a source on this page to help us add it.
What is the context window of Qwen-Plus (2025-07-28)?
Qwen-Plus (2025-07-28) supports a 997,952-token context window with up to 32,768 output tokens.
Does Qwen-Plus (2025-07-28) support function calling?
Yes — Qwen-Plus (2025-07-28) supports function (tool) calling.
Is Qwen-Plus (2025-07-28) good for production?
Qwen-Plus (2025-07-28) 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 Qwen-Plus (2025-07-28) 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 Qwen-Plus (2025-07-28)
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.