Qwen-VL Max vs Qwen Plus
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen-VL Max by Alibaba and Qwen Plus by Alibaba represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Qwen Plus holds a notable pricing advantage for token ingestion. On output generation, Qwen Plus is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Qwen Plus provides an expanded memory horizon with 1,000K tokens. In latency-sensitive production environments, Qwen Plus leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Qwen-VL Max
深度推理模型Alibaba 推出的 Qwen-VL Max 人工智能模型，具备 131K 上下文窗口，输入价格为 $0.7/百万 Token，输出价格为 $2.8/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.70 / 1M
$2.80 / 1M
131.1K tokens
N/A
Qwen Plus
深度推理模型Alibaba 推出的 Qwen Plus 人工智能模型，具备 1M 上下文窗口，输入价格为 $0.4/百万 Token，输出价格为 $1.2/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.40 / 1M
$1.20 / 1M
1M tokens
N/A
核心差异一览
- Qwen Plus is 2.1x cheaper for API usage (input + output tokens combined).
- Qwen Plus has a larger context window (1M vs 131K tokens).
- Qwen Plus is faster at 95 tok/s vs 75 tok/s.
| Feature | Qwen-VL Max | Qwen Plus |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 76 | 75 |
Provider | Alibaba | Alibaba |
Input Price Cost per million input tokens | $0.70 / 1M | $0.40 / 1M |
Output Price Cost per million output tokens | $2.80 / 1M | $1.20 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.47 / 1M | $0.23 / 1M |
Context Window Maximum input + output tokens | 131.1K tokens~197 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 8K | 8K |
Throughput Speed Tokens generated per second | 75 tok/s | 95 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
GPQA Diamond Graduate-level reasoning | 46.5% | 42.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.07 / 1M | $0.04 / 1M |
各项指标谁更胜一筹？
At-a-glance decision frameworkEvaluations ongoing
权威基准测试得分对比
Official evaluation metrics verified by independent third-party research labs.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Tokens emitted per second during active generation streaming.
延迟与响应速度
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen-VL Max · Strengths & Key Advantages
- 由 Alibaba 研发的高精度 Multimodal Vision-Language Transformer 架构，支持 131K 上下文窗口
- 极具竞争力的 API 定价（输入 $0.7/1M，输出 $2.8/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Qwen Plus · Strengths & Key Advantages
- 由 Alibaba 研发的高精度 High-Throughput Dense Autoregressive Transformer 架构，支持 1M 上下文窗口
- 极具竞争力的 API 定价（输入 $0.4/1M，输出 $1.2/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen-VL Max | Qwen Plus |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.15 | $0.08 |
Output Tokens / Task Tokens generated per task | 16K | 15K |
Reasoning Tokens / Task Thinking tokens burned per task | 5K | 4K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen-VL Max | Qwen Plus |
|---|---|---|
| Official API Model ID (SDK) | qwen-vl-max-instruct | qwen-plus-instruct |
| API Protocol | Alibaba REST API (OpenAI-Compatible) | Alibaba REST API (OpenAI-Compatible) |
| Release Date | 2024-04 | 2024-01 |
| Knowledge Cutoff | December 2023 | September 2024 |
| Architecture | Multimodal Vision-Language Transformer | High-Throughput Dense Autoregressive Transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.7/1M in) across full 131K window | Flat rate ($0.4/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Cached input at $0.07/1M tokens | Cached input at $0.04/1M tokens |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | API Prompt Engineering & RAG Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Alibaba Production Safety & Alignment Verified | Alibaba Production Safety & Alignment Verified |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision | text |
| Output Modalities | text | text |
Qwen-VL Max vs Qwen Plus: 常见问题解答 (FAQ)
Which is more intelligent, Qwen-VL Max or Qwen Plus?
Qwen-VL Max is more intelligent. It scores 76 compared with 75 on the composite Intelligence Index.
Which model is faster?
Qwen Plus is faster, generating 95 tokens per second compared with 75 tokens per second.
Which model is cheaper to run?
Qwen Plus is cheaper at $0.23 per 1M tokens blended, compared with $0.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen Plus has lower latency, with a time to first token of 0.42s compared with 0.65s.
Which model has a larger context window?
Qwen Plus has a larger context window, supporting 1M tokens compared with 131.1K tokens.
综合结论
Verdict & Model Selection Recommendation: Choose Qwen-VL Max if your priority is Invoice processing, complex document intelligence, visual inspection, and diagram QA. Its performance in demanding tasks justifies its operational token cost. Choose Qwen Plus if your workflow prioritizes High-volume document ingestion, long context summaries, and conversational bots. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
常见问题 (FAQ)
How does Qwen-VL Max compare to Qwen Plus in API token pricing and cost per token?+
Qwen-VL Max costs $0.70 per 1M input tokens and $2.80 per 1M output tokens, whereas Qwen Plus costs $0.40 input and $1.20 output. Overall, Qwen Plus is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Qwen-VL Max or Qwen Plus?+
Qwen-VL Max supports a context window of 131K tokens (~98k words), while Qwen Plus supports 1,000K tokens (~750k words). Therefore, Qwen Plus can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Qwen-VL Max or Qwen Plus?+
Qwen-VL Max scores at the frontier of coding benchmarks, while Qwen Plus scores competitively for software generation. For complex autonomous debugging and agentic engineering, Qwen-VL Max provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Qwen-VL Max and Qwen Plus?+
Qwen-VL Max features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Qwen Plus provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Qwen-VL Max and Qwen Plus?+
Qwen-VL Max generates approximately 75 tokens per second, while Qwen Plus delivers 95 tokens per second. In latency-sensitive production environments, Qwen Plus provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Qwen-VL Max and Qwen Plus to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Qwen Plus to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Qwen-VL Max. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
数据来源: GetInfoUs Independent AI Benchmarks & Official API Specs