Qwen3.5 Plus vs Qwen3 Max
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen3.5 Plus by Alibaba and Qwen3 Max 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, Qwen3.5 Plus holds a notable pricing advantage for token ingestion. On output generation, Qwen3.5 Plus is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Qwen3.5 Plus provides an expanded memory horizon with 1,000K tokens. In latency-sensitive production environments, Qwen3.5 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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Qwen3.5 Plus
深度推理模型Alibaba 推出的 Qwen3.5 Plus 人工智能模型，具备 1M 上下文窗口，输入价格为 $0.6/百万 Token，输出价格为 $2.4/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.60 / 1M
$2.40 / 1M
1M tokens
64%
Qwen3 Max
深度推理模型Alibaba 推出的 Qwen3-Max 人工智能模型，具备 262K 上下文窗口，输入价格为 $2.0/百万 Token，输出价格为 $6.0/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$2.00 / 1M
$6.00 / 1M
262.1K tokens
62.5%
核心差异一览
- Qwen3.5 Plus is 2.9x cheaper for API usage (input + output tokens combined).
- Qwen3.5 Plus has a larger context window (1M vs 262K tokens).
- Qwen3.5 Plus is faster at 85 tok/s vs 72 tok/s.
- Qwen3.5 Plus leads on SWE-bench coding (64.0% vs 62.5%).
| Feature | Qwen3.5 Plus | Qwen3 Max |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 87 | 88 |
Provider | Alibaba | Alibaba |
Input Price Cost per million input tokens | $0.60 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $2.40 / 1M | $6.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.40 / 1M | $1.28 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 262.1K tokens~393 A4 pages of text |
Max Output Tokens Per single request | 16K | 16K |
Throughput Speed Tokens generated per second | 85 tok/s | 72 tok/s |
SWE-bench Verified Real-world software engineering | 64% | 62.5% |
GPQA Diamond Graduate-level reasoning | 72% | 71.2% |
Cached Input Price Prompt caching discount per 1M tokens | $0.06 / 1M | $0.40 / 1M |
各项指标谁更胜一筹？
At-a-glance decision framework权威基准测试得分对比
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
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.
Qwen3.5 Plus · Strengths & Key Advantages
- 由 Alibaba 研发的高精度 Enterprise Million-Token Multimodal Transformer 架构，支持 1M 上下文窗口
- 极具竞争力的 API 定价（输入 $0.6/1M，输出 $2.4/1M），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
Qwen3 Max · Strengths & Key Advantages
- 由 Alibaba 研发的高精度 Advanced Dense/MoE Hybrid Frontier Transformer 架构，支持 262K 上下文窗口
- 极具竞争力的 API 定价（输入 $2.0/1M，输出 $6.0/1M），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 | Qwen3.5 Plus | Qwen3 Max |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.19 | $0.28 |
Output Tokens / Task Tokens generated per task | 24K | 25K |
Reasoning Tokens / Task Thinking tokens burned per task | 8K | 10K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen3.5 Plus | Qwen3 Max |
|---|---|---|
| Official API Model ID (SDK) | qwen3-5-plus-instruct | qwen3-max-instruct |
| API Protocol | Alibaba REST API (OpenAI-Compatible) | Alibaba REST API (OpenAI-Compatible) |
| Release Date | 2026-02 | 2025-09 |
| Knowledge Cutoff | December 2025 | July 2025 |
| Architecture | Enterprise Million-Token Multimodal Transformer | Advanced Dense/MoE Hybrid Frontier Transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.6/1M in) across full 1000K window | Flat rate ($2/1M in) across full 262K window |
| Prompt Cache Policy & TTL | Cached input at $0.06/1M tokens | Cached input at $0.4/1M 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 |
Qwen3.5 Plus vs Qwen3 Max: 常见问题解答 (FAQ)
Which is more intelligent, Qwen3.5 Plus or Qwen3 Max?
Qwen3 Max is more intelligent. It scores 88 compared with 87 on the composite Intelligence Index.
Which model is faster?
Qwen3.5 Plus is faster, generating 85 tokens per second compared with 72 tokens per second.
Which model is cheaper to run?
Qwen3.5 Plus is cheaper at $0.40 per 1M tokens blended, compared with $1.28 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen3.5 Plus has lower latency, with a time to first token of 0.49s compared with 0.59s.
Which model has a larger context window?
Qwen3.5 Plus has a larger context window, supporting 1M tokens compared with 262.1K tokens.
综合结论
Verdict & Model Selection Recommendation: Choose Qwen3.5 Plus if your priority is Multimodal enterprise document analysis, long-context codebases, and autonomous web agents. Its performance in demanding tasks justifies its operational token cost. Choose Qwen3 Max if your workflow prioritizes Complex multi-turn agents, enterprise strategic planning, and automated STEM research. 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 Qwen3.5 Plus compare to Qwen3 Max in API token pricing and cost per token?+
Qwen3.5 Plus costs $0.60 per 1M input tokens and $2.40 per 1M output tokens, whereas Qwen3 Max costs $2.00 input and $6.00 output. Overall, Qwen3.5 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: Qwen3.5 Plus or Qwen3 Max?+
Qwen3.5 Plus supports a context window of 1,000K tokens (~750k words), while Qwen3 Max supports 262K tokens (~197k words). Therefore, Qwen3.5 Plus can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Qwen3.5 Plus or Qwen3 Max?+
Qwen3.5 Plus scores 64% on SWE-bench Verified while Qwen3 Max scores 62.5%. For autonomous software engineering and multi-file debugging, Qwen3.5 Plus holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Qwen3.5 Plus and Qwen3 Max?+
Qwen3.5 Plus features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Qwen3 Max provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Qwen3.5 Plus and Qwen3 Max?+
Qwen3.5 Plus generates approximately 85 tokens per second, while Qwen3 Max delivers 72 tokens per second. In latency-sensitive production environments, Qwen3.5 Plus provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Qwen3.5 Plus and Qwen3 Max to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Qwen3.5 Plus to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Qwen3 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.
Related reading
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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