Qwen3 Max vs GLM-5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen3 Max by Alibaba and GLM-5 by Zhipu AI 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, GLM-5 holds a notable pricing advantage for token ingestion. On output generation, GLM-5 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Qwen3 Max provides an expanded memory horizon with 262K tokens. In latency-sensitive production environments, Qwen3 Max 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 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%
GLM-5
开源权重模型Zhipu AI 推出的 GLM-5 人工智能模型，具备 204K 上下文窗口，输入价格为 $0/百万 Token，输出价格为 $0/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.00 / 1M
$0.00 / 1M
204.8K tokens
58%
核心差异一览
- GLM-5 is 1.0x cheaper for API usage (input + output tokens combined).
- Qwen3 Max has a larger context window (262K vs 205K tokens).
- Qwen3 Max is faster at 72 tok/s vs 68 tok/s.
- Qwen3 Max leads on SWE-bench coding (62.5% vs 58.0%).
| Feature | Qwen3 Max | GLM-5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 88 | 87 |
Provider | Alibaba | Zhipu AI |
Input Price Cost per million input tokens | $2.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $6.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.28 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 262.1K tokens~393 A4 pages of text | 204.8K tokens~307 A4 pages of text |
Max Output Tokens Per single request | 16K | 16K |
Throughput Speed Tokens generated per second | 72 tok/s | 68 tok/s |
SWE-bench Verified Real-world software engineering | 62.5% | 58% |
GPQA Diamond Graduate-level reasoning | 71.2% | 68% |
Cached Input Price Prompt caching discount per 1M tokens | $0.40 / 1M | $0.00 / 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 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
GLM-5 · Strengths & Key Advantages
- 由 Zhipu AI 研发的高精度 Frontier Sparse Mixture-of-Experts Autoregressive Transformer 架构，支持 204K 上下文窗口
- 极具竞争力的 API 定价（输入 $0/1M，输出 $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 Max | GLM-5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.28 | $0.00 |
Output Tokens / Task Tokens generated per task | 25K | 24K |
Reasoning Tokens / Task Thinking tokens burned per task | 10K | 8K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen3 Max | GLM-5 |
|---|---|---|
| Official API Model ID (SDK) | qwen3-max-instruct | glm-5 |
| API Protocol | Alibaba REST API (OpenAI-Compatible) | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-09 | 2026-02 |
| Knowledge Cutoff | July 2025 | December 2025 |
| Architecture | Advanced Dense/MoE Hybrid Frontier Transformer | Frontier Sparse Mixture-of-Experts Autoregressive Transformer |
| Parameters | Confidential | 240B MoE |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($2/1M in) across full 262K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Cached input at $0.4/1M tokens | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | 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 | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | 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) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 149 GB VRAM (4-bit Quantized) · 516 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Alibaba Production Safety & Alignment Verified | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text | text |
| Output Modalities | text | text |
Qwen3 Max vs GLM-5: 常见问题解答 (FAQ)
Which is more intelligent, Qwen3 Max or GLM-5?
Qwen3 Max is more intelligent. It scores 88 compared with 87 on the composite Intelligence Index.
Which model is faster?
Qwen3 Max is faster, generating 72 tokens per second compared with 68 tokens per second.
Which model is cheaper to run?
GLM-5 is cheaper at $0.00 per 1M tokens blended, compared with $1.28 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GLM-5 has lower latency, with a time to first token of 0.58s compared with 0.59s.
Which model has a larger context window?
Qwen3 Max has a larger context window, supporting 262.1K tokens compared with 204.8K tokens.
综合结论
Verdict & Model Selection Recommendation: Choose Qwen3 Max if your priority is Complex multi-turn agents, enterprise strategic planning, and automated STEM research. Its performance in demanding tasks justifies its operational token cost. Choose GLM-5 if your workflow prioritizes Enterprise software development agents, research automation, and private corporate intelligence. 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 Max compare to GLM-5 in API token pricing and cost per token?+
Qwen3 Max costs $2.00 per 1M input tokens and $6.00 per 1M output tokens, whereas GLM-5 costs $0.00 input and $0.00 output. Overall, GLM-5 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 Max or GLM-5?+
Qwen3 Max supports a context window of 262K tokens (~197k words), while GLM-5 supports 205K tokens (~154k words). Therefore, Qwen3 Max can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Qwen3 Max or GLM-5?+
Qwen3 Max scores 62.5% on SWE-bench Verified while GLM-5 scores 58%. For autonomous software engineering and multi-file debugging, Qwen3 Max holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Qwen3 Max and GLM-5?+
Qwen3 Max features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GLM-5 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Qwen3 Max and GLM-5?+
Qwen3 Max generates approximately 72 tokens per second, while GLM-5 delivers 68 tokens per second. In latency-sensitive production environments, Qwen3 Max provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Qwen3 Max and GLM-5 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GLM-5 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.
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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