Qwen3 Max vs GLM-5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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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
Deep-Reasoning-ModellQwen3-Max von Alibaba ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 262K Token zu API-Preisen von $2.0/1M Input- und $6.0/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$2.00 / 1M
$6.00 / 1M
262.1K tokens
62.5%
GLM-5
Open-Weights-ModellGLM-5 von Zhipu AI ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 204K Token zu API-Preisen von $0/1M Input- und $0/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$0.00 / 1M
$0.00 / 1M
204.8K tokens
58%
Wichtigste Unterschiede auf einen Blick
- 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 |
Wer gewinnt in welcher Disziplin?
At-a-glance decision frameworkGeprüfte Benchmark-Ergebnisse im Vergleich
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.
Latenz & Antwortzeit
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Interaktiver Token-Kosten-Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen3 Max · Strengths & Key Advantages
- Fortschrittliche Advanced Dense/MoE Hybrid Frontier Transformer-Architektur von Alibaba mit 262K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($2.0/1M Input, $6.0/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
GLM-5 · Strengths & Key Advantages
- Fortschrittliche Frontier Sparse Mixture-of-Experts Autoregressive Transformer-Architektur von Zhipu AI mit 204K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($0/1M Input, $0/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
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 |
Technische Spezifikationen
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: Schnelle Antworten (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.
Fazit
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.
Häufig gestellte Fragen (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.
Offizielle Quellen: GetInfoUs Independent AI Benchmarks & Official API Specs