GLM-5V-Turbo vs Hunyuan Large V2
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GLM-5V-Turbo by Zhipu AI and Hunyuan Large V2 by Tencent 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, Hunyuan Large V2 holds a notable pricing advantage for token ingestion. On output generation, GLM-5V-Turbo is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Hunyuan Large V2 provides an expanded memory horizon with 512K tokens. In latency-sensitive production environments, GLM-5V-Turbo 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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GLM-5V-Turbo
深度推理模型Zhipu AI 推出的 GLM-5V-Turbo 人工智能模型，具备 200K 上下文窗口，输入价格为 $0.9/百万 Token，输出价格为 $2.7/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.90 / 1M
$2.70 / 1M
200K tokens
N/A
Hunyuan Large V2
深度推理模型Tencent 推出的 Hunyuan Large V2 人工智能模型，具备 512K 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
512K tokens
N/A
核心差异一览
- Hunyuan Large V2 is 1.1x cheaper for API usage (input + output tokens combined).
- Hunyuan Large V2 has a larger context window (512K vs 200K tokens).
- GLM-5V-Turbo is faster at 85 tok/s vs 80 tok/s.
| Feature | GLM-5V-Turbo | Hunyuan Large V2 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 85 | 85 |
Provider | Zhipu AI | Tencent |
Input Price Cost per million input tokens | $0.90 / 1M | $0.70 / 1M |
Output Price Cost per million output tokens | $2.70 / 1M | $2.80 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.51 / 1M | $0.47 / 1M |
Context Window Maximum input + output tokens | 200K tokens~300 A4 pages of text | 512K tokens~768 A4 pages of text |
Max Output Tokens Per single request | 16K | 16K |
Throughput Speed Tokens generated per second | 85 tok/s | 80 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
GPQA Diamond Graduate-level reasoning | 64% | 66% |
Cached Input Price Prompt caching discount per 1M tokens | $0.09 / 1M | $0.07 / 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.
GLM-5V-Turbo · Strengths & Key Advantages
- 由 Zhipu AI 研发的高精度 Frontier Commercial Vision-Language Transformer 架构，支持 200K 上下文窗口
- 极具竞争力的 API 定价（输入 $0.9/1M，输出 $2.7/1M），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
Hunyuan Large V2 · Strengths & Key Advantages
- 由 Tencent 研发的高精度 High-Scale Sparse Mixture-of-Experts Transformer 架构，支持 512K 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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GLM-5V-Turbo | Hunyuan Large V2 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.22 | $0.22 |
Output Tokens / Task Tokens generated per task | 20K | 22K |
Reasoning Tokens / Task Thinking tokens burned per task | 6K | 7K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | GLM-5V-Turbo | Hunyuan Large V2 |
|---|---|---|
| Official API Model ID (SDK) | glm-5v-turbo | hunyuan-large-v2 |
| API Protocol | Zhipu AI REST API (OpenAI-Compatible) | Tencent REST API (OpenAI-Compatible) |
| Release Date | 2026-04 | 2026-05 |
| Knowledge Cutoff | February 2026 | February 2026 |
| Architecture | Frontier Commercial Vision-Language Transformer | High-Scale Sparse Mixture-of-Experts Transformer |
| Parameters | Confidential | 500B total (30B active MoE) |
| 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) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.9/1M in) across full 200K window | Flat rate ($0.7/1M in) across full 512K window |
| Prompt Cache Policy & TTL | Cached input at $0.09/1M tokens | Cached input at $0.07/1M 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 | Zhipu AI Production Safety & Alignment Verified | Tencent 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 |
GLM-5V-Turbo vs Hunyuan Large V2: 常见问题解答 (FAQ)
Which model is faster?
GLM-5V-Turbo is faster, generating 85 tokens per second compared with 80 tokens per second.
Which model is cheaper to run?
Hunyuan Large V2 is cheaper at $0.47 per 1M tokens blended, compared with $0.51 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Hunyuan Large V2 has lower latency, with a time to first token of 0.48s compared with 0.49s.
Which model has a larger context window?
Hunyuan Large V2 has a larger context window, supporting 512K tokens compared with 200K tokens.
综合结论
Verdict & Model Selection Recommendation: Choose GLM-5V-Turbo if your priority is Visual RPA, mobile UI automated testing, financial document intelligence, and visual agents. Its performance in demanding tasks justifies its operational token cost. Choose Hunyuan Large V2 if your workflow prioritizes Social chatbots, creative writing, game NPC dialogue, and extensive document summarization. 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 GLM-5V-Turbo compare to Hunyuan Large V2 in API token pricing and cost per token?+
GLM-5V-Turbo costs $0.90 per 1M input tokens and $2.70 per 1M output tokens, whereas Hunyuan Large V2 costs $0.70 input and $2.80 output. Overall, Hunyuan Large V2 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: GLM-5V-Turbo or Hunyuan Large V2?+
GLM-5V-Turbo supports a context window of 200K tokens (~150k words), while Hunyuan Large V2 supports 512K tokens (~384k words). Therefore, Hunyuan Large V2 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GLM-5V-Turbo or Hunyuan Large V2?+
GLM-5V-Turbo scores at the frontier of coding benchmarks, while Hunyuan Large V2 scores competitively for software generation. For complex autonomous debugging and agentic engineering, GLM-5V-Turbo provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between GLM-5V-Turbo and Hunyuan Large V2?+
GLM-5V-Turbo features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Hunyuan Large V2 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between GLM-5V-Turbo and Hunyuan Large V2?+
GLM-5V-Turbo generates approximately 85 tokens per second, while Hunyuan Large V2 delivers 80 tokens per second. In latency-sensitive production environments, GLM-5V-Turbo provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GLM-5V-Turbo and Hunyuan Large V2 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Hunyuan Large V2 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GLM-5V-Turbo. 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