Hunyuan TurboS vs Qwen Turbo
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Hunyuan TurboS by Tencent and Qwen Turbo 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 Turbo holds a notable pricing advantage for token ingestion. On output generation, Qwen Turbo is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Qwen Turbo provides an expanded memory horizon with 1,000K tokens. In latency-sensitive production environments, both models deliver balanced throughput and low time-to-first-token latency for interactive 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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Hunyuan TurboS
高性能 AI 模型Tencent 推出的 Hunyuan TurboS 人工智能模型，具备 32K 上下文窗口，输入价格为 $0.06/百万 Token，输出价格为 $0.24/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.06 / 1M
$0.24 / 1M
32.8K tokens
N/A
Qwen Turbo
高性能 AI 模型Alibaba 推出的 Qwen Turbo 人工智能模型，具备 1M 上下文窗口，输入价格为 $0.05/百万 Token，输出价格为 $0.2/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.05 / 1M
$0.20 / 1M
1M tokens
N/A
核心差异一览
- Qwen Turbo is 1.2x cheaper for API usage (input + output tokens combined).
- Qwen Turbo has a larger context window (1M vs 33K tokens).
- Qwen Turbo is faster at 160 tok/s vs 160 tok/s.
| Feature | Hunyuan TurboS | Qwen Turbo |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 70 | 68 |
Provider | Tencent | Alibaba |
Input Price Cost per million input tokens | $0.06 / 1M | $0.05 / 1M |
Output Price Cost per million output tokens | $0.24 / 1M | $0.20 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.04 / 1M | $0.03 / 1M |
Context Window Maximum input + output tokens | 32.8K tokens~49 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 4K | 8K |
Throughput Speed Tokens generated per second | 160 tok/s | 160 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.01 / 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.
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.
Hunyuan TurboS · Strengths & Key Advantages
- 由 Tencent 研发的高精度 Lightweight Fast-Inference Transformer 架构，支持 32K 上下文窗口
- 极具竞争力的 API 定价（输入 $0.06/1M，输出 $0.24/1M），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 Turbo · Strengths & Key Advantages
- 由 Alibaba 研发的高精度 Distilled High-Throughput Transformer 架构，支持 1M 上下文窗口
- 极具竞争力的 API 定价（输入 $0.05/1M，输出 $0.2/1M），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 | Hunyuan TurboS | Qwen Turbo |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.01 | $0.01 |
Output Tokens / Task Tokens generated per task | 8K | 12K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Hunyuan TurboS | Qwen Turbo |
|---|---|---|
| Official API Model ID (SDK) | hunyuan-turbos | qwen-turbo-instruct |
| API Protocol | Tencent REST API (OpenAI-Compatible) | Alibaba REST API (OpenAI-Compatible) |
| Release Date | 2025-03 | 2024-11 |
| Knowledge Cutoff | January 2025 | September 2024 |
| Architecture | Lightweight Fast-Inference Transformer | Distilled High-Throughput 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 ($0.06/1M in) across full 33K window | Flat rate ($0.05/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Cached input at $0.006/1M tokens | Cached input at $0.005/1M tokens |
| 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 | API Prompt Engineering & RAG Distillation |
| 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) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Tencent 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 | text |
| Output Modalities | text | text |
Hunyuan TurboS vs Qwen Turbo: 常见问题解答 (FAQ)
Which is more intelligent, Hunyuan TurboS or Qwen Turbo?
Hunyuan TurboS is more intelligent. It scores 70 compared with 68 on the composite Intelligence Index.
Which model has lower latency?
Hunyuan TurboS has lower latency, with a time to first token of 0.25s compared with 0.29s.
Which model has a larger context window?
Qwen Turbo has a larger context window, supporting 1M tokens compared with 32.8K tokens.
综合结论
Verdict & Model Selection Recommendation: Choose Hunyuan TurboS if your priority is Real-time customer service bots, live interactive streaming chat, and rapid intent classification. Its performance in demanding tasks justifies its operational token cost. Choose Qwen Turbo if your workflow prioritizes Bulk classification, log parsing, translation, and high-frequency chatbot endpoints. 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 Hunyuan TurboS compare to Qwen Turbo in API token pricing and cost per token?+
Hunyuan TurboS costs $0.06 per 1M input tokens and $0.24 per 1M output tokens, whereas Qwen Turbo costs $0.05 input and $0.20 output. Overall, Qwen Turbo 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: Hunyuan TurboS or Qwen Turbo?+
Hunyuan TurboS supports a context window of 33K tokens (~25k words), while Qwen Turbo supports 1,000K tokens (~750k words). Therefore, Qwen Turbo can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Hunyuan TurboS or Qwen Turbo?+
Hunyuan TurboS scores at the frontier of coding benchmarks, while Qwen Turbo scores competitively for software generation. For complex autonomous debugging and agentic engineering, Hunyuan TurboS provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Hunyuan TurboS and Qwen Turbo?+
Hunyuan TurboS is optimized for low-latency direct generation. In contrast, Qwen Turbo focuses on standard fast inference.
How does inference speed (tokens per second) compare between Hunyuan TurboS and Qwen Turbo?+
Both Hunyuan TurboS and Qwen Turbo deliver matching throughput at approximately 160 tokens per second. In latency-sensitive production environments, streaming response velocity is comparable, with real-world time-to-first-token (TTFT) determined primarily by provider routing and concurrency load.
Can you implement a tiered LLM routing setup combining Hunyuan TurboS and Qwen Turbo to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Qwen Turbo to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Hunyuan TurboS. 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