Qwen Flash vs Hunyuan TurboS
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen Flash by Alibaba and Hunyuan TurboS 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, Qwen Flash holds a notable pricing advantage for token ingestion. On output generation, Qwen Flash is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Qwen Flash provides an expanded memory horizon with 1,000K tokens. In latency-sensitive production environments, Qwen Flash 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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Qwen Flash
高性能 AI 模型Alibaba 推出的 Qwen Flash 人工智能模型，具备 1M 上下文窗口，输入价格为 $0.02/百万 Token，输出价格为 $0.08/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.02 / 1M
$0.08 / 1M
1M tokens
N/A
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
核心差异一览
- Qwen Flash is 3.0x cheaper for API usage (input + output tokens combined).
- Qwen Flash has a larger context window (1M vs 33K tokens).
- Qwen Flash is faster at 180 tok/s vs 160 tok/s.
| Feature | Qwen Flash | Hunyuan TurboS |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 67 | 70 |
Provider | Alibaba | Tencent |
Input Price Cost per million input tokens | $0.02 / 1M | $0.06 / 1M |
Output Price Cost per million output tokens | $0.08 / 1M | $0.24 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.01 / 1M | $0.04 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 32.8K tokens~49 A4 pages of text |
Max Output Tokens Per single request | 8K | 4K |
Throughput Speed Tokens generated per second | 180 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 | $0.00 / 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.
Qwen Flash · Strengths & Key Advantages
- 由 Alibaba 研发的高精度 Ultra-Lightweight Distilled Autoregressive Transformer 架构，支持 1M 上下文窗口
- 极具竞争力的 API 定价（输入 $0.02/1M，输出 $0.08/1M），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 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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen Flash | Hunyuan TurboS |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.01 | $0.01 |
Output Tokens / Task Tokens generated per task | 10K | 8K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen Flash | Hunyuan TurboS |
|---|---|---|
| Official API Model ID (SDK) | qwen-flash-instruct | hunyuan-turbos |
| API Protocol | Alibaba REST API (OpenAI-Compatible) | Tencent REST API (OpenAI-Compatible) |
| Release Date | 2025-07 | 2025-03 |
| Knowledge Cutoff | May 2025 | January 2025 |
| Architecture | Ultra-Lightweight Distilled Autoregressive Transformer | Lightweight Fast-Inference 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 ($0.02/1M in) across full 1000K window | Flat rate ($0.06/1M in) across full 33K window |
| Prompt Cache Policy & TTL | Cached input at $0.002/1M tokens | Cached input at $0.006/1M 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 | Alibaba 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 | text |
| Output Modalities | text | text |
Qwen Flash vs Hunyuan TurboS: 常见问题解答 (FAQ)
Which is more intelligent, Qwen Flash or Hunyuan TurboS?
Hunyuan TurboS is more intelligent. It scores 70 compared with 67 on the composite Intelligence Index.
Which model is faster?
Qwen Flash is faster, generating 180 tokens per second compared with 160 tokens per second.
Which model is cheaper to run?
Qwen Flash is cheaper at $0.01 per 1M tokens blended, compared with $0.04 (7:2:1 cache:input:output ratio).
Which model has a larger context window?
Qwen Flash has a larger context window, supporting 1M tokens compared with 32.8K tokens.
综合结论
Verdict & Model Selection Recommendation: Choose Qwen Flash if your priority is High-throughput enterprise routing, translation pipelines, and automated log analysis. Its performance in demanding tasks justifies its operational token cost. Choose Hunyuan TurboS if your workflow prioritizes Real-time customer service bots, live interactive streaming chat, and rapid intent classification. 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 Qwen Flash compare to Hunyuan TurboS in API token pricing and cost per token?+
Qwen Flash costs $0.02 per 1M input tokens and $0.08 per 1M output tokens, whereas Hunyuan TurboS costs $0.06 input and $0.24 output. Overall, Qwen Flash 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: Qwen Flash or Hunyuan TurboS?+
Qwen Flash supports a context window of 1,000K tokens (~750k words), while Hunyuan TurboS supports 33K tokens (~25k words). Therefore, Qwen Flash can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Qwen Flash or Hunyuan TurboS?+
Qwen Flash scores at the frontier of coding benchmarks, while Hunyuan TurboS scores competitively for software generation. For complex autonomous debugging and agentic engineering, Qwen Flash provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Qwen Flash and Hunyuan TurboS?+
Qwen Flash is optimized for low-latency direct generation. In contrast, Hunyuan TurboS focuses on standard fast inference.
How does inference speed (tokens per second) compare between Qwen Flash and Hunyuan TurboS?+
Qwen Flash generates approximately 180 tokens per second, while Hunyuan TurboS delivers 160 tokens per second. In latency-sensitive production environments, Qwen Flash provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Qwen Flash and Hunyuan TurboS to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Qwen Flash 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