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.
원하는 AI 모델 선택 및 실시간 비교
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Hunyuan TurboS
고성능 AI 모델Tencent의 Hunyuan TurboS 모델은 32K 토큰 컨텍스트 윈도우와 입력 $0.06/1M 토큰, 출력 $0.24/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.06 / 1M
$0.24 / 1M
32.8K tokens
N/A
Qwen Turbo
고성능 AI 모델Alibaba의 Qwen Turbo 모델은 1M 토큰 컨텍스트 윈도우와 입력 $0.05/1M 토큰, 출력 $0.2/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.05 / 1M
$0.20 / 1M
1M tokens
N/A
한눈에 보는 핵심 차이점
- 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 |
부문별 우위 모델
At-a-glance decision frameworkEvaluations ongoing
검증된 벤치마크 점수 비교
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.
지연 시간(Latency) 및 응답 속도
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
실시간 API 토큰 비용 시뮬레이터
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Hunyuan TurboS · Strengths & Key Advantages
- Tencent의 첨단 Lightweight Fast-Inference Transformer 아키텍처와 최대 32K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.06/1M, 출력 $0.24/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Qwen Turbo · Strengths & Key Advantages
- Alibaba의 첨단 Distilled High-Throughput Transformer 아키텍처와 최대 1M 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.05/1M, 출력 $0.2/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
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 |
기술 사양 (Specifications)
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: 핵심 요약 문답 (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.
종합 평가
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.
자주 묻는 질문 (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.
공식 출처: GetInfoUs Independent AI Benchmarks & Official API Specs