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.
Switch & Compare Any Models
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Qwen Flash
Efficient FlashAlibaba highest-throughput text API engine offering million-token capabilities at nominal price tiers.
$0.02 / 1M
$0.08 / 1M
1M tokens
N/A
Hunyuan TurboS
Fast HunyuanTencent's low-latency cloud model designed for instant chat responses, high-volume classification, and everyday customer interactions.
$0.06 / 1M
$0.24 / 1M
32.8K tokens
N/A
Key Differences at a Glance
- 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 |
Who Wins Where?
At-a-glance decision frameworkEvaluations ongoing
Empirical Head-to-Head Benchmark Scores
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.
Latency & Response Time
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Real-Time Interactive Token Cost Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen Flash · Strengths & Key Advantages
- Dirt cheap API cost ($0.02 / 1M input tokens)
- Blistering throughput up to 180 tokens/sec
- Solid performance on translation, entity extraction, and customer routing
Considerations & Trade-offs
- Inadequate for intricate analytical problem-solving or complex codebases
- Higher susceptibility to hallucination when pushed beyond 600K context tokens
Hunyuan TurboS · Strengths & Key Advantages
- Sub-250ms initial token latency ideal for real-time interactive apps
- Affordable API price point ($0.06 / 1M input tokens)
- Stable enterprise serving backed by Tencent Cloud infrastructure
Considerations & Trade-offs
- 32K context window is relatively modest compared to modern 256K+ models
- Not suited for deep scientific problem-solving or complex coding
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 |
Technical Specifications
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: Quick Answers
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.
Verdict
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.
Frequently asked questions
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.
Related reading
- Hunyuan TurboS vs Qwen Turbo: Benchmarks, Pricing & Speed ComparisonCompare
- Qwen Flash vs Qwen Turbo: Benchmarks, Pricing & Speed ComparisonCompare
- Aion-3.0 vs Aion-Mini: Benchmarks, Pricing & Speed ComparisonCompare
- Aion-3.0 vs Gemini 3.6 Flash: Benchmarks, Pricing & Speed ComparisonCompare
- Aion-Mini vs Laguna XS 2.1: Benchmarks, Pricing & Speed ComparisonCompare
- Aya Expanse 32B vs Llama 3.2 90B Vision: Benchmarks, Pricing & Speed ComparisonCompare
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.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs