Qwen Flash vs Qwen Turbo
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen Flash by Alibaba 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 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, both models provide an identical 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.
بدّل وقارن بين أي نموذجين
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Qwen Flash
نموذج ذكاء اصطناعي عالي الأداءنموذج Qwen Flash المقدم من Alibaba يوفر نافذة سياق تصل إلى 1M رمز، بتكلفة $0.02 لكل مليون رمز إدخال و$0.08 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$0.02 / 1M
$0.08 / 1M
1M tokens
N/A
Qwen Turbo
نموذج ذكاء اصطناعي عالي الأداءنموذج Qwen Turbo المقدم من Alibaba يوفر نافذة سياق تصل إلى 1M رمز، بتكلفة $0.05 لكل مليون رمز إدخال و$0.2 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$0.05 / 1M
$0.20 / 1M
1M tokens
N/A
أهم الفروقات في لمحة
- Qwen Flash is 2.5x cheaper for API usage (input + output tokens combined).
- Qwen Flash is faster at 180 tok/s vs 160 tok/s.
| Feature | Qwen Flash | Qwen Turbo |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 67 | 68 |
Provider | Alibaba | Alibaba |
Input Price Cost per million input tokens | $0.02 / 1M | $0.05 / 1M |
Output Price Cost per million output tokens | $0.08 / 1M | $0.20 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.01 / 1M | $0.03 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 8K | 8K |
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 |
من يتفوق في كل مجال؟
At-a-glance decision frameworkEvaluations ongoing
نتائج الاختبارات المعيارية الموثقة
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.
زمن الاستجابة والسرعة
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
محاكي تكلفة الرموز التفاعلي
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen Flash · Strengths & Key Advantages
- بنية Ultra-Lightweight Distilled Autoregressive Transformer متطورة من Alibaba مع سعة سياق تبلغ 1M رمز
- كفاءة عالية في التكلفة ($0.02/1M إدخال و$0.08/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
Qwen Turbo · Strengths & Key Advantages
- بنية Distilled High-Throughput Transformer متطورة من Alibaba مع سعة سياق تبلغ 1M رمز
- كفاءة عالية في التكلفة ($0.05/1M إدخال و$0.2/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen Flash | Qwen Turbo |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.01 | $0.01 |
Output Tokens / Task Tokens generated per task | 10K | 12K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
المواصفات التقنية
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen Flash | Qwen Turbo |
|---|---|---|
| Official API Model ID (SDK) | qwen-flash-instruct | qwen-turbo-instruct |
| API Protocol | Alibaba REST API (OpenAI-Compatible) | Alibaba REST API (OpenAI-Compatible) |
| Release Date | 2025-07 | 2024-11 |
| Knowledge Cutoff | May 2025 | September 2024 |
| Architecture | Ultra-Lightweight Distilled Autoregressive Transformer | Distilled High-Throughput 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) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.02/1M in) across full 1000K window | Flat rate ($0.05/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Cached input at $0.002/1M 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 | Alibaba 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 |
Qwen Flash vs Qwen Turbo: إجابات سريعة (FAQ)
Which is more intelligent, Qwen Flash or Qwen Turbo?
Qwen Turbo is more intelligent. It scores 68 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 $0.03 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen Flash has lower latency, with a time to first token of 0.25s compared with 0.29s.
الحكم النهائي
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 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.
الأسئلة الشائعة (FAQ)
How does Qwen Flash compare to Qwen Turbo 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 Qwen Turbo costs $0.05 input and $0.20 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 Qwen Turbo?+
Both Qwen Flash and Qwen Turbo offer an identical context window of 1,000K tokens (~750k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: Qwen Flash or Qwen Turbo?+
Qwen Flash scores at the frontier of coding benchmarks, while Qwen Turbo 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 Qwen Turbo?+
Qwen Flash 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 Qwen Flash and Qwen Turbo?+
Qwen Flash generates approximately 180 tokens per second, while Qwen Turbo 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 Qwen Turbo 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 Qwen 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.
المصادر الرسمية: GetInfoUs Independent AI Benchmarks & Official API Specs