Head-to-Head Model Comparison

Qwen Flash vs Qwen Turbo

Benchmarks, API Token Pricing, Context Window & Speed Comparison

由 GetInfoUs 研究团队审核最后审核日期:October 2026编辑政策
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Qwen Flash vs Qwen Turbo: Benchmarks, Pricing & Speed Comparison

Alibaba 的 Qwen Flash 与 Alibaba 的 Qwen Turbo 是当前备受关注的两款主力 AI 模型。Qwen Flash 提供 1M Token 上下文窗口,API 定价为每百万 Token 输入 $0.02 / 输出 $0.08;而 Qwen Turbo 提供 1M Token 上下文窗口,定价为每百万 Token 输入 $0.05 / 输出 $0.20。以下为两者的基准测试、延迟与成本全面对比。

模型直接对比 (VS)

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Popular Matchups:
Alibaba

Qwen Flash

超高速低延迟推理

Alibaba 推出的 Qwen Flash 是一款面向实时交互对话、高并发路由与极低延迟生产环境的超高速大模型,具备 1M 上下文窗口、180 Token/秒生成速度与 67/100 综合智能指数(每百万 Token $0.02 输入 / $0.08 输出)。

Intelligence Index67 / 100
Input Token:

$0.02 / 1M

Output Token:

$0.08 / 1M

Context Window:

1M tokens

SWE-bench Verified:

N/A

Alibaba

Qwen Turbo

超高速低延迟推理

Alibaba 推出的 Qwen Turbo 是一款面向实时交互对话、高并发路由与极低延迟生产环境的超高速大模型,具备 1M 上下文窗口、160 Token/秒生成速度与 68/100 综合智能指数(每百万 Token $0.05 输入 / $0.2 输出)。

Intelligence Index68 / 100
Input Token:

$0.05 / 1M

Output Token:

$0.20 / 1M

Context Window:

1M tokens

SWE-bench Verified:

N/A

核心差异一览

  • 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.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureQwen FlashQwen Turbo
Intelligence Index
Composite reasoning & knowledge score (0-100)
6768
Provider
AlibabaAlibaba
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 text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
8K8K
Throughput Speed
Tokens generated per second
180 tok/s160 tok/s
SWE-bench Verified
Real-world software engineering
N/AN/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 framework
Coding

Evaluations ongoing

Cost
Qwen Flash
2.5x cheaper monthly runtime
Speed
Qwen Flash
+13% faster generation
Context
Tied
1M tokens window

权威基准测试得分对比

Official evaluation metrics verified by independent third-party research labs.

Qwen FlashQwen Turbo
MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

63.5%vs65%
LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1195ptsvs1210pts
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

180 tok/s vs160 tok/s

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.

Time to First Token (TTFT)Lower is better
Qwen Flash0.25s
Qwen Turbo0.29s
Est. End-to-End Response (500 tokens)TTFT + generation time
Qwen Flash3.03s
Qwen Turbo3.42s

实时 Token 成本模拟器

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Qwen Flash Monthly API Bill
$0.11 / month
$0.02/1M in · $0.08/1M out
Qwen Turbo Monthly API Bill
$0.26 / month
$0.05/1M in · $0.2/1M out
Qwen Flash saves approximately $0.16 / month (60%) under this workload.2.5x Savings

Qwen Flash · Strengths & Key Advantages

  • 提供 1M 上下文窗口与最高 8K 输出容量,核心基准表现优异(MMLU-Pro 63.5% · Arena Elo 1195)
  • 高达 180 Token/秒的生成吞吐量,首字延迟 (TTFT) 仅约 250 毫秒
  • 极具竞争力的 API 定价(每百万 Token $0.02 输入 / $0.08 输出),原生支持提示词缓存、函数调用与结构化 JSON 输出
Considerations & Trade-offs
  • 闭源专有云端模型,不支持下载权重进行离线本地物理隔离部署
  • 在超长链路的前沿科研推导任务上,深度略逊于顶配重型旗舰推理模型

Qwen Turbo · Strengths & Key Advantages

  • 提供 1M 上下文窗口与最高 8K 输出容量,核心基准表现优异(MMLU-Pro 65% · Arena Elo 1210)
  • 高达 160 Token/秒的生成吞吐量,首字延迟 (TTFT) 仅约 290 毫秒
  • 极具竞争力的 API 定价(每百万 Token $0.05 输入 / $0.2 输出),原生支持提示词缓存、函数调用与结构化 JSON 输出
Considerations & Trade-offs
  • 闭源专有云端模型,不支持下载权重进行离线本地物理隔离部署
  • 在超长链路的前沿科研推导任务上,深度略逊于顶配重型旗舰推理模型

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricQwen FlashQwen Turbo
Cost per Task
Estimated USD to finish one task
$0.01$0.01
Output Tokens / Task
Tokens generated per task
10K12K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

技术规格参数

Architecture, release timeline, capabilities, and modalities.

FeatureQwen FlashQwen Turbo
Official API Model ID (SDK)qwen-flash-instructqwen-turbo-instruct
API ProtocolAlibaba REST API (OpenAI-Compatible)Alibaba REST API (OpenAI-Compatible)
Release Date2025-072024-11
Knowledge CutoffMay 2025September 2024
ArchitectureUltra-Lightweight Distilled Autoregressive TransformerDistilled High-Throughput Transformer
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM5,000 – 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 windowFlat rate ($0.05/1M in) across full 1000K window
Prompt Cache Policy & TTLCached input at $0.002/1M tokensCached input at $0.005/1M tokens
Computer Use / GUI AgentAPI / 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 CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportAPI Prompt Engineering & RAG DistillationAPI Prompt Engineering & RAG Distillation
Multimodal Token BillingText BPE (~4 chars/token)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMCloud-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 ClassificationAlibaba Production Safety & Alignment VerifiedAlibaba Production Safety & Alignment Verified
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise TierSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestexttext
Output Modalitiestexttext

Qwen Flash vs Qwen Turbo: 常见问题解答 (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.

综合结论

综合来看,Qwen Flash(适合:面向实时交互对话、高并发路由与极低延迟生产环境的超高速任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。)与 Qwen Turbo(适合:面向实时交互对话、高并发路由与极低延迟生产环境的超高速任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。)各具优势:Qwen Turbo 在综合智能与编程基准测试中表现更强,Qwen Flash 拥有更快的 Token 生成速度,而 Qwen Flash 则在规模化 API 部署中具备更高的成本效益。

常见问题 (FAQ)

在编程和深度推理方面,Qwen Flash 和 Qwen Turbo 哪个更强?+

在综合智能指数与编程基准评测中,Qwen Turbo 表现占优。建议结合上方的 SWE-bench Verified 与 GPQA Diamond 跑分根据具体场景选择。

Qwen Flash 与 Qwen Turbo 的 API 价格哪个更便宜?+

Qwen Flash 的 API 价格为输入 $0.02 / 输出 $0.08(每百万 Token),Qwen Turbo 为输入 $0.05 / 输出 $0.20。综合混合成本来看,Qwen Flash 更具性价比。

哪款模型拥有更大的上下文窗口和更快的响应速度?+

Qwen Flash 支持 1M Token 上下文窗口,而 Qwen Turbo 支持 1M Token。在流式生成速度方面,Qwen Flash 的吞吐表现更快。

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

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: Alibaba 与 Alibaba 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。