QwQ 32B Preview vs Qwen2.5-VL 72B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Alibaba 的 QwQ 32B Preview 与 Alibaba 的 Qwen2.5-VL 72B 是当前备受关注的两款主力 AI 模型。QwQ 32B Preview 提供 32.8K Token 上下文窗口，API 定价为每百万 Token 输入 $0.20 / 输出 $0.60；而 Qwen2.5-VL 72B 提供 131.1K Token 上下文窗口，定价为每百万 Token 输入 $0.00 / 输出 $0.00。以下为两者的基准测试、延迟与成本全面对比。
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QwQ 32B Preview
开源权重推理大模型Alibaba 推出的 QwQ 32B Preview 是一款通过大规模强化学习训练、支持私有化部署的开源权重推理大模型，具备 33K 上下文窗口、52 Token/秒生成速度与 47/100 综合智能指数（每百万 Token $0.2 输入 / $0.6 输出）。
$0.20 / 1M
$0.60 / 1M
32.8K tokens
54.2%
Qwen2.5-VL 72B
原生多模态视觉模型Alibaba 推出的 Qwen2.5-VL 72B 是一款原生支持图文多模态理解、视觉图表解析与跨模态推理的大模型，具备 131K 上下文窗口、55 Token/秒生成速度与 79/100 综合智能指数（免费开源权重）。
$0.00 / 1M
$0.00 / 1M
131.1K tokens
38%
核心差异一览
- Qwen2.5-VL 72B is 1.0x cheaper for API usage (input + output tokens combined).
- Qwen2.5-VL 72B has a larger context window (131K vs 33K tokens).
- Qwen2.5-VL 72B is faster at 55 tok/s vs 52 tok/s.
- QwQ 32B Preview leads on SWE-bench coding (54.2% vs 38.0%).
| Feature | QwQ 32B Preview | Qwen2.5-VL 72B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 47 | 79 |
Provider | Alibaba | Alibaba |
Input Price Cost per million input tokens | $0.20 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.60 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.14 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 32.8K tokens~49 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 8K | 8K |
Throughput Speed Tokens generated per second | 52 tok/s | 55 tok/s |
SWE-bench Verified Real-world software engineering | 54.2% | 38% |
GPQA Diamond Graduate-level reasoning | 65.2% | 58% |
Cached Input Price Prompt caching discount per 1M tokens | $0.05 / 1M | $0.00 / 1M |
各项指标谁更胜一筹？
At-a-glance decision framework权威基准测试得分对比
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
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.
QwQ 32B Preview · Strengths & Key Advantages
- 提供 33K 上下文窗口与最高 8K 输出容量，核心基准表现优异（SWE-bench Verified 54.2% · GPQA Diamond 65.2%）
- 高达 52 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 820 毫秒
- 开放模型权重 (32B)，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
- 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
- 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
Qwen2.5-VL 72B · Strengths & Key Advantages
- 提供 131K 上下文窗口与最高 8K 输出容量，核心基准表现优异（SWE-bench Verified 38% · GPQA Diamond 58%）
- 高达 55 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 720 毫秒
- 开放模型权重 (72B)，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
- 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
- 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.
| Metric | QwQ 32B Preview | Qwen2.5-VL 72B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.45 | $0.00 |
Output Tokens / Task Tokens generated per task | 22K | 20K |
Reasoning Tokens / Task Thinking tokens burned per task | 18K | 6K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | QwQ 32B Preview | Qwen2.5-VL 72B |
|---|---|---|
| Official API Model ID (SDK) | qwq-32b-instruct | qwen2-5-vl-72b-instruct |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-11 | 2024-09 |
| Knowledge Cutoff | September 2024 | June 2024 |
| Architecture | Reinforcement Learning Reasoning Transformer | Open-Weights Multimodal Vision-Language Transformer |
| Parameters | 32B | 72B |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($0.2/1M in) across full 33K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Cached input at $0.05/1M tokens | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling | 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 | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 20 GB VRAM (4-bit Quantized) · 69 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 45 GB VRAM (4-bit Quantized) · 155 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text | text, vision |
| Output Modalities | text | text |
QwQ 32B Preview vs Qwen2.5-VL 72B: 常见问题解答 (FAQ)
Which is more intelligent, QwQ 32B Preview or Qwen2.5-VL 72B?
Qwen2.5-VL 72B is more intelligent. It scores 79 compared with 47 on the composite Intelligence Index.
Which model is faster?
Qwen2.5-VL 72B is faster, generating 55 tokens per second compared with 52 tokens per second.
Which model is cheaper to run?
Qwen2.5-VL 72B is cheaper at $0.00 per 1M tokens blended, compared with $0.14 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen2.5-VL 72B has lower latency, with a time to first token of 0.72s compared with 0.82s.
Which model has a larger context window?
Qwen2.5-VL 72B has a larger context window, supporting 131.1K tokens compared with 32.8K tokens.
综合结论
综合来看，QwQ 32B Preview（适合：通过大规模强化学习训练、支持私有化部署的开源权重推理任务、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 Qwen2.5-VL 72B（适合：原生支持图文多模态理解、视觉图表解析与跨模态推理的任务、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：Qwen2.5-VL 72B 在综合智能与编程基准测试中表现更强，Qwen2.5-VL 72B 拥有更快的 Token 生成速度，而 Qwen2.5-VL 72B 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，QwQ 32B Preview 和 Qwen2.5-VL 72B 哪个更强？+
在综合智能指数与编程基准评测中，Qwen2.5-VL 72B 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。
QwQ 32B Preview 与 Qwen2.5-VL 72B 的 API 价格哪个更便宜？+
QwQ 32B Preview 的 API 价格为输入 $0.20 / 输出 $0.60（每百万 Token），Qwen2.5-VL 72B 为输入 $0.00 / 输出 $0.00。综合混合成本来看，Qwen2.5-VL 72B 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？+
QwQ 32B Preview 支持 32.8K Token 上下文窗口，而 Qwen2.5-VL 72B 支持 131.1K Token。在流式生成速度方面，Qwen2.5-VL 72B 的吞吐表现更快。
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: Alibaba 与 Alibaba 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。