QwQ 32B Preview vs Qwen2.5-VL 72B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
QwQ 32B Preview da Alibaba e Qwen2.5-VL 72B da Alibaba são dois modelos de IA de destaque. O QwQ 32B Preview oferece uma janela de contexto de 32.8K tokens a $0.20/$0.60 por milhão de tokens, enquanto o Qwen2.5-VL 72B oferece 131.1K tokens de contexto a $0.00/$0.00 por milhão de tokens. Veja abaixo a comparação completa de benchmarks, latência e custos.
Troque e Compare Qualquer Modelo
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QwQ 32B Preview
Raciocínio de Pesos AbertosO QwQ 32B Preview da Alibaba é um modelo de IA projetado para raciocínio em cadeia de pensamento com pesos abertos e alta eficiência, oferecendo janela de contexto de 33K, velocidade de 52 tokens/s, índice de inteligência de 47/100 e tarifa
$0.20 / 1M
$0.60 / 1M
32.8K tokens
54.2%
Qwen2.5-VL 72B
IA Multimodal NativaO Qwen2.5-VL 72B da Alibaba é um modelo de IA projetado para compreensão multimodal nativa de texto e imagem e leitura visual de documentos, oferecendo janela de contexto de 131K, velocidade de 55 tokens/s, índice de inteligência de 79/100
$0.00 / 1M
$0.00 / 1M
131.1K tokens
38%
Principais Diferenças em Resumo
- 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 |
Quem vence em cada categoria?
At-a-glance decision frameworkPontuações de Benchmarks Verificadas
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.
Latência e Tempo de Resposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interativo de Custo de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
QwQ 32B Preview · Strengths & Key Advantages
- Janela de contexto de 33K com limite máximo de saída de 8K tokens (SWE-bench Verified 54.2% · GPQA Diamond 65.2%)
- Taxa de transferência rápida de 52 tokens/segundo com latência inicial (TTFT) de 820 ms
- Pesos abertos (32B) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama
Considerations & Trade-offs
- Requer servidores GPU com memória VRAM adequada para execução local em precisão total
- Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade
Qwen2.5-VL 72B · Strengths & Key Advantages
- Janela de contexto de 131K com limite máximo de saída de 8K tokens (SWE-bench Verified 38% · GPQA Diamond 58%)
- Taxa de transferência rápida de 55 tokens/segundo com latência inicial (TTFT) de 720 ms
- Pesos abertos (72B) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama
Considerations & Trade-offs
- Requer servidores GPU com memória VRAM adequada para execução local em precisão total
- Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade
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 |
Especificações Técnicas
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: Respostas Rápidas (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.
Veredito
Tanto QwQ 32B Preview (Indicado para raciocínio em cadeia de pensamento com pesos abertos e alta eficiência, arquiteturas RAG corporativas e integração escalável via API.) quanto Qwen2.5-VL 72B (Indicado para compreensão multimodal nativa de texto e imagem e leitura visual de documentos, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: Qwen2.5-VL 72B lidera em inteligência geral e programação, Qwen2.5-VL 72B oferece geração mais rápida e Qwen2.5-VL 72B apresenta o melhor custo-benefício em produção.
Perguntas Frequentes (FAQ)
Qual é melhor para programação e raciocínio, QwQ 32B Preview ou Qwen2.5-VL 72B?+
Nas avaliações de inteligência e código, Qwen2.5-VL 72B leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.
Qual modelo tem a API mais barata, QwQ 32B Preview ou Qwen2.5-VL 72B?+
O QwQ 32B Preview custa $0.20 / $0.60 por 1M de tokens (entrada/saída) e o Qwen2.5-VL 72B custa $0.00 / $0.00. No custo combinado, Qwen2.5-VL 72B é mais econômico.
Qual modelo possui maior janela de contexto e velocidade?+
O QwQ 32B Preview suporta 32.8K tokens de contexto contra 131.1K tokens do Qwen2.5-VL 72B, enquanto Qwen2.5-VL 72B entrega maior velocidade de geração.
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.
Fontes oficiais: Documentação oficial de API da Alibaba e Alibaba e benchmarks independentes (2026).