Qwen 2.5 72B vs Qwen 2.5 32B Instruct
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen 2.5 72B da Alibaba e Qwen 2.5 32B Instruct da Alibaba são dois modelos de IA de destaque. O Qwen 2.5 72B oferece uma janela de contexto de 128K tokens a $0.35/$0.70 por milhão de tokens, enquanto o Qwen 2.5 32B Instruct oferece 131.1K tokens de contexto a $0.18/$0.36 por milhão de tokens. Veja abaixo a comparação completa de benchmarks, latência e custos.
Troque e Compare Qualquer Modelo
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Qwen 2.5 72B
Modelo Base de Pesos AbertosO Qwen 2.5 72B da Alibaba é um modelo de IA projetado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, oferecendo janela de contexto de 128K, velocidade de 64 tokens/s, índice de inteligência de 44/100 e tar
$0.35 / 1M
$0.70 / 1M
128K tokens
45.2%
Qwen 2.5 32B Instruct
Modelo Base de Pesos AbertosO Qwen 2.5 32B Instruct da Alibaba é um modelo de IA projetado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, oferecendo janela de contexto de 131K, velocidade de 85 tokens/s, índice de inteligência de 41/
$0.18 / 1M
$0.36 / 1M
131.1K tokens
40.8%
Principais Diferenças em Resumo
- Qwen 2.5 32B Instruct is 1.9x cheaper for API usage (input + output tokens combined).
- Qwen 2.5 32B Instruct has a larger context window (131K vs 128K tokens).
- Qwen 2.5 32B Instruct is faster at 85 tok/s vs 64 tok/s.
- Qwen 2.5 72B leads on SWE-bench coding (45.2% vs 40.8%).
| Feature | Qwen 2.5 72B | Qwen 2.5 32B Instruct |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 44 | 41 |
Provider | Alibaba | Alibaba |
Input Price Cost per million input tokens | $0.35 / 1M | $0.18 / 1M |
Output Price Cost per million output tokens | $0.70 / 1M | $0.36 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.19 / 1M | $0.10 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 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 | 64 tok/s | 85 tok/s |
SWE-bench Verified Real-world software engineering | 45.2% | 40.8% |
GPQA Diamond Graduate-level reasoning | 52.8% | 48.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.07 / 1M | $0.04 / 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.
Qwen 2.5 72B · Strengths & Key Advantages
- Janela de contexto de 128K com limite máximo de saída de 8K tokens (SWE-bench Verified 45.2% · GPQA Diamond 52.8%)
- Taxa de transferência rápida de 64 tokens/segundo com latência inicial (TTFT) de 610 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
Qwen 2.5 32B Instruct · Strengths & Key Advantages
- Janela de contexto de 131K com limite máximo de saída de 8K tokens (SWE-bench Verified 40.8% · GPQA Diamond 48.5%)
- Taxa de transferência rápida de 85 tokens/segundo com latência inicial (TTFT) de 480 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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen 2.5 72B | Qwen 2.5 32B Instruct |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.28 | $0.16 |
Output Tokens / Task Tokens generated per task | 32K | 28K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Especificações Técnicas
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen 2.5 72B | Qwen 2.5 32B Instruct |
|---|---|---|
| Official API Model ID (SDK) | qwen-2-5-72b-instruct | qwen-2-5-32b-instruct |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-09 | 2024-09 |
| Knowledge Cutoff | September 2024 | June 2024 |
| Architecture | Dense Autoregressive Transformer | Dense Autoregressive Transformer |
| Parameters | 72B | 32B |
| 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 ($0.35/1M in) across full 128K window | Flat rate ($0.18/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Cached input at $0.07/1M tokens | Cached input at $0.036/1M 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 | 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) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 45 GB VRAM (4-bit Quantized) · 155 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 20 GB VRAM (4-bit Quantized) · 69 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 |
| Output Modalities | text | text |
Qwen 2.5 72B vs Qwen 2.5 32B Instruct: Respostas Rápidas (FAQ)
Which is more intelligent, Qwen 2.5 72B or Qwen 2.5 32B Instruct?
Qwen 2.5 72B is more intelligent. It scores 44 compared with 41 on the composite Intelligence Index.
Which model is faster?
Qwen 2.5 32B Instruct is faster, generating 85 tokens per second compared with 64 tokens per second.
Which model is cheaper to run?
Qwen 2.5 32B Instruct is cheaper at $0.10 per 1M tokens blended, compared with $0.19 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen 2.5 32B Instruct has lower latency, with a time to first token of 0.48s compared with 0.61s.
Which model has a larger context window?
Qwen 2.5 32B Instruct has a larger context window, supporting 131.1K tokens compared with 128K tokens.
Veredito
Tanto Qwen 2.5 72B (Indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas RAG corporativas e integração escalável via API.) quanto Qwen 2.5 32B Instruct (Indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: Qwen 2.5 72B lidera em inteligência geral e programação, Qwen 2.5 32B Instruct oferece geração mais rápida e Qwen 2.5 32B Instruct apresenta o melhor custo-benefício em produção.
Perguntas Frequentes (FAQ)
Qual é melhor para programação e raciocínio, Qwen 2.5 72B ou Qwen 2.5 32B Instruct?+
Nas avaliações de inteligência e código, Qwen 2.5 72B leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.
Qual modelo tem a API mais barata, Qwen 2.5 72B ou Qwen 2.5 32B Instruct?+
O Qwen 2.5 72B custa $0.35 / $0.70 por 1M de tokens (entrada/saída) e o Qwen 2.5 32B Instruct custa $0.18 / $0.36. No custo combinado, Qwen 2.5 32B Instruct é mais econômico.
Qual modelo possui maior janela de contexto e velocidade?+
O Qwen 2.5 72B suporta 128K tokens de contexto contra 131.1K tokens do Qwen 2.5 32B Instruct, enquanto Qwen 2.5 32B Instruct entrega maior velocidade de geração.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
- Llama 3.2 1B vs. Qwen 2.5 32B Instruct: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
- Llama 3.2 3B vs. Qwen 2.5 72B: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
- Pixtral 12B vs. Qwen 2.5 32B Instruct: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
- Qwen 2.5 32B Instruct vs. Qwen 2.5 7B Instruct: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
- Qwen3-Next 80B-A3B Instruct vs. Qwen 2.5 72B: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
- Aion-3.0 vs. Aion-Mini: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
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).