Qwen3.5 27B vs DeepSeek Chat
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen3.5 27B da Alibaba e DeepSeek Chat da DeepSeek são dois modelos de IA de destaque. O Qwen3.5 27B oferece uma janela de contexto de 262.1K tokens a $0.00/$0.00 por milhão de tokens, enquanto o DeepSeek Chat oferece 1M tokens de contexto a $0.28/$0.42 por milhão de tokens. Veja abaixo a comparação completa de benchmarks, latência e custos.
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Qwen3.5 27B
Modelo Base de Pesos AbertosO Qwen3.5 27B 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 262K, velocidade de 90 tokens/s, índice de inteligência de 75/100 e tari
$0.00 / 1M
$0.00 / 1M
262.1K tokens
N/A
DeepSeek Chat
Modelo Base de Pesos AbertosO DeepSeek Chat da DeepSeek é um modelo de IA projetado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, oferecendo janela de contexto de 1M, velocidade de 75 tokens/s, índice de inteligência de 50/100 e tar
$0.28 / 1M
$0.42 / 1M
1M tokens
54%
Principais Diferenças em Resumo
- Qwen3.5 27B is 1.0x cheaper for API usage (input + output tokens combined).
- DeepSeek Chat has a larger context window (1M vs 262K tokens).
- Qwen3.5 27B is faster at 90 tok/s vs 75 tok/s.
| Feature | Qwen3.5 27B | DeepSeek Chat |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 75 | 50 |
Provider | Alibaba | DeepSeek |
Input Price Cost per million input tokens | $0.00 / 1M | $0.28 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.42 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.12 / 1M |
Context Window Maximum input + output tokens | 262.1K tokens~393 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 8K | 16K |
Throughput Speed Tokens generated per second | 90 tok/s | 75 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 54% |
GPQA Diamond Graduate-level reasoning | 56.5% | 65% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.03 / 1M |
Quem vence em cada categoria?
At-a-glance decision frameworkEvaluations ongoing
Pontuações 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.
Qwen3.5 27B · Strengths & Key Advantages
- Janela de contexto de 262K com limite máximo de saída de 8K tokens (GPQA Diamond 56.5% · MMLU-Pro 74%)
- Taxa de transferência rápida de 90 tokens/segundo com latência inicial (TTFT) de 410 ms
- Pesos abertos (27B) 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
DeepSeek Chat · Strengths & Key Advantages
- Janela de contexto de 1M com limite máximo de saída de 16K tokens (SWE-bench Verified 54% · GPQA Diamond 65%)
- Taxa de transferência rápida de 75 tokens/segundo com latência inicial (TTFT) de 520 ms
- Pesos abertos (671B Total (37B )) 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 | Qwen3.5 27B | DeepSeek Chat |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.15 |
Output Tokens / Task Tokens generated per task | 13K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Especificações Técnicas
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen3.5 27B | DeepSeek Chat |
|---|---|---|
| Official API Model ID (SDK) | qwen3-5-27b-instruct | deepseek-chat |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-02 | 2025-12 |
| Knowledge Cutoff | December 2025 | September 2025 |
| Architecture | Dense Multimodal Autoregressive Transformer | Multi-Head Latent Attention (MLA) Sparse MoE |
| Parameters | 27B | 671B Total (37B Active) |
| 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) | Free across full context window (only KV-cache VRAM scales) | Flat rate ($0.28/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix caching at $0.028/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 17 GB VRAM (4-bit Quantized) · 58 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 416 GB VRAM (4-bit Quantized) · 1443 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, vision | text |
| Output Modalities | text | text |
Qwen3.5 27B vs DeepSeek Chat: Respostas Rápidas (FAQ)
Which is more intelligent, Qwen3.5 27B or DeepSeek Chat?
Qwen3.5 27B is more intelligent. It scores 75 compared with 50 on the composite Intelligence Index.
Which model is faster?
Qwen3.5 27B is faster, generating 90 tokens per second compared with 75 tokens per second.
Which model is cheaper to run?
Qwen3.5 27B is cheaper at $0.00 per 1M tokens blended, compared with $0.12 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen3.5 27B has lower latency, with a time to first token of 0.41s compared with 0.52s.
Which model has a larger context window?
DeepSeek Chat has a larger context window, supporting 1M tokens compared with 262.1K tokens.
Veredito
Tanto Qwen3.5 27B (Indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas RAG corporativas e integração escalável via API.) quanto DeepSeek Chat (Indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: Qwen3.5 27B lidera em inteligência geral e programação, Qwen3.5 27B oferece geração mais rápida e Qwen3.5 27B apresenta o melhor custo-benefício em produção.
Perguntas Frequentes (FAQ)
Qual é melhor para programação e raciocínio, Qwen3.5 27B ou DeepSeek Chat?+
Nas avaliações de inteligência e código, Qwen3.5 27B leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.
Qual modelo tem a API mais barata, Qwen3.5 27B ou DeepSeek Chat?+
O Qwen3.5 27B custa $0.00 / $0.00 por 1M de tokens (entrada/saída) e o DeepSeek Chat custa $0.28 / $0.42. No custo combinado, Qwen3.5 27B é mais econômico.
Qual modelo possui maior janela de contexto e velocidade?+
O Qwen3.5 27B suporta 262.1K tokens de contexto contra 1M tokens do DeepSeek Chat, enquanto Qwen3.5 27B 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
- DeepSeek Chat vs. Qwen3.5 9B: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
- Qwen3.5 27B vs. Qwen3-Next 80B-A3B Instruct: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
- Qwen3.5 35B-A3B vs. DeepSeek Chat: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
- Qwen3.5 35B-A3B vs. Qwen3.5 27B: Comparação de Benchmarks, Preços API e Velocidade (2026)Comparar
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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 DeepSeek e benchmarks independentes (2026).