DeepSeek Chat vs Qwen3.5 9B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
DeepSeek Chat da DeepSeek e Qwen3.5 9B da Alibaba são dois modelos de IA de destaque. O DeepSeek Chat oferece uma janela de contexto de 1M tokens a $0.28/$0.42 por milhão de tokens, enquanto o Qwen3.5 9B oferece 262.1K 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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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%
Qwen3.5 9B
Modelo Base de Pesos AbertosO Qwen3.5 9B 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 165 tokens/s, índice de inteligência de 70/100 e tari
$0.00 / 1M
$0.00 / 1M
262.1K tokens
N/A
Principais Diferenças em Resumo
- Qwen3.5 9B is 1.0x cheaper for API usage (input + output tokens combined).
- DeepSeek Chat has a larger context window (1M vs 262K tokens).
- Qwen3.5 9B is faster at 165 tok/s vs 75 tok/s.
| Feature | DeepSeek Chat | Qwen3.5 9B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 50 | 70 |
Provider | DeepSeek | Alibaba |
Input Price Cost per million input tokens | $0.28 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.42 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.12 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 262.1K tokens~393 A4 pages of text |
Max Output Tokens Per single request | 16K | 8K |
Throughput Speed Tokens generated per second | 75 tok/s | 165 tok/s |
SWE-bench Verified Real-world software engineering | 54% | N/A |
GPQA Diamond Graduate-level reasoning | 65% | N/A |
Cached Input Price Prompt caching discount per 1M tokens | $0.03 / 1M | $0.00 / 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.
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
Qwen3.5 9B · Strengths & Key Advantages
- Janela de contexto de 262K com limite máximo de saída de 8K tokens (MMLU-Pro 68% · Arena Elo 1230)
- Taxa de transferência rápida de 165 tokens/segundo com latência inicial (TTFT) de 220 ms
- Pesos abertos (9B) 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 | DeepSeek Chat | Qwen3.5 9B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.15 | $0.00 |
Output Tokens / Task Tokens generated per task | 35K | 10K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Especificações Técnicas
Architecture, release timeline, capabilities, and modalities.
| Feature | DeepSeek Chat | Qwen3.5 9B |
|---|---|---|
| Official API Model ID (SDK) | deepseek-chat | qwen3-5-9b-instruct |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-12 | 2026-02 |
| Knowledge Cutoff | September 2025 | December 2025 |
| Architecture | Multi-Head Latent Attention (MLA) Sparse MoE | Compact Dense Autoregressive Transformer |
| Parameters | 671B Total (37B Active) | 9B |
| 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 ($0.28/1M in) across full 1000K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.028/1M (no extra cache-write fee) | 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) | 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) · 416 GB VRAM (4-bit Quantized) · 1443 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 6 GB VRAM (4-bit Quantized) · 19 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 |
DeepSeek Chat vs Qwen3.5 9B: Respostas Rápidas (FAQ)
Which is more intelligent, DeepSeek Chat or Qwen3.5 9B?
Qwen3.5 9B is more intelligent. It scores 70 compared with 50 on the composite Intelligence Index.
Which model is faster?
Qwen3.5 9B is faster, generating 165 tokens per second compared with 75 tokens per second.
Which model is cheaper to run?
Qwen3.5 9B 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 9B has lower latency, with a time to first token of 0.22s 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 DeepSeek Chat (Indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas RAG corporativas e integração escalável via API.) quanto Qwen3.5 9B (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 9B lidera em inteligência geral e programação, Qwen3.5 9B oferece geração mais rápida e Qwen3.5 9B apresenta o melhor custo-benefício em produção.
Perguntas Frequentes (FAQ)
Qual é melhor para programação e raciocínio, DeepSeek Chat ou Qwen3.5 9B?+
Nas avaliações de inteligência e código, Qwen3.5 9B leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.
Qual modelo tem a API mais barata, DeepSeek Chat ou Qwen3.5 9B?+
O DeepSeek Chat custa $0.28 / $0.42 por 1M de tokens (entrada/saída) e o Qwen3.5 9B custa $0.00 / $0.00. No custo combinado, Qwen3.5 9B é mais econômico.
Qual modelo possui maior janela de contexto e velocidade?+
O DeepSeek Chat suporta 1M tokens de contexto contra 262.1K tokens do Qwen3.5 9B, enquanto Qwen3.5 9B 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
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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 DeepSeek e Alibaba e benchmarks independentes (2026).