Qwen3.5 27B vs DeepSeek Chat
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen3.5 27B by Alibaba and DeepSeek Chat by DeepSeek represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Qwen3.5 27B holds a notable pricing advantage for token ingestion. On output generation, Qwen3.5 27B is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, DeepSeek Chat provides an expanded memory horizon with 1,000K tokens. In latency-sensitive production environments, Qwen3.5 27B leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Qwen3.5 27B
开源权重模型Alibaba 推出的 Qwen3.5 27B 人工智能模型，具备 262K 上下文窗口，输入价格为 $0/百万 Token，输出价格为 $0/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.00 / 1M
$0.00 / 1M
262.1K tokens
N/A
DeepSeek Chat
开源权重模型DeepSeek 推出的 DeepSeek Chat 人工智能模型，具备 1M 上下文窗口，输入价格为 $0.28/百万 Token，输出价格为 $0.42/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.28 / 1M
$0.42 / 1M
1M tokens
54%
核心差异一览
- 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 |
各项指标谁更胜一筹？
At-a-glance decision frameworkEvaluations ongoing
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.
Qwen3.5 27B · Strengths & Key Advantages
- 由 Alibaba 研发的高精度 Dense Multimodal Autoregressive Transformer 架构，支持 262K 上下文窗口
- 极具竞争力的 API 定价（输入 $0/1M，输出 $0/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
DeepSeek Chat · Strengths & Key Advantages
- 由 DeepSeek 研发的高精度 Multi-Head Latent Attention (MLA) Sparse MoE 架构，支持 1M 上下文窗口
- 极具竞争力的 API 定价（输入 $0.28/1M，输出 $0.42/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
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 |
技术规格参数
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: 常见问题解答 (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.
综合结论
Verdict & Model Selection Recommendation: Choose Qwen3.5 27B if your priority is Dedicated self-hosted API endpoints and vision classification pipelines. Its performance in demanding tasks justifies its operational token cost. Choose DeepSeek Chat if your workflow prioritizes Ultra-low-cost conversational agents, large document querying, and automated customer support. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
常见问题 (FAQ)
How does Qwen3.5 27B compare to DeepSeek Chat in API token pricing and cost per token?+
Qwen3.5 27B costs $0.00 per 1M input tokens and $0.00 per 1M output tokens, whereas DeepSeek Chat costs $0.28 input and $0.42 output. Overall, Qwen3.5 27B is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Qwen3.5 27B or DeepSeek Chat?+
Qwen3.5 27B supports a context window of 262K tokens (~197k words), while DeepSeek Chat supports 1,000K tokens (~750k words). Therefore, DeepSeek Chat can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Qwen3.5 27B or DeepSeek Chat?+
Qwen3.5 27B scores at the frontier of coding benchmarks, while DeepSeek Chat scores 54%. For complex autonomous debugging and agentic engineering, DeepSeek Chat provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Qwen3.5 27B and DeepSeek Chat?+
Qwen3.5 27B is optimized for low-latency direct generation. In contrast, DeepSeek Chat focuses on standard fast inference.
How does inference speed (tokens per second) compare between Qwen3.5 27B and DeepSeek Chat?+
Qwen3.5 27B generates approximately 90 tokens per second, while DeepSeek Chat delivers 75 tokens per second. In latency-sensitive production environments, Qwen3.5 27B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Qwen3.5 27B and DeepSeek Chat to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Qwen3.5 27B to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to DeepSeek Chat. This tiered routing pattern cuts overall API bills by up to 70%.
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.
数据来源: GetInfoUs Independent AI Benchmarks & Official API Specs