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Head-to-Head Model Comparison

Qwen3.5 27B vs Qwen3-Next 80B-A3B Instruct

Benchmarks, API Token Pricing, Context Window & Speed Comparison

由 GetInfoUs 研究团队审核最后审核日期：October 2026编辑政策
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Qwen3.5 27B vs Qwen3-Next 80B-A3B Instruct: Benchmarks, Pricing & Speed Comparison

Alibaba 的 Qwen3.5 27B 与 Alibaba 的 Qwen3-Next 80B-A3B Instruct 是当前备受关注的两款主力 AI 模型。Qwen3.5 27B 提供 262.1K Token 上下文窗口，API 定价为每百万 Token 输入 $0.00 / 输出 $0.00；而 Qwen3-Next 80B-A3B Instruct 提供 131.1K Token 上下文窗口，定价为每百万 Token 输入 $0.00 / 输出 $0.00。以下为两者的基准测试、延迟与成本全面对比。

模型直接对比 (VS)

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Popular Matchups:
Alibaba

Qwen3.5 27B

开源权重基础大模型

Alibaba 推出的 Qwen3.5 27B 是一款支持私有云服务器自托管、微调与完全数据主权的开源权重基础大模型，具备 262K 上下文窗口、90 Token/秒生成速度与 75/100 综合智能指数（免费开源权重）。

Intelligence Index75 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

N/A

Alibaba

Qwen3-Next 80B-A3B Instruct

开源权重基础大模型

Alibaba 推出的 Qwen3-Next 80B-A3B Instruct 是一款支持私有云服务器自托管、微调与完全数据主权的开源权重基础大模型，具备 131K 上下文窗口、135 Token/秒生成速度与 78/100 综合智能指数（免费开源权重）。

Intelligence Index78 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

N/A

核心差异一览

  • Both models cost the same under this workload — choose on capability, not price.
  • Qwen3.5 27B has a larger context window (262K vs 131K tokens).
  • Qwen3-Next 80B-A3B Instruct is faster at 135 tok/s vs 90 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureQwen3.5 27BQwen3-Next 80B-A3B Instruct
Intelligence Index
Composite reasoning & knowledge score (0-100)
7578
Provider
AlibabaAlibaba
Input Price
Cost per million input tokens
$0.00 / 1M$0.00 / 1M
Output Price
Cost per million output tokens
$0.00 / 1M$0.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.00 / 1M$0.00 / 1M
Context Window
Maximum input + output tokens
262.1K tokens~393 A4 pages of text131.1K tokens~197 A4 pages of text
Max Output Tokens
Per single request
8K8K
Throughput Speed
Tokens generated per second
90 tok/s135 tok/s
SWE-bench Verified
Real-world software engineering
N/AN/A
GPQA Diamond
Graduate-level reasoning
56.5%60%

各项指标谁更胜一筹？

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Tied
Identical monthly runtime cost
Speed
Qwen3-Next 80B-A3B Instruct
+50% faster generation
Context
Qwen3.5 27B
262.1K tokens window

权威基准测试得分对比

Official evaluation metrics verified by independent third-party research labs.

Qwen3.5 27BQwen3-Next 80B-A3B Instruct
GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

56.5%vs60%
MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

74%vs78%
LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1285ptsvs1315pts
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

90 tok/s vs135 tok/s

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.

Time to First Token (TTFT)Lower is better
Qwen3.5 27B0.41s
Qwen3-Next 80B-A3B Instruct0.31s
Est. End-to-End Response (500 tokens)TTFT + generation time
Qwen3.5 27B5.97s
Qwen3-Next 80B-A3B Instruct4.01s

实时 Token 成本模拟器

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Qwen3.5 27B Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
Qwen3-Next 80B-A3B Instruct Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
These models cost the same under this workload — no savings either way.Break-even

Qwen3.5 27B · Strengths & Key Advantages

  • 提供 262K 上下文窗口与最高 8K 输出容量，核心基准表现优异（GPQA Diamond 56.5% · MMLU-Pro 74%）
  • 高达 90 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 410 毫秒
  • 开放模型权重 (27B)，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
  • 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
  • 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型

Qwen3-Next 80B-A3B Instruct · Strengths & Key Advantages

  • 提供 131K 上下文窗口与最高 8K 输出容量，核心基准表现优异（GPQA Diamond 60% · MMLU-Pro 78%）
  • 高达 135 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 310 毫秒
  • 开放模型权重 (80B total (3B MoE))，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
  • 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
  • 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricQwen3.5 27BQwen3-Next 80B-A3B Instruct
Cost per Task
Estimated USD to finish one task
$0.00$0.00
Output Tokens / Task
Tokens generated per task
13K15K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

技术规格参数

Architecture, release timeline, capabilities, and modalities.

FeatureQwen3.5 27BQwen3-Next 80B-A3B Instruct
Official API Model ID (SDK)qwen3-5-27b-instructqwen3-next-80b-a3b-instruct-instruct
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2026-022025-09
Knowledge CutoffDecember 2025August 2025
ArchitectureDense Multimodal Autoregressive TransformerStandard Instruction-Tuned Sparse Mixture-of-Experts
Parameters27B80B total (3B active MoE)
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (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)Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTLAutomatic prefix KV-caching in vLLM / SGLang (0 cost)Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull 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 BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / 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) · 50 GB VRAM (4-bit Quantized) · 172 GB VRAM (Native BF16)
Safety & Frontier ClassificationOpen-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 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestext, visiontext
Output Modalitiestexttext

Qwen3.5 27B vs Qwen3-Next 80B-A3B Instruct: 常见问题解答 (FAQ)

Which is more intelligent, Qwen3.5 27B or Qwen3-Next 80B-A3B Instruct?

Qwen3-Next 80B-A3B Instruct is more intelligent. It scores 78 compared with 75 on the composite Intelligence Index.

Which model is faster?

Qwen3-Next 80B-A3B Instruct is faster, generating 135 tokens per second compared with 90 tokens per second.

Which model has lower latency?

Qwen3-Next 80B-A3B Instruct has lower latency, with a time to first token of 0.31s compared with 0.41s.

Which model has a larger context window?

Qwen3.5 27B has a larger context window, supporting 262.1K tokens compared with 131.1K tokens.

综合结论

综合来看，Qwen3.5 27B（适合：支持私有云服务器自托管、微调与完全数据主权的开源权重基础任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）与 Qwen3-Next 80B-A3B Instruct（适合：支持私有云服务器自托管、微调与完全数据主权的开源权重基础任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）各具优势：Qwen3-Next 80B-A3B Instruct 在综合智能与编程基准测试中表现更强，Qwen3-Next 80B-A3B Instruct 拥有更快的 Token 生成速度，而 Qwen3.5 27B 则在规模化 API 部署中具备更高的成本效益。

常见问题 (FAQ)

在编程和深度推理方面，Qwen3.5 27B 和 Qwen3-Next 80B-A3B Instruct 哪个更强？+

在综合智能指数与编程基准评测中，Qwen3-Next 80B-A3B Instruct 表现占优。建议结合上方的 SWE-bench Verified 与 GPQA Diamond 跑分根据具体场景选择。

Qwen3.5 27B 与 Qwen3-Next 80B-A3B Instruct 的 API 价格哪个更便宜？+

Qwen3.5 27B 的 API 价格为输入 $0.00 / 输出 $0.00（每百万 Token），Qwen3-Next 80B-A3B Instruct 为输入 $0.00 / 输出 $0.00。综合混合成本来看，Qwen3.5 27B 更具性价比。

哪款模型拥有更大的上下文窗口和更快的响应速度？+

Qwen3.5 27B 支持 262.1K Token 上下文窗口，而 Qwen3-Next 80B-A3B Instruct 支持 131.1K Token。在流式生成速度方面，Qwen3-Next 80B-A3B Instruct 的吞吐表现更快。

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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: Alibaba 与 Alibaba 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。