GetInfoUs
Head-to-Head Model Comparison

GLM-4.5 vs Qwen3-Next 80B-A3B (Thinking)

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
分享
Related topics (12)
AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
GLM-4.5 vs Qwen3-Next 80B-A3B (Thinking): Benchmarks, Pricing & Speed Comparison

Zhipu AI 的 GLM-4.5 与 Alibaba 的 Qwen3-Next 80B-A3B (Thinking) 是当前备受关注的两款主力 AI 模型。GLM-4.5 提供 131.1K 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 (Thinking) 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)

切换并对比任意模型

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Profile
Profile
Multi-Model Arena Stack:
5-Model Matrix Workbench
Popular Matchups:
Zhipu AI

GLM-4.5

开源权重推理大模型

Zhipu AI 推出的 GLM-4.5 是一款通过大规模强化学习训练、支持私有化部署的开源权重推理大模型，具备 131K 上下文窗口、75 Token/秒生成速度与 80/100 综合智能指数（免费开源权重）。

Intelligence Index80 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

42%

Alibaba

Qwen3-Next 80B-A3B (Thinking)

开源权重基础大模型

Alibaba 推出的 Qwen3-Next 80B-A3B (Thinking) 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、95 Token/秒生成速度与 82/100 综合智能指数（免费开源权重）。

Intelligence Index82 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

46%

核心差异一览

  • Both models cost the same under this workload — choose on capability, not price.
  • Qwen3-Next 80B-A3B (Thinking) is faster at 95 tok/s vs 75 tok/s.
  • Qwen3-Next 80B-A3B (Thinking) leads on SWE-bench coding (46.0% vs 42.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGLM-4.5Qwen3-Next 80B-A3B (Thinking)
Intelligence Index
Composite reasoning & knowledge score (0-100)
8082
Provider
Zhipu AIAlibaba
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
131.1K tokens~197 A4 pages of text131.1K tokens~197 A4 pages of text
Max Output Tokens
Per single request
8K16K
Throughput Speed
Tokens generated per second
75 tok/s95 tok/s
SWE-bench Verified
Real-world software engineering
42%46%
GPQA Diamond
Graduate-level reasoning
61%68.5%

各项指标谁更胜一筹？

At-a-glance decision framework
Coding
Qwen3-Next 80B-A3B (Thinking)
+4.0% SWE-bench lead
Cost
Tied
Identical monthly runtime cost
Speed
Qwen3-Next 80B-A3B (Thinking)
+27% faster generation
Context
Tied
131.1K tokens window

权威基准测试得分对比

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

GLM-4.5Qwen3-Next 80B-A3B (Thinking)
SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

42%vs46%
GPQA Diamond (Doctorate-Level Science & Math)

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

61%vs68.5%
MMLU-Pro (Multi-task Language Understanding)

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

75%vs80%
LMSYS Chatbot Arena Elo

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

1290ptsvs1345pts
AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

N/Avs82%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

75 tok/s vs95 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
GLM-4.50.55s
Qwen3-Next 80B-A3B (Thinking)0.54s
Est. End-to-End Response (500 tokens)TTFT + generation time
GLM-4.57.22s
Qwen3-Next 80B-A3B (Thinking)5.80s

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)
GLM-4.5 Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
Qwen3-Next 80B-A3B (Thinking) 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

GLM-4.5 · 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（SWE-bench Verified 42% · GPQA Diamond 61%）
  • 高达 75 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 550 毫秒
  • 开放模型权重 (130B 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

Qwen3-Next 80B-A3B (Thinking) · Strengths & Key Advantages

  • 提供 131K 上下文窗口与最高 16K 输出容量，核心基准表现优异（SWE-bench Verified 46% · GPQA Diamond 68.5%）
  • 高达 95 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 540 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.

MetricGLM-4.5Qwen3-Next 80B-A3B (Thinking)
Cost per Task
Estimated USD to finish one task
$0.00$0.00
Output Tokens / Task
Tokens generated per task
16K20K
Reasoning Tokens / Task
Thinking tokens burned per task
6K12K

技术规格参数

Architecture, release timeline, capabilities, and modalities.

FeatureGLM-4.5Qwen3-Next 80B-A3B (Thinking)
Official API Model ID (SDK)glm-4-5qwen3-next-80b-a3b-thinking-instruct
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2025-072025-09
Knowledge CutoffMay 2025August 2025
ArchitectureHybrid Reasoning Mixture-of-Experts TransformerDeliberate Reasoning Sparse Mixture-of-Experts
Parameters130B MoE80B 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 AgentAPI / 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 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)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 81 GB VRAM (4-bit Quantized) · 280 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 Modalitiestexttext
Output Modalitiestexttext

GLM-4.5 vs Qwen3-Next 80B-A3B (Thinking): 常见问题解答 (FAQ)

Which is more intelligent, GLM-4.5 or Qwen3-Next 80B-A3B (Thinking)?

Qwen3-Next 80B-A3B (Thinking) is more intelligent. It scores 82 compared with 80 on the composite Intelligence Index.

Which model is faster?

Qwen3-Next 80B-A3B (Thinking) is faster, generating 95 tokens per second compared with 75 tokens per second.

Which model has lower latency?

Qwen3-Next 80B-A3B (Thinking) has lower latency, with a time to first token of 0.54s compared with 0.55s.

综合结论

综合来看，GLM-4.5（适合：通过大规模强化学习训练、支持私有化部署的开源权重推理任务、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 (Thinking)（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 (Thinking) 在综合智能与编程基准测试中表现更强，Qwen3-Next 80B-A3B (Thinking) 拥有更快的 Token 生成速度，而 GLM-4.5 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，GLM-4.5 和 Qwen3-Next 80B-A3B (Thinking) 哪个更强？+

在综合智能指数与编程基准评测中，Qwen3-Next 80B-A3B (Thinking) 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。

GLM-4.5 与 Qwen3-Next 80B-A3B (Thinking) 的 API 价格哪个更便宜？+

GLM-4.5 的 API 价格为输入 $0.00 / 输出 $0.00（每百万 Token），Qwen3-Next 80B-A3B (Thinking) 为输入 $0.00 / 输出 $0.00。综合混合成本来看，GLM-4.5 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？+

GLM-4.5 支持 131.1K Token 上下文窗口，而 Qwen3-Next 80B-A3B (Thinking) 支持 131.1K Token。在流式生成速度方面，Qwen3-Next 80B-A3B (Thinking) 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: Zhipu AI 与 Alibaba 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。