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Head-to-Head Model Comparison

GLM-4.7-FlashX vs Qwen3.5 122B-A10B

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
GLM-4.7-FlashX vs Qwen3.5 122B-A10B: Benchmarks, Pricing & Speed Comparison

Zhipu AI 的 GLM-4.7-FlashX 与 Alibaba 的 Qwen3.5 122B-A10B 是当前备受关注的两款主力 AI 模型。GLM-4.7-FlashX 提供 200K Token 上下文窗口，API 定价为每百万 Token 输入 $0.02 / 输出 $0.04；而 Qwen3.5 122B-A10B 提供 262.1K 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。

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Zhipu AI

GLM-4.7-FlashX

轻量级高性价比模型

Zhipu AI 推出的 GLM-4.7-FlashX 是一款兼顾极低 API 调用成本与高吞吐效率的轻量级大模型，具备 200K 上下文窗口、150 Token/秒生成速度与 76/100 综合智能指数（每百万 Token $0.02 输入 / $0.04 输出）。

Intelligence Index76 / 100
Input Token:

$0.02 / 1M

Output Token:

$0.04 / 1M

Context Window:

200K tokens

SWE-bench Verified:

N/A

Alibaba

Qwen3.5 122B-A10B

原生多模态视觉模型

Alibaba 推出的 Qwen3.5 122B-A10B 是一款原生支持图文多模态理解、视觉图表解析与跨模态推理的大模型，具备 262K 上下文窗口、78 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:

262.1K tokens

SWE-bench Verified:

N/A

核心差异一览

  • Qwen3.5 122B-A10B is 1.0x cheaper for API usage (input + output tokens combined).
  • Qwen3.5 122B-A10B has a larger context window (262K vs 200K tokens).
  • GLM-4.7-FlashX is faster at 150 tok/s vs 78 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGLM-4.7-FlashXQwen3.5 122B-A10B
Intelligence Index
Composite reasoning & knowledge score (0-100)
7682
Provider
Zhipu AIAlibaba
Input Price
Cost per million input tokens
$0.02 / 1M$0.00 / 1M
Output Price
Cost per million output tokens
$0.04 / 1M$0.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.01 / 1M$0.00 / 1M
Context Window
Maximum input + output tokens
200K tokens~300 A4 pages of text262.1K tokens~393 A4 pages of text
Max Output Tokens
Per single request
8K16K
Throughput Speed
Tokens generated per second
150 tok/s78 tok/s
SWE-bench Verified
Real-world software engineering
N/AN/A
GPQA Diamond
Graduate-level reasoning
N/A65.5%
Cached Input Price
Prompt caching discount per 1M tokens
$0.00 / 1M$0.00 / 1M

各项指标谁更胜一筹？

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Qwen3.5 122B-A10B
1.0x cheaper monthly runtime
Speed
GLM-4.7-FlashX
+92% faster generation
Context
Qwen3.5 122B-A10B
262.1K tokens window

权威基准测试得分对比

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

GLM-4.7-FlashXQwen3.5 122B-A10B
GPQA Diamond (Doctorate-Level Science & Math)

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

N/Avs65.5%
MMLU-Pro (Multi-task Language Understanding)

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

74.5%vs80.5%
LMSYS Chatbot Arena Elo

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

1290ptsvs1350pts
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

150 tok/s vs78 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-4.7-FlashX0.26s
Qwen3.5 122B-A10B0.51s
Est. End-to-End Response (500 tokens)TTFT + generation time
GLM-4.7-FlashX3.59s
Qwen3.5 122B-A10B6.92s

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-4.7-FlashX Monthly API Bill
$0.07 / month
$0.02/1M in · $0.04/1M out
Qwen3.5 122B-A10B Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
Qwen3.5 122B-A10B saves approximately $0.07 / month (100%) under this workload.1.0x Savings

GLM-4.7-FlashX · Strengths & Key Advantages

  • 提供 200K 上下文窗口与最高 8K 输出容量，核心基准表现优异（MMLU-Pro 74.5% · Arena Elo 1290）
  • 高达 150 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 260 毫秒
  • 开放模型权重，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

Qwen3.5 122B-A10B · Strengths & Key Advantages

  • 提供 262K 上下文窗口与最高 16K 输出容量，核心基准表现优异（GPQA Diamond 65.5% · MMLU-Pro 80.5%）
  • 高达 78 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 510 毫秒
  • 开放模型权重 (122B total (10B 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-4.7-FlashXQwen3.5 122B-A10B
Cost per Task
Estimated USD to finish one task
$0.01$0.00
Output Tokens / Task
Tokens generated per task
13K18K
Reasoning Tokens / Task
Thinking tokens burned per task
1K5K

技术规格参数

Architecture, release timeline, capabilities, and modalities.

FeatureGLM-4.7-FlashXQwen3.5 122B-A10B
Official API Model ID (SDK)glm-4-7-flashxqwen3-5-122b-a10b-instruct
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2026-012026-02
Knowledge CutoffNovember 2025December 2025
ArchitectureEnhanced Latency-Optimized TransformerEfficient Open Multimodal MoE Transformer
ParametersConfidential122B total (10B 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)Flat rate ($0.02/1M in) across full 200K windowFree across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTLCached input at $0.003/1M tokensAutomatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI AgentAPI / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
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) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 76 GB VRAM (4-bit Quantized) · 262 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, vision
Output Modalitiestexttext

GLM-4.7-FlashX vs Qwen3.5 122B-A10B: 常见问题解答 (FAQ)

Which is more intelligent, GLM-4.7-FlashX or Qwen3.5 122B-A10B?

Qwen3.5 122B-A10B is more intelligent. It scores 82 compared with 76 on the composite Intelligence Index.

Which model is faster?

GLM-4.7-FlashX is faster, generating 150 tokens per second compared with 78 tokens per second.

Which model is cheaper to run?

Qwen3.5 122B-A10B is cheaper at $0.00 per 1M tokens blended, compared with $0.01 (7:2:1 cache:input:output ratio).

Which model has lower latency?

GLM-4.7-FlashX has lower latency, with a time to first token of 0.26s compared with 0.51s.

Which model has a larger context window?

Qwen3.5 122B-A10B has a larger context window, supporting 262.1K tokens compared with 200K tokens.

综合结论

综合来看，GLM-4.7-FlashX（适合：兼顾极低 API 调用成本与高吞吐效率的轻量级任务、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 Qwen3.5 122B-A10B（适合：原生支持图文多模态理解、视觉图表解析与跨模态推理的任务、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：Qwen3.5 122B-A10B 在综合智能与编程基准测试中表现更强，GLM-4.7-FlashX 拥有更快的 Token 生成速度，而 Qwen3.5 122B-A10B 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-4.7-FlashX 和 Qwen3.5 122B-A10B 哪个更强？+

在综合智能指数与编程基准评测中，Qwen3.5 122B-A10B 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-4.7-FlashX 与 Qwen3.5 122B-A10B 的 API 价格哪个更便宜？+

GLM-4.7-FlashX 的 API 价格为输入 $0.02 / 输出 $0.04（每百万 Token），Qwen3.5 122B-A10B 为输入 $0.00 / 输出 $0.00。综合混合成本来看，Qwen3.5 122B-A10B 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-4.7-FlashX 支持 200K Token 上下文窗口，而 Qwen3.5 122B-A10B 支持 262.1K Token。在流式生成速度方面，GLM-4.7-FlashX 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)。