GLM-4.7-FlashX vs Qwen3.5 122B-A10B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GLM-4.7-FlashX by Zhipu AI and Qwen3.5 122B-A10B by Alibaba 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 122B-A10B holds a notable pricing advantage for token ingestion. On output generation, Qwen3.5 122B-A10B is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Qwen3.5 122B-A10B provides an expanded memory horizon with 262K tokens. In latency-sensitive production environments, GLM-4.7-FlashX 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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GLM-4.7-FlashX
深度推理模型Zhipu AI 推出的 GLM-4.7-FlashX 人工智能模型，具备 200K 上下文窗口，输入价格为 $0.02/百万 Token，输出价格为 $0.04/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.02 / 1M
$0.04 / 1M
200K tokens
N/A
Qwen3.5 122B-A10B
开源权重模型Alibaba 推出的 Qwen3.5 122B-A10B 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
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.
| Feature | GLM-4.7-FlashX | Qwen3.5 122B-A10B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 76 | 82 |
Provider | Zhipu AI | Alibaba |
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 text | 262.1K tokens~393 A4 pages of text |
Max Output Tokens Per single request | 8K | 16K |
Throughput Speed Tokens generated per second | 150 tok/s | 78 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
GPQA Diamond Graduate-level reasoning | N/A | 65.5% |
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 frameworkEvaluations ongoing
权威基准测试得分对比
Official evaluation metrics verified by independent third-party research labs.
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.
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.
GLM-4.7-FlashX · Strengths & Key Advantages
- 由 Zhipu AI 研发的高精度 Enhanced Latency-Optimized Transformer 架构，支持 200K 上下文窗口
- 极具竞争力的 API 定价（输入 $0.02/1M，输出 $0.04/1M），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
Qwen3.5 122B-A10B · Strengths & Key Advantages
- 由 Alibaba 研发的高精度 Efficient Open Multimodal MoE 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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GLM-4.7-FlashX | Qwen3.5 122B-A10B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.01 | $0.00 |
Output Tokens / Task Tokens generated per task | 13K | 18K |
Reasoning Tokens / Task Thinking tokens burned per task | 1K | 5K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | GLM-4.7-FlashX | Qwen3.5 122B-A10B |
|---|---|---|
| Official API Model ID (SDK) | glm-4-7-flashx | qwen3-5-122b-a10b-instruct |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-01 | 2026-02 |
| Knowledge Cutoff | November 2025 | December 2025 |
| Architecture | Enhanced Latency-Optimized Transformer | Efficient Open Multimodal MoE Transformer |
| Parameters | Confidential | 122B 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 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 ($0.02/1M in) across full 200K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Cached input at $0.003/1M tokens | 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) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| 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) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | BF16 / 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 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, vision |
| Output Modalities | text | text |
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
Verdict & Model Selection Recommendation: Choose GLM-4.7-FlashX if your priority is Mission-critical automated routing, backend agent functions, and high-frequency API bridges. Its performance in demanding tasks justifies its operational token cost. Choose Qwen3.5 122B-A10B if your workflow prioritizes Mid-sized on-premise multimodal deployment and automated document intelligence. 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 GLM-4.7-FlashX compare to Qwen3.5 122B-A10B in API token pricing and cost per token?+
GLM-4.7-FlashX costs $0.02 per 1M input tokens and $0.04 per 1M output tokens, whereas Qwen3.5 122B-A10B costs $0.00 input and $0.00 output. Overall, Qwen3.5 122B-A10B 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: GLM-4.7-FlashX or Qwen3.5 122B-A10B?+
GLM-4.7-FlashX supports a context window of 200K tokens (~150k words), while Qwen3.5 122B-A10B supports 262K tokens (~197k words). Therefore, Qwen3.5 122B-A10B can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GLM-4.7-FlashX or Qwen3.5 122B-A10B?+
GLM-4.7-FlashX scores at the frontier of coding benchmarks, while Qwen3.5 122B-A10B scores competitively for software generation. For complex autonomous debugging and agentic engineering, GLM-4.7-FlashX provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between GLM-4.7-FlashX and Qwen3.5 122B-A10B?+
GLM-4.7-FlashX features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Qwen3.5 122B-A10B provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between GLM-4.7-FlashX and Qwen3.5 122B-A10B?+
GLM-4.7-FlashX generates approximately 150 tokens per second, while Qwen3.5 122B-A10B delivers 78 tokens per second. In latency-sensitive production environments, GLM-4.7-FlashX provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GLM-4.7-FlashX and Qwen3.5 122B-A10B to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Qwen3.5 122B-A10B to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GLM-4.7-FlashX. 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
- GLM-4.7-FlashX vs GLM-4.7-Flash: Benchmarks, Pricing & Speed Comparison对比
- GLM-5 vs GLM-4.7-FlashX: Benchmarks, Pricing & Speed Comparison对比
- Qwen3.5 122B-A10B vs Qwen3-Next 80B-A3B (Thinking): Benchmarks, Pricing & Speed Comparison对比
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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