Head-to-Head Model Comparison

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

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial

Ganti & Bandingkan Model Apa Saja

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

GLM-4.7-FlashXProfile

Qwen3.5 122B-A10BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Zhipu AI

GLM-4.7-FlashX

Model Ringan & Hemat Biaya

GLM-4.7-FlashX dari Zhipu AI adalah model AI yang dirancang untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, menghadirkan jendela konteks 200K, kecepatan 150 token/detik, indeks kecerdasan 76/100, serta tari

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

AI Multimodal Asli

Qwen3.5 122B-A10B dari Alibaba adalah model AI yang dirancang untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, menghadirkan jendela konteks 262K, kecepatan 78 token/detik, indeks kecerdasan 82/100, serta akse

Intelligence Index82 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

N/A

Perbedaan Utama Sekilas

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 →

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

Siapa Unggul di Mana?

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

Skor Benchmark Terverifikasi

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

Latensi & Waktu Respons

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

Simulator Biaya Token Interaktif

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

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% 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

Jendela konteks 200K dengan kapasitas output maksimum hingga 8K token (MMLU-Pro 74.5% · Arena Elo 1290)
Kecepatan generasi tinggi 150 token/detik dengan latensi token pertama (TTFT) hanya 260 ms
Bobot terbuka yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

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

Jendela konteks 262K dengan kapasitas output maksimum hingga 16K token (GPQA Diamond 65.5% · MMLU-Pro 80.5%)
Kecepatan generasi tinggi 78 token/detik dengan latensi token pertama (TTFT) hanya 510 ms
Bobot terbuka (122B total (10B MoE)) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

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

Spesifikasi Teknis

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: Jawaban Cepat (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.

Kesimpulan

Baik GLM-4.7-FlashX (Sangat cocok untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Qwen3.5 122B-A10B (Sangat cocok untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Qwen3.5 122B-A10B unggul dalam penalaran dan pemrograman, GLM-4.7-FlashX memberikan kecepatan generasi token lebih tinggi, dan Qwen3.5 122B-A10B lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, GLM-4.7-FlashX atau Qwen3.5 122B-A10B?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Qwen3.5 122B-A10B memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API GLM-4.7-FlashX vs Qwen3.5 122B-A10B?+

GLM-4.7-FlashX mengenakan biaya $0.02 / $0.04 per 1M token input/output, sedangkan Qwen3.5 122B-A10B sebesar $0.00 / $0.00. Secara keseluruhan, Qwen3.5 122B-A10B lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

GLM-4.7-FlashX mendukung konteks 200K token dibandingkan 262.1K token pada Qwen3.5 122B-A10B, sementara GLM-4.7-FlashX unggul dalam kecepatan output token per detik.

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 & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

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

Ganti & Bandingkan Model Apa Saja

GLM-4.7-FlashX

Qwen3.5 122B-A10B

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

GLM-4.7-FlashX · Strengths & Key Advantages

Considerations & Trade-offs

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

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

GLM-4.7-FlashX vs Qwen3.5 122B-A10B: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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