Head-to-Head Model Comparison

GLM-5 vs GLM-4.7-FlashX

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial

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

GLM-5Profile

GLM-4.7-FlashXProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Zhipu AI

GLM-5

Model Dasar Bobot Terbuka

GLM-5 dari Zhipu AI adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 205K, kecepatan 68 token/detik, indeks kecerdasan 87/100, serta akses gratis open-

Intelligence Index87 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

204.8K tokens

SWE-bench Verified:

58%

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

Perbedaan Utama Sekilas

GLM-5 is 1.0x cheaper for API usage (input + output tokens combined).
GLM-5 has a larger context window (205K vs 200K tokens).
GLM-4.7-FlashX is faster at 150 tok/s vs 68 tok/s.

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Feature	GLM-5	GLM-4.7-FlashX
Intelligence Index Composite reasoning & knowledge score (0-100)	87	76
Provider	Zhipu AI	Zhipu AI
Input Price Cost per million input tokens	$0.00 / 1M	$0.02 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$0.04 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.00 / 1M	$0.01 / 1M
Context Window Maximum input + output tokens	204.8K tokens~307 A4 pages of text	200K tokens~300 A4 pages of text
Max Output Tokens Per single request	16K	8K
Throughput Speed Tokens generated per second	68 tok/s	150 tok/s
SWE-bench Verified Real-world software engineering	58%	N/A
GPQA Diamond Graduate-level reasoning	68%	N/A
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

GLM-5

1.0x cheaper monthly runtime

Speed

GLM-4.7-FlashX

+121% faster generation

Context

GLM-5

204.8K tokens window

Skor Benchmark Terverifikasi

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

GLM-5GLM-4.7-FlashX

SWE-bench Verified (Autonomous Coding)

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

58%vsN/A

GPQA Diamond (Doctorate-Level Science & Math)

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

68%vsN/A

MMLU-Pro (Multi-task Language Understanding)

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

84%vs74.5%

LMSYS Chatbot Arena Elo

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

1390ptsvs1290pts

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

68 tok/s vs150 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-50.58s

GLM-4.7-FlashX0.26s

Est. End-to-End Response (500 tokens)TTFT + generation time

GLM-57.93s

GLM-4.7-FlashX3.59s

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-5 Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

GLM-4.7-FlashX Monthly API Bill

$0.07 / month

$0.02/1M in · $0.04/1M out

GLM-5 saves approximately $0.07 / month (100%) under this workload.1.0x Savings

GLM-5 · Strengths & Key Advantages

Jendela konteks 205K dengan kapasitas output maksimum hingga 16K token (SWE-bench Verified 58% · GPQA Diamond 68%)
Kecepatan generasi tinggi 68 token/detik dengan latensi token pertama (TTFT) hanya 580 ms
Bobot terbuka (240B 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

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

Token Use & Task Economics

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

Metric	GLM-5	GLM-4.7-FlashX
Cost per Task Estimated USD to finish one task	$0.00	$0.01
Output Tokens / Task Tokens generated per task	24K	13K
Reasoning Tokens / Task Thinking tokens burned per task	8K	1K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Feature	GLM-5	GLM-4.7-FlashX
Official API Model ID (SDK)	glm-5	glm-4-7-flashx
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-02	2026-01
Knowledge Cutoff	December 2025	November 2025
Architecture	Frontier Sparse Mixture-of-Experts Autoregressive Transformer	Enhanced Latency-Optimized Transformer
Parameters	240B MoE	Confidential
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)	Free across full context window (only KV-cache VRAM scales)	Flat rate ($0.02/1M in) across full 200K window
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Cached input at $0.003/1M tokens
Computer Use / GUI Agent	API / 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 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)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 149 GB VRAM (4-bit Quantized) · 516 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 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
Output Modalities	text	text

GLM-5 vs GLM-4.7-FlashX: Jawaban Cepat (FAQ)

Which is more intelligent, GLM-5 or GLM-4.7-FlashX?

GLM-5 is more intelligent. It scores 87 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 68 tokens per second.

Which model is cheaper to run?

GLM-5 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 0.58s.

Which model has a larger context window?

GLM-5 has a larger context window, supporting 204.8K tokens compared with 200K tokens.

Kesimpulan

Baik GLM-5 (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun 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.) sangat tangguh: GLM-5 unggul dalam penalaran dan pemrograman, GLM-4.7-FlashX memberikan kecepatan generasi token lebih tinggi, dan GLM-5 lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, GLM-5 atau GLM-4.7-FlashX?+

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

Berapa perbandingan harga API GLM-5 vs GLM-4.7-FlashX?+

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

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

GLM-5 mendukung konteks 204.8K token dibandingkan 200K token pada GLM-4.7-FlashX, 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-5 vs GLM-4.7-FlashX

Ganti & Bandingkan Model Apa Saja

GLM-5

GLM-4.7-FlashX

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

GLM-5 · Strengths & Key Advantages

Considerations & Trade-offs

GLM-4.7-FlashX · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

GLM-5 vs GLM-4.7-FlashX: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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