Head-to-Head Model Comparison

Gemini 2.5 Flash vs GLM-4.5-Flash

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.

Gemini 2.5 FlashProfile

GLM-4.5-FlashProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Google

Gemini 2.5 Flash

Inferensi Super Cepat

Gemini 2.5 Flash dari Google adalah model AI yang dirancang untuk interaksi real-time dengan latensi sangat rendah dan routing bervolume tinggi, menghadirkan jendela konteks 1.0M, kecepatan 160 token/detik, indeks kecerdasan 68/100, serta t

Intelligence Index68 / 100

Input Token:

$0.30 / 1M

Output Token:

$2.50 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

58.2%

Zhipu AI

GLM-4.5-Flash

Inferensi Super Cepat

GLM-4.5-Flash dari Zhipu AI adalah model AI yang dirancang untuk interaksi real-time dengan latensi sangat rendah dan routing bervolume tinggi, menghadirkan jendela konteks 131K, kecepatan 170 token/detik, indeks kecerdasan 72/100, serta ta

Intelligence Index72 / 100

Input Token:

$0.01 / 1M

Output Token:

$0.02 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

N/A

Perbedaan Utama Sekilas

GLM-4.5-Flash is 81.5x cheaper for API usage (input + output tokens combined).
Gemini 2.5 Flash has a larger context window (1M vs 131K tokens).
GLM-4.5-Flash is faster at 170 tok/s vs 160 tok/s.

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

Feature	Gemini 2.5 Flash	GLM-4.5-Flash
Intelligence Index Composite reasoning & knowledge score (0-100)	68	72
Provider	Google	Zhipu AI
Input Price Cost per million input tokens	$0.30 / 1M	$0.01 / 1M
Output Price Cost per million output tokens	$2.50 / 1M	$0.02 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.36 / 1M	$0.00 / 1M
Context Window Maximum input + output tokens	1.05M tokens~1,573 A4 pages of text	131.1K tokens~197 A4 pages of text
Max Output Tokens Per single request	66K	8K
Throughput Speed Tokens generated per second	160 tok/s	170 tok/s
SWE-bench Verified Real-world software engineering	58.2%	N/A
GPQA Diamond Graduate-level reasoning	75.4%	N/A
Cached Input Price Prompt caching discount per 1M tokens	$0.07 / 1M	$0.00 / 1M

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

GLM-4.5-Flash

81.5x cheaper monthly runtime

Speed

GLM-4.5-Flash

+6% faster generation

Context

Gemini 2.5 Flash

1.05M tokens window

Skor Benchmark Terverifikasi

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

Gemini 2.5 FlashGLM-4.5-Flash

SWE-bench Verified (Autonomous Coding)

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

58.2%vsN/A

GPQA Diamond (Doctorate-Level Science & Math)

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

75.4%vsN/A

MMLU-Pro (Multi-task Language Understanding)

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

83.2%vs68%

LMSYS Chatbot Arena Elo

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

1385ptsvs1240pts

AIME (Advanced Math Competition)

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

84.5%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

160 tok/s vs170 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

Gemini 2.5 Flash0.45s

GLM-4.5-Flash0.22s

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

Gemini 2.5 Flash3.58s

GLM-4.5-Flash3.16s

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)

Gemini 2.5 Flash Monthly API Bill

$2.81 / month

$0.3/1M in · $2.5/1M out

GLM-4.5-Flash Monthly API Bill

$0.03 / month

$0.01/1M in · $0.02/1M out

GLM-4.5-Flash saves approximately $2.78 / month (99%) under this workload.81.5x Savings

Gemini 2.5 Flash · Strengths & Key Advantages

Jendela konteks 1.0M dengan kapasitas output maksimum hingga 66K token (SWE-bench Verified 58.2% · GPQA Diamond 75.4%)
Kecepatan generasi tinggi 160 token/detik dengan latensi token pertama (TTFT) hanya 450 ms
Skema harga API kompetitif (tarif $0.3/$2.5 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur

Considerations & Trade-offs

Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

GLM-4.5-Flash · Strengths & Key Advantages

Jendela konteks 131K dengan kapasitas output maksimum hingga 8K token (MMLU-Pro 68% · Arena Elo 1240)
Kecepatan generasi tinggi 170 token/detik dengan latensi token pertama (TTFT) hanya 220 ms
Skema harga API kompetitif (tarif $0.01/$0.02 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur

Considerations & Trade-offs

Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
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	Gemini 2.5 Flash	GLM-4.5-Flash
Cost per Task Estimated USD to finish one task	$0.85	$0.01
Output Tokens / Task Tokens generated per task	32K	10K
Reasoning Tokens / Task Thinking tokens burned per task	14K	0K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Feature	Gemini 2.5 Flash	GLM-4.5-Flash
Official API Model ID (SDK)	models/gemini-2-5-flash	glm-4-5-flash
API Protocol	Google Gemini API (generateContent) + OpenAI-Compatible SDK	Zhipu AI REST API (OpenAI-Compatible)
Release Date	2025-06	2025-07
Knowledge Cutoff	March 2025	May 2025
Architecture	High-throughput sparse multimodal transformer with thinking mode	Ultra-High-Speed Distilled Transformer
Parameters	Confidential	Confidential
Enterprise Rate Limits (RPM / TPM)	10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM	5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)	Standard rate up to 200K tokens; 2x input/output tier above 200K tokens	Flat rate ($0.01/1M in) across full 131K window
Prompt Cache Policy & TTL	Implicit + Explicit Context Caching at $0.075/1M with configurable TTL	Cached input at $0.001/1M tokens
Computer Use / GUI Agent	Supported (Screen coordinate grounding, browser & desktop GUI action loops)	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	Built-in Google Search Grounding + Managed Python Code Execution Sandbox	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning Support	Vertex AI Supervised Adapter Tuning & Context Distillation	API Prompt Engineering & RAG Distillation
Multimodal Token Billing	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec)	Text BPE (~4 chars/token)
Quantization & Min GPU VRAM	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier Classification	Google DeepMind Frontier Safety Framework Verified	Zhipu AI Production Safety & Alignment Verified
Enterprise Compliance (ZDR/HIPAA/SOC2)	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalities	text, vision, audio, video	text
Output Modalities	text	text

Gemini 2.5 Flash vs GLM-4.5-Flash: Jawaban Cepat (FAQ)

Which is more intelligent, Gemini 2.5 Flash or GLM-4.5-Flash?

GLM-4.5-Flash is more intelligent. It scores 72 compared with 68 on the composite Intelligence Index.

Which model is faster?

GLM-4.5-Flash is faster, generating 170 tokens per second compared with 160 tokens per second.

Which model is cheaper to run?

GLM-4.5-Flash is cheaper at $0.00 per 1M tokens blended, compared with $0.36 (7:2:1 cache:input:output ratio).

Which model has lower latency?

GLM-4.5-Flash has lower latency, with a time to first token of 0.22s compared with 0.45s.

Which model has a larger context window?

Gemini 2.5 Flash has a larger context window, supporting 1.05M tokens compared with 131.1K tokens.

Kesimpulan

Baik Gemini 2.5 Flash (Sangat cocok untuk interaksi real-time dengan latensi sangat rendah dan routing bervolume tinggi, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun GLM-4.5-Flash (Sangat cocok untuk interaksi real-time dengan latensi sangat rendah dan routing bervolume tinggi, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: GLM-4.5-Flash unggul dalam penalaran dan pemrograman, GLM-4.5-Flash memberikan kecepatan generasi token lebih tinggi, dan GLM-4.5-Flash lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Gemini 2.5 Flash atau GLM-4.5-Flash?+

Dalam indeks kecerdasan gabungan dan benchmark coding, GLM-4.5-Flash memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Gemini 2.5 Flash vs GLM-4.5-Flash?+

Gemini 2.5 Flash mengenakan biaya $0.30 / $2.50 per 1M token input/output, sedangkan GLM-4.5-Flash sebesar $0.01 / $0.02. Secara keseluruhan, GLM-4.5-Flash lebih hemat biaya.

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

Gemini 2.5 Flash mendukung konteks 1.05M token dibandingkan 131.1K token pada GLM-4.5-Flash, sementara GLM-4.5-Flash 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)

Gemini 2.5 Flash vs GLM-4.5-Flash

Ganti & Bandingkan Model Apa Saja

Gemini 2.5 Flash

GLM-4.5-Flash

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

Gemini 2.5 Flash · Strengths & Key Advantages

Considerations & Trade-offs

GLM-4.5-Flash · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

Gemini 2.5 Flash vs GLM-4.5-Flash: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

Related reading