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Head-to-Head Model Comparison

GLM-4.5-Flash vs Gemini 4 Argon

Benchmarks, API Token Pricing, Context Window & Speed Comparison

تمت المراجعة بواسطة فريق أبحاث GetInfoUsآخر مراجعة: October 2026السياسة التحريرية
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Zhipu AIGooglelightweightflagship

Compare GLM-4.5-Flash by Zhipu AI against Gemini 4 Argon by Google across real-world coding, reasoning, token economics, and latency.

مقارنة مباشرة (VS)

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GLM-4.5-Flash

استدلال فائق السرعة

نموذج GLM-4.5-Flash من Zhipu AI مصمم خصيصاً لـالتفاعل الفوري بزمن استجابة منخفض جداً وتوجيه الطلبات عالية الكثافة، حيث يوفر نافذة سياق بسعة 131K وسرعة 170 رمز/ثانية ومؤشر ذكاء 72/100 (بتكلفة $0.01/$0.02 لكل مليون رمز).

Intelligence Index72 / 100
Input Token:

$0.01 / 1M

Output Token:

$0.02 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

N/A

Google

Gemini 4 Argon

ذكاء اصطناعي متعدد الوسائط

نموذج Gemini 4 Argon من Google مصمم خصيصاً لـالفهم الأصلي متعدد الوسائط للنصوص والصور وتحليل المخططات والمستندات، حيث يوفر نافذة سياق بسعة 1M وسرعة 115 رمز/ثانية ومؤشر ذكاء 97/100 (بتكلفة $2/$10 لكل مليون رمز).

Intelligence Index97 / 100
Input Token:

$2.00 / 1M

Output Token:

$10.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

81.9%

أهم الفروقات في لمحة

  • GLM-4.5-Flash is 352.2x cheaper for API usage (input + output tokens combined).
  • Gemini 4 Argon has a larger context window (1M vs 131K tokens).
  • GLM-4.5-Flash is faster at 170 tok/s vs 115 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGLM-4.5-FlashGemini 4 Argon
Intelligence Index
Composite reasoning & knowledge score (0-100)
7297
Provider
Zhipu AIGoogle
Input Price
Cost per million input tokens
$0.01 / 1M$2.00 / 1M
Output Price
Cost per million output tokens
$0.02 / 1M$10.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.00 / 1M$1.47 / 1M
Context Window
Maximum input + output tokens
131.1K tokens~197 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
8K1000K
Throughput Speed
Tokens generated per second
170 tok/s115 tok/s
SWE-bench Verified
Real-world software engineering
N/A81.9%
GPQA Diamond
Graduate-level reasoning
N/A81.5%
Cached Input Price
Prompt caching discount per 1M tokens
$0.00 / 1M$0.10 / 1M

من يتفوق في كل مجال؟

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
GLM-4.5-Flash
352.2x cheaper monthly runtime
Speed
GLM-4.5-Flash
+48% faster generation
Context
Gemini 4 Argon
1M tokens window

نتائج الاختبارات المعيارية الموثقة

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

GLM-4.5-FlashGemini 4 Argon
SWE-bench Verified (Autonomous Coding)

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

N/Avs81.9%
GPQA Diamond (Doctorate-Level Science & Math)

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

N/Avs81.5%
MMLU-Pro (Multi-task Language Understanding)

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

68%vs90.8%
LMSYS Chatbot Arena Elo

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

1240ptsvs1525pts
Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

N/Avs18.9%
Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

N/Avs57.4%
SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

N/Avs56.4%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs80.5%
AIME (Advanced Math Competition)

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

N/Avs94.8%
Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

N/Avs96.4%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

170 tok/s vs115 tok/s

زمن الاستجابة والسرعة

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-4.5-Flash0.22s
Gemini 4 Argon0.85s
Est. End-to-End Response (500 tokens)TTFT + generation time
GLM-4.5-Flash3.16s
Gemini 4 Argon5.20s

محاكي تكلفة الرموز التفاعلي

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-4.5-Flash Monthly API Bill
$0.03 / month
$0.01/1M in · $0.02/1M out
Gemini 4 Argon Monthly API Bill
$12.15 / month
$2/1M in · $10/1M out
GLM-4.5-Flash saves approximately $12.12 / month (100%) under this workload.352.2x Savings

GLM-4.5-Flash · Strengths & Key Advantages

  • نافذة سياق بسعة 131K وحد أقصى للمخرجات يصل إلى 8K رمز (MMLU-Pro 68% · Arena Elo 1240)
  • سرعة توليد عالية تبلغ 170 رمز/ثانية مع زمن استجابة لأول رمز (TTFT) قدره 220 مللي ثانية
  • تسعير API تنافسي (بتكلفة $0.01/$0.02 لكل مليون رمز) مع دعم التخزين المؤقت للأوامر والمخرجات المهيكلة بصيغة JSON
Considerations & Trade-offs
  • نموذج سحابي مغلق الأوزان لا يدعم تنزيل الأوزان للتشغيل المحلي المعزول عن الإنترنت
  • في المسائل البحثية فائقة التعقيد، قد تتفوق نماذج الاستدلال الثقيلة المتخصصة في عمق التحليل

Gemini 4 Argon · Strengths & Key Advantages

  • نافذة سياق بسعة 1M وحد أقصى للمخرجات يصل إلى 1M رمز (SWE-bench Verified 81.9% · GPQA Diamond 81.5%)
  • سرعة توليد عالية تبلغ 115 رمز/ثانية مع زمن استجابة لأول رمز (TTFT) قدره 850 مللي ثانية
  • تسعير API تنافسي (بتكلفة $2/$10 لكل مليون رمز) مع دعم التخزين المؤقت للأوامر والمخرجات المهيكلة بصيغة JSON
Considerations & Trade-offs
  • نموذج سحابي مغلق الأوزان لا يدعم تنزيل الأوزان للتشغيل المحلي المعزول عن الإنترنت
  • في حالات الاستخدام الكثيف، يُنصح بتفعيل التخزين المؤقت (Prompt Caching) وBatch API لترشيد النفقات

Token Use & Task Economics

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

MetricGLM-4.5-FlashGemini 4 Argon
Cost per Task
Estimated USD to finish one task
$0.01$2.45
Output Tokens / Task
Tokens generated per task
10K75K
Reasoning Tokens / Task
Thinking tokens burned per task
0K36K

المواصفات التقنية

Architecture, release timeline, capabilities, and modalities.

FeatureGLM-4.5-FlashGemini 4 Argon
Official API Model ID (SDK)glm-4-5-flashmodels/gemini-4-argon
API ProtocolZhipu AI REST API (OpenAI-Compatible)Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date2025-072026-09
Knowledge CutoffMay 2025June 2026
ArchitectureUltra-High-Speed Distilled TransformerPathways Multimodal Sparse Mixture-of-Experts
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($0.01/1M in) across full 131K windowStandard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTLCached input at $0.001/1M tokensImplicit + Explicit Context Caching at $0.1/1M with configurable TTL
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 toolsBuilt-in Google Search Grounding + Managed Python Code Execution Sandbox
Official Fine-Tuning SupportAPI Prompt Engineering & RAG DistillationVertex AI Supervised Adapter Tuning & Context Distillation
Multimodal Token BillingText BPE (~4 chars/token)Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec)
Quantization & Min GPU VRAMCloud-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 ClassificationZhipu AI Production Safety & Alignment VerifiedGoogle DeepMind Frontier Safety Framework Verified
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise TierSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestexttext, vision, audio, video
Output Modalitiestexttext

GLM-4.5-Flash vs Gemini 4 Argon: إجابات سريعة (FAQ)

Which is more intelligent, GLM-4.5-Flash or Gemini 4 Argon?

Gemini 4 Argon is more intelligent. It scores 97 compared with 72 on the composite Intelligence Index.

Which model is faster?

GLM-4.5-Flash is faster, generating 170 tokens per second compared with 115 tokens per second.

Which model is cheaper to run?

GLM-4.5-Flash is cheaper at $0.00 per 1M tokens blended, compared with $1.47 (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 0.85s.

Which model has a larger context window?

Gemini 4 Argon has a larger context window, supporting 1M tokens compared with 131.1K tokens.

الحكم النهائي

Choose GLM-4.5-Flash for مثالي لـالتفاعل الفوري بزمن استجابة منخفض جداً وتوجيه الطلبات عالية الكثافة وأنظمة rag المؤسسية وربط واجهات البرمجة (api) القابلة للتوسع., or choose Gemini 4 Argon for مثالي لـالفهم الأصلي متعدد الوسائط للنصوص والصور وتحليل المخططات والمستندات وأنظمة rag المؤسسية وربط واجهات البرمجة (api) القابلة للتوسع..

الأسئلة الشائعة (FAQ)

Which is cheaper: GLM-4.5-Flash or Gemini 4 Argon?+

GLM-4.5-Flash costs $0.01/1M input and $0.02/1M output tokens, while Gemini 4 Argon costs $2/1M input and $10/1M output tokens.

Which model has a larger context window: GLM-4.5-Flash or Gemini 4 Argon?+

GLM-4.5-Flash supports 131K tokens whereas Gemini 4 Argon supports 1M tokens.

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.

المصادر الرسمية: Zhipu AI & Google Official Documentation