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Head-to-Head Model Comparison

Gemini Embedding 2 vs Gemini 4 Argon

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs अनुसंधान दल द्वारा समीक्षितअंतिम समीक्षा: October 2026संपादकीय नीति
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Compare Gemini Embedding 2 by Google against Gemini 4 Argon by Google across real-world coding, reasoning, token economics, and latency.

आमने-सामने मॉडल तुलना (VS)

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Google

Gemini Embedding 2

उच्च-प्रदर्शन AI मॉडल

Google द्वारा विकसित Gemini Embedding 2 एक उन्नत AI मॉडल है, जिसमें 8K टोकन का कॉन्टेक्स्ट विंडो और $0.2/1M इनपुट तथा $0/1M आउटपुट मूल्य निर्धारण है। यह कोडिंग, विश्लेषण और एंटरप्राइज वर्कफ़्लो के लिए उपयुक्त है।

Intelligence Index78 / 100
Input Token:

$0.20 / 1M

Output Token:

$0.00 / 1M

Context Window:

8.2K tokens

SWE-bench Verified:

N/A

Google

Gemini 4 Argon

डीप रीजनिंग मॉडल

Google द्वारा विकसित Gemini 4 Argon एक उन्नत AI मॉडल है, जिसमें 1M टोकन का कॉन्टेक्स्ट विंडो और $2/1M इनपुट तथा $10/1M आउटपुट मूल्य निर्धारण है। यह कोडिंग, विश्लेषण और एंटरप्राइज वर्कफ़्लो के लिए उपयुक्त है।

Intelligence Index97 / 100
Input Token:

$2.00 / 1M

Output Token:

$10.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

81.9%

एक नज़र में मुख्य अंतर

  • Gemini Embedding 2 is 40.5x cheaper for API usage (input + output tokens combined).
  • Gemini 4 Argon has a larger context window (1M vs 8K tokens).
  • Gemini Embedding 2 is faster at 350 tok/s vs 115 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGemini Embedding 2Gemini 4 Argon
Intelligence Index
Composite reasoning & knowledge score (0-100)
7897
Provider
GoogleGoogle
Input Price
Cost per million input tokens
$0.20 / 1M$2.00 / 1M
Output Price
Cost per million output tokens
$0.00 / 1M$10.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.04 / 1M$1.47 / 1M
Context Window
Maximum input + output tokens
8.2K tokens~12 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
4K1000K
Throughput Speed
Tokens generated per second
350 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

किस क्षेत्र में कौन आगे है?

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Gemini Embedding 2
40.5x cheaper monthly runtime
Speed
Gemini Embedding 2
+204% faster generation
Context
Gemini 4 Argon
1M tokens window

सत्यापित बेंचमार्क स्कोर तुलना

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

Gemini Embedding 2Gemini 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.

N/Avs90.8%
LMSYS Chatbot Arena Elo

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

1340ptsvs1525pts
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.

350 tok/s vs115 tok/s

लेटेंसी और रिस्पॉन्स समय

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 Embedding 20.20s
Gemini 4 Argon0.85s
Est. End-to-End Response (500 tokens)TTFT + generation time
Gemini Embedding 21.63s
Gemini 4 Argon5.20s

रियल-टाइम टोकन लागत सिम्युलेटर

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)
Gemini Embedding 2 Monthly API Bill
$0.30 / month
$0.2/1M in · $0/1M out
Gemini 4 Argon Monthly API Bill
$12.15 / month
$2/1M in · $10/1M out
Gemini Embedding 2 saves approximately $11.85 / month (98%) under this workload.40.5x Savings

Gemini Embedding 2 · Strengths & Key Advantages

  • Google की उन्नत Unified multimodal joint-embedding transformer आर्किटेक्चर और 8K कॉन्टेक्स्ट विंडो क्षमता
  • किफायती और पारदर्शी API मूल्य ($0.2/1M इनपुट, $0/1M आउटपुट) तेज़ रिस्पॉन्स के साथ
  • जटिल कोडिंग, तार्किक विश्लेषण और बहुभाषी कार्यों में उच्च सटीकता
Considerations & Trade-offs
  • बड़े पैमाने पर लंबे कॉन्टेक्स्ट प्रॉम्प्ट्स के लिए टोकन बजट प्रबंधन आवश्यक है
  • विशिष्ट डोमेन कार्यों में सर्वोत्तम परिणाम के लिए सटीक प्रॉम्प्ट इंजीनियरिंग की आवश्यकता होती है

Gemini 4 Argon · Strengths & Key Advantages

  • Google की उन्नत Pathways Multimodal Sparse Mixture-of-Experts आर्किटेक्चर और 1M कॉन्टेक्स्ट विंडो क्षमता
  • किफायती और पारदर्शी API मूल्य ($2/1M इनपुट, $10/1M आउटपुट) तेज़ रिस्पॉन्स के साथ
  • जटिल कोडिंग, तार्किक विश्लेषण और बहुभाषी कार्यों में उच्च सटीकता
Considerations & Trade-offs
  • बड़े पैमाने पर लंबे कॉन्टेक्स्ट प्रॉम्प्ट्स के लिए टोकन बजट प्रबंधन आवश्यक है
  • विशिष्ट डोमेन कार्यों में सर्वोत्तम परिणाम के लिए सटीक प्रॉम्प्ट इंजीनियरिंग की आवश्यकता होती है

Token Use & Task Economics

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

MetricGemini Embedding 2Gemini 4 Argon
Cost per Task
Estimated USD to finish one task
$0.08$2.45
Output Tokens / Task
Tokens generated per task
0K75K
Reasoning Tokens / Task
Thinking tokens burned per task
0K36K

तकनीकी विशिष्टताएँ (Specs)

Architecture, release timeline, capabilities, and modalities.

FeatureGemini Embedding 2Gemini 4 Argon
Official API Model ID (SDK)models/gemini-embedding-2models/gemini-4-argon
API ProtocolGoogle Gemini API (generateContent) + OpenAI-Compatible SDKGoogle Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date2026-042026-09
Knowledge CutoffFebruary 2026June 2026
ArchitectureUnified multimodal joint-embedding transformerPathways Multimodal Sparse Mixture-of-Experts
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 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 ($0.2/1M in) across full 8K windowStandard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTLImplicit + Explicit Context Caching at $0/1M with configurable TTLImplicit + Explicit Context Caching at $0.1/1M with configurable TTL
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingPrompt-based JSON extraction onlyNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxBuilt-in Google Search Grounding + Managed Python Code Execution SandboxBuilt-in Google Search Grounding + Managed Python Code Execution Sandbox
Official Fine-Tuning SupportVertex AI Supervised Adapter Tuning & Context DistillationVertex AI Supervised Adapter Tuning & Context Distillation
Multimodal Token BillingText 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) · 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 ClassificationGoogle DeepMind Frontier Safety Framework 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 Modalitiestext, vision, audio, videotext, vision, audio, video
Output Modalitiesembeddingtext

Gemini Embedding 2 vs Gemini 4 Argon: त्वरित प्रश्नोत्तर (FAQ)

Which is more intelligent, Gemini Embedding 2 or Gemini 4 Argon?

Gemini 4 Argon is more intelligent. It scores 97 compared with 78 on the composite Intelligence Index.

Which model is faster?

Gemini Embedding 2 is faster, generating 350 tokens per second compared with 115 tokens per second.

Which model is cheaper to run?

Gemini Embedding 2 is cheaper at $0.04 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemini Embedding 2 has lower latency, with a time to first token of 0.20s compared with 0.85s.

Which model has a larger context window?

Gemini 4 Argon has a larger context window, supporting 1M tokens compared with 8.2K tokens.

निष्कर्ष

Choose Gemini Embedding 2 for एंटरप्राइज ऑटोमेशन, सॉफ्टवेयर डेवलपमेंट, ai एजेंट्स और google आधारित प्रोडक्शन सिस्टम, or choose Gemini 4 Argon for एंटरप्राइज ऑटोमेशन, सॉफ्टवेयर डेवलपमेंट, ai एजेंट्स और google आधारित प्रोडक्शन सिस्टम.

अक्सर पूछे जाने वाले प्रश्न (FAQ)

Which is cheaper: Gemini Embedding 2 or Gemini 4 Argon?+

Gemini Embedding 2 costs $0.2/1M input and $0/1M output tokens, while Gemini 4 Argon costs $2/1M input and $10/1M output tokens.

Which model has a larger context window: Gemini Embedding 2 or Gemini 4 Argon?+

Gemini Embedding 2 supports 8K 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.

स्रोत: Google & Google Official Documentation