Head-to-Head Model Comparison

Llama 3.2 90B Vision 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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किसी भी मॉडल को बदलें और तुलना करें

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

Llama 3.2 90B VisionProfile

Gemini 4 ArgonProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Live comparison: no in-depth editorial review has been published for this pair yet.

Popular Matchups:

Llama 3.2 90B Vision

ओपन-वेट्स मॉडल

Meta द्वारा विकसित Llama 3.2 90B Vision एक उन्नत AI मॉडल है, जिसमें 128K टोकन का कॉन्टेक्स्ट विंडो और $0.0/1M इनपुट तथा $0.0/1M आउटपुट मूल्य निर्धारण है। यह कोडिंग, विश्लेषण और एंटरप्राइज वर्कफ़्लो के लिए उपयुक्त है।

Intelligence Index44 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

28.5%

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

Llama 3.2 90B Vision is 1.0x cheaper for API usage (input + output tokens combined).
Gemini 4 Argon has a larger context window (1M vs 128K tokens).
Gemini 4 Argon is faster at 115 tok/s vs 75 tok/s.
Gemini 4 Argon leads on SWE-bench coding (81.9% vs 28.5%).

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

Feature	Llama 3.2 90B Vision	Gemini 4 Argon
Intelligence Index Composite reasoning & knowledge score (0-100)	44	97
Provider	Meta	Google
Input Price Cost per million input tokens	$0.00 / 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	$0.00 / 1M	$1.47 / 1M
Context Window Maximum input + output tokens	128K tokens~192 A4 pages of text	1M tokens~1,500 A4 pages of text
Max Output Tokens Per single request	8K	1000K
Throughput Speed Tokens generated per second	75 tok/s	115 tok/s
SWE-bench Verified Real-world software engineering	28.5%	81.9%
GPQA Diamond Graduate-level reasoning	48.2%	81.5%
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

Gemini 4 Argon

+53.4% SWE-bench lead

Cost

Llama 3.2 90B Vision

1.0x cheaper monthly runtime

Speed

Gemini 4 Argon

+53% faster generation

Context

Gemini 4 Argon

1M tokens window

सत्यापित बेंचमार्क स्कोर तुलना

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

Llama 3.2 90B VisionGemini 4 Argon

SWE-bench Verified (Autonomous Coding)

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

28.5%vs81.9%

GPQA Diamond (Doctorate-Level Science & Math)

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

48.2%vs81.5%

MMLU-Pro (Multi-task Language Understanding)

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

73.5%vs90.8%

LMSYS Chatbot Arena Elo

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

1260ptsvs1525pts

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.

75 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

Llama 3.2 90B Vision0.58s

Gemini 4 Argon0.85s

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

Llama 3.2 90B Vision7.25s

Gemini 4 Argon5.20s

रियल-टाइम टोकन लागत सिम्युलेटर

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)

Llama 3.2 90B Vision Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Gemini 4 Argon Monthly API Bill

$12.15 / month

$2/1M in · $10/1M out

Llama 3.2 90B Vision saves approximately $12.15 / month (100%) under this workload.1.0x Savings

Llama 3.2 90B Vision · Strengths & Key Advantages

Meta की उन्नत Multimodal Adapter-Fused GQA Transformer आर्किटेक्चर और 128K कॉन्टेक्स्ट विंडो क्षमता
किफायती और पारदर्शी API मूल्य ($0.0/1M इनपुट, $0.0/1M 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.

Metric	Llama 3.2 90B Vision	Gemini 4 Argon
Cost per Task Estimated USD to finish one task	$0.20	$2.45
Output Tokens / Task Tokens generated per task	24K	75K
Reasoning Tokens / Task Thinking tokens burned per task	0K	36K

तकनीकी विशिष्टताएँ (Specs)

Architecture, release timeline, capabilities, and modalities.

Feature	Llama 3.2 90B Vision	Gemini 4 Argon
Official API Model ID (SDK)	meta-llama/Llama-3.2-90B-Vision	models/gemini-4-argon
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date	2024-09	2026-09
Knowledge Cutoff	December 2023	June 2026
Architecture	Multimodal Adapter-Fused GQA Transformer	Pathways Multimodal Sparse Mixture-of-Experts
Parameters	90B	Confidential
Enterprise Rate Limits (RPM / TPM)	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud	10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)	Free across full context window (only KV-cache VRAM scales)	Standard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Implicit + Explicit Context Caching at $0.1/1M with configurable TTL
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	Built-in Google Search Grounding + Managed Python Code Execution Sandbox
Official Fine-Tuning Support	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)	Vertex AI Supervised Adapter Tuning & Context Distillation
Multimodal Token Billing	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)	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 VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 56 GB VRAM (4-bit Quantized) · 194 GB VRAM (Native BF16)	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier Classification	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)	Google DeepMind Frontier Safety Framework Verified
Enterprise Compliance (ZDR/HIPAA/SOC2)	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalities	text, vision	text, vision, audio, video
Output Modalities	text	text

Llama 3.2 90B Vision vs Gemini 4 Argon: त्वरित प्रश्नोत्तर (FAQ)

Which is more intelligent, Llama 3.2 90B Vision or Gemini 4 Argon?

Gemini 4 Argon is more intelligent. It scores 97 compared with 44 on the composite Intelligence Index.

Which model is faster?

Gemini 4 Argon is faster, generating 115 tokens per second compared with 75 tokens per second.

Which model is cheaper to run?

Llama 3.2 90B Vision 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?

Llama 3.2 90B Vision has lower latency, with a time to first token of 0.58s compared with 0.85s.

Which model has a larger context window?

Gemini 4 Argon has a larger context window, supporting 1M tokens compared with 128K tokens.

निष्कर्ष

Choose Llama 3.2 90B Vision for एंटरप्राइज ऑटोमेशन, सॉफ्टवेयर डेवलपमेंट, ai एजेंट्स और meta 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: Llama 3.2 90B Vision or Gemini 4 Argon?+

Llama 3.2 90B Vision costs $0/1M 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: Llama 3.2 90B Vision or Gemini 4 Argon?+

Llama 3.2 90B Vision supports 128K 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 & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Llama 3.2 90B Vision vs Gemini 4 Argon

किसी भी मॉडल को बदलें और तुलना करें

Llama 3.2 90B Vision

Gemini 4 Argon

किस क्षेत्र में कौन आगे है?

सत्यापित बेंचमार्क स्कोर तुलना

लेटेंसी और रिस्पॉन्स समय

रियल-टाइम टोकन लागत सिम्युलेटर

Llama 3.2 90B Vision · Strengths & Key Advantages

Considerations & Trade-offs

Gemini 4 Argon · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

तकनीकी विशिष्टताएँ (Specs)

Llama 3.2 90B Vision vs Gemini 4 Argon: त्वरित प्रश्नोत्तर (FAQ)

निष्कर्ष

अक्सर पूछे जाने वाले प्रश्न (FAQ)

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