Head-to-Head Model Comparison

Llama 4 Scout vs Gemini 4 Argon

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Vérifié par l’équipe de recherche de GetInfoUsDernière révision : October 2026Politique éditoriale

Changez et Comparez les Modèles

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

Llama 4 ScoutProfile

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 4 Scout

Modèle de Base à Poids Ouverts

Llama 4 Scout de Meta est un modèle d’IA conçu pour l’auto-hébergement sur infrastructure privée, le fine-tuning et la souveraineté des données, offrant une fenêtre de contexte de 3.5M, une vitesse de 95 tokens/s, un indice d’intelligence d

Intelligence Index76 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

3.5M tokens

SWE-bench Verified:

N/A

Google

Gemini 4 Argon

IA Multimodale Native

Gemini 4 Argon de Google est un modèle d’IA conçu pour la compréhension multimodale native (texte, vision) et l’analyse de documents complexes, offrant une fenêtre de contexte de 1M, une vitesse de 115 tokens/s, un indice d’intelligence de

Intelligence Index97 / 100

Input Token:

$2.00 / 1M

Output Token:

$10.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

81.9%

Différences Clés en un Coup d’Œil

Llama 4 Scout is 1.0x cheaper for API usage (input + output tokens combined).
Llama 4 Scout has a larger context window (4M vs 1M tokens).
Gemini 4 Argon is faster at 115 tok/s vs 95 tok/s.

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

Caractéristique	Llama 4 Scout	Gemini 4 Argon
Intelligence Index Composite reasoning & knowledge score (0-100)	76	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	3.5M tokens~5,250 A4 pages of text	1M tokens~1,500 A4 pages of text
Max Output Tokens Per single request	33K	1000K
Throughput Speed Tokens generated per second	95 tok/s	115 tok/s
SWE-bench Verified Real-world software engineering	N/A	81.9%
GPQA Diamond Graduate-level reasoning	N/A	81.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.00 / 1M	$0.10 / 1M

Qui gagne où ?

At-a-glance decision framework

Code

Evaluations ongoing

Coût

Llama 4 Scout

1.0x cheaper monthly runtime

Vitesse

Gemini 4 Argon

+21% faster generation

Contexte

Llama 4 Scout

3.5M tokens window

Scores de Benchmarks Vérifiés

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

Llama 4 ScoutGemini 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.

78%vs90.8%

LMSYS Chatbot Arena Elo

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

1370ptsvs1525pts

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.

92.5%vs96.4%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

95 tok/s vs115 tok/s

Latence et Temps de Réponse

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 4 Scout0.65s

Gemini 4 Argon0.85s

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

Llama 4 Scout5.91s

Gemini 4 Argon5.20s

Simulateur Interactif de Coût de Tokens

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 4 Scout 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 4 Scout saves approximately $12.15 / month (100%) under this workload.1.0x Savings

Llama 4 Scout · Strengths & Key Advantages

Fenêtre de contexte de 3.5M et capacité de sortie maximale de 33K tokens (MMLU-Pro 78% · Arena Elo 1370)
Débit rapide de 95 tokens/seconde avec une latence au premier token (TTFT) de 650 ms
Poids ouverts (17B (109B total MoE)) permettant un déploiement sur site ou cloud privé via vLLM, SGLang ou Ollama

Considerations & Trade-offs

Nécessite des serveurs GPU dotés de suffisamment de mémoire VRAM pour l’inférence locale à pleine précision
Sur les problèmes de recherche fondamentale très complexes, les modèles de raisonnement lourd restent supérieurs

Gemini 4 Argon · Strengths & Key Advantages

Fenêtre de contexte de 1M et capacité de sortie maximale de 1M tokens (SWE-bench Verified 81.9% · GPQA Diamond 81.5%)
Débit rapide de 115 tokens/seconde avec une latence au premier token (TTFT) de 850 ms
Tarification API optimisée ($2/$10 par million de tokens) avec prise en charge du cache de prompts et des schémas JSON stricts

Considerations & Trade-offs

Modèle cloud propriétaire ne permettant pas le téléchargement local des poids sur serveur isolé
Pour les charges à très fort volume, l’activation du cache de prompts et de l’API Batch est recommandée

Token Use & Task Economics

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

Metric	Llama 4 Scout	Gemini 4 Argon
Cost per Task Estimated USD to finish one task	$0.00	$2.45
Output Tokens / Task Tokens generated per task	10K	75K
Reasoning Tokens / Task Thinking tokens burned per task	0K	36K

Spécifications Techniques

Architecture, release timeline, capabilities, and modalities.

Caractéristique	Llama 4 Scout	Gemini 4 Argon
Official API Model ID (SDK)	meta-llama/Llama-4-Scout	models/gemini-4-argon
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date	2025-04	2026-09
Knowledge Cutoff	January 2025	June 2026
Architecture	Extreme-context sparse MoE multimodal transformer (17B active / 109B total)	Pathways Multimodal Sparse Mixture-of-Experts
Parameters	17B (109B total 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	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) · 11 GB VRAM (4-bit Quantized) · 37 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 4 Scout vs Gemini 4 Argon: Réponses Rapides (FAQ)

Which is more intelligent, Llama 4 Scout or Gemini 4 Argon?

Gemini 4 Argon is more intelligent. It scores 97 compared with 76 on the composite Intelligence Index.

Which model is faster?

Gemini 4 Argon is faster, generating 115 tokens per second compared with 95 tokens per second.

Which model is cheaper to run?

Llama 4 Scout 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 4 Scout has lower latency, with a time to first token of 0.65s compared with 0.85s.

Which model has a larger context window?

Llama 4 Scout has a larger context window, supporting 3.5M tokens compared with 1M tokens.

Verdict

Choose Llama 4 Scout for conçu pour l’auto-hébergement sur infrastructure privée, le fine-tuning et la souveraineté des données, l’intégration rag et les pipelines de production évolutifs., or choose Gemini 4 Argon for conçu pour la compréhension multimodale native (texte, vision) et l’analyse de documents complexes, l’intégration rag et les pipelines de production évolutifs..

Questions fréquentes

Which is cheaper: Llama 4 Scout or Gemini 4 Argon?+

Llama 4 Scout 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 4 Scout or Gemini 4 Argon?+

Llama 4 Scout supports 3.5M 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 4 Scout vs Gemini 4 Argon

Changez et Comparez les Modèles

Llama 4 Scout

Gemini 4 Argon

Qui gagne où ?

Scores de Benchmarks Vérifiés

Latence et Temps de Réponse

Simulateur Interactif de Coût de Tokens

Llama 4 Scout · Strengths & Key Advantages

Considerations & Trade-offs

Gemini 4 Argon · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spécifications Techniques

Llama 4 Scout vs Gemini 4 Argon: Réponses Rapides (FAQ)

Verdict

Questions fréquentes

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