Head-to-Head Model Comparison

Gemma 3 vs Llama 4 Scout

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial

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

Gemma 3Profile

Llama 4 ScoutProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Google

Gemma 3

AI Multimodal Asli

Gemma 3 dari Google adalah model AI yang dirancang untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, menghadirkan jendela konteks 131K, kecepatan 105 token/detik, indeks kecerdasan 68/100, serta akses gratis o

Intelligence Index68 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

N/A

Llama 4 Scout

Model Dasar Bobot Terbuka

Llama 4 Scout dari Meta adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 3.5M, kecepatan 95 token/detik, indeks kecerdasan 76/100, serta akses gratis o

Intelligence Index76 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

3.5M tokens

SWE-bench Verified:

N/A

Perbedaan Utama Sekilas

Both models cost the same under this workload — choose on capability, not price.
Llama 4 Scout has a larger context window (4M vs 131K tokens).
Gemma 3 is faster at 105 tok/s vs 95 tok/s.

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

Feature	Gemma 3	Llama 4 Scout
Intelligence Index Composite reasoning & knowledge score (0-100)	68	76
Provider	Google	Meta
Input Price Cost per million input tokens	$0.00 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.00 / 1M	$0.00 / 1M
Context Window Maximum input + output tokens	131.1K tokens~197 A4 pages of text	3.5M tokens~5,250 A4 pages of text
Max Output Tokens Per single request	16K	33K
Throughput Speed Tokens generated per second	105 tok/s	95 tok/s
SWE-bench Verified Real-world software engineering	N/A	N/A
GPQA Diamond Graduate-level reasoning	61%	N/A

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Tied

Identical monthly runtime cost

Speed

Gemma 3

+11% faster generation

Context

Llama 4 Scout

3.5M tokens window

Skor Benchmark Terverifikasi

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

Gemma 3Llama 4 Scout

GPQA Diamond (Doctorate-Level Science & Math)

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

61%vsN/A

MMLU-Pro (Multi-task Language Understanding)

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

78%vs78%

LMSYS Chatbot Arena Elo

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

1335ptsvs1370pts

Long-Context Reasoning

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

N/Avs92.5%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

105 tok/s vs95 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

Gemma 30.32s

Llama 4 Scout0.65s

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

Gemma 35.08s

Llama 4 Scout5.91s

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)

Gemma 3 Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Llama 4 Scout Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

These models cost the same under this workload — no savings either way.Break-even

Gemma 3 · Strengths & Key Advantages

Jendela konteks 131K dengan kapasitas output maksimum hingga 16K token (GPQA Diamond 61% · MMLU-Pro 78%)
Kecepatan generasi tinggi 105 token/detik dengan latensi token pertama (TTFT) hanya 320 ms
Bobot terbuka (1B / 4B / 12B / 27B) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

Llama 4 Scout · Strengths & Key Advantages

Jendela konteks 3.5M dengan kapasitas output maksimum hingga 33K token (MMLU-Pro 78% · Arena Elo 1370)
Kecepatan generasi tinggi 95 token/detik dengan latensi token pertama (TTFT) hanya 650 ms
Bobot terbuka (17B (109B total MoE)) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
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	Gemma 3	Llama 4 Scout
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	5K	10K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Feature	Gemma 3	Llama 4 Scout
Official API Model ID (SDK)	models/gemma-3	meta-llama/Llama-4-Scout
API Protocol	Google Gemini API (generateContent) + OpenAI-Compatible SDK	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-03	2025-04
Knowledge Cutoff	December 2024	January 2025
Architecture	Native multimodal open transformer with expanded context	Extreme-context sparse MoE multimodal transformer (17B active / 109B total)
Parameters	1B / 4B / 12B / 27B	17B (109B total MoE)
Enterprise Rate Limits (RPM / TPM)	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)	Free across full context window (only KV-cache VRAM scales)	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Automatic prefix KV-caching in vLLM / SGLang (0 cost)
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	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
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)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 2 GB VRAM (4-bit Quantized) · 4 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 11 GB VRAM (4-bit Quantized) · 37 GB VRAM (Native BF16)
Safety & Frontier Classification	Google DeepMind Frontier Safety Framework Verified	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalities	text, vision	text, vision
Output Modalities	text	text

Gemma 3 vs Llama 4 Scout: Jawaban Cepat (FAQ)

Which is more intelligent, Gemma 3 or Llama 4 Scout?

Llama 4 Scout is more intelligent. It scores 76 compared with 68 on the composite Intelligence Index.

Which model is faster?

Gemma 3 is faster, generating 105 tokens per second compared with 95 tokens per second.

Which model has lower latency?

Gemma 3 has lower latency, with a time to first token of 0.32s compared with 0.65s.

Which model has a larger context window?

Llama 4 Scout has a larger context window, supporting 3.5M tokens compared with 131.1K tokens.

Kesimpulan

Baik Gemma 3 (Sangat cocok untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Llama 4 Scout (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Llama 4 Scout unggul dalam penalaran dan pemrograman, Gemma 3 memberikan kecepatan generasi token lebih tinggi, dan Gemma 3 lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Gemma 3 atau Llama 4 Scout?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Llama 4 Scout memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Gemma 3 vs Llama 4 Scout?+

Gemma 3 mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan Llama 4 Scout sebesar $0.00 / $0.00. Secara keseluruhan, Gemma 3 lebih hemat biaya.

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

Gemma 3 mendukung konteks 131.1K token dibandingkan 3.5M token pada Llama 4 Scout, sementara Gemma 3 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)

Gemma 3 vs Llama 4 Scout

Ganti & Bandingkan Model Apa Saja

Gemma 3

Llama 4 Scout

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

Gemma 3 · Strengths & Key Advantages

Considerations & Trade-offs

Llama 4 Scout · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

Gemma 3 vs Llama 4 Scout: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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