Head-to-Head Model Comparison

Gemma 4 E4B vs Gemma 4 E2B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial

Ganti & Bandingkan Model Apa Saja

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

Gemma 4 E4BProfile

Gemma 4 E2BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Google

Gemma 4 E4B

Model Dasar Bobot Terbuka

Gemma 4 E4B dari Google adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 131K, kecepatan 190 token/detik, indeks kecerdasan 65/100, serta akses gratis

Intelligence Index65 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

N/A

Google

Gemma 4 E2B

Model Dasar Bobot Terbuka

Gemma 4 E2B dari Google adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 131K, kecepatan 260 token/detik, indeks kecerdasan 55/100, serta akses gratis

Intelligence Index55 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

N/A

Perbedaan Utama Sekilas

Both models cost the same under this workload — choose on capability, not price.
Gemma 4 E2B is faster at 260 tok/s vs 190 tok/s.

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

Feature	Gemma 4 E4B	Gemma 4 E2B
Intelligence Index Composite reasoning & knowledge score (0-100)	65	55
Provider	Google	Google
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	131.1K tokens~197 A4 pages of text
Max Output Tokens Per single request	16K	8K
Throughput Speed Tokens generated per second	190 tok/s	260 tok/s
SWE-bench Verified Real-world software engineering	N/A	N/A

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Tied

Identical monthly runtime cost

Speed

Gemma 4 E2B

+37% faster generation

Context

Tied

131.1K tokens window

Skor Benchmark Terverifikasi

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

Gemma 4 E4BGemma 4 E2B

MMLU-Pro (Multi-task Language Understanding)

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

74%vs67.2%

LMSYS Chatbot Arena Elo

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

1320ptsvs1260pts

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

190 tok/s vs260 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 4 E4B0.22s

Gemma 4 E2B0.18s

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

Gemma 4 E4B2.85s

Gemma 4 E2B2.10s

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 4 E4B Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Gemma 4 E2B 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 4 E4B · Strengths & Key Advantages

Jendela konteks 131K dengan kapasitas output maksimum hingga 16K token (MMLU-Pro 74% · Arena Elo 1320)
Kecepatan generasi tinggi 190 token/detik dengan latensi token pertama (TTFT) hanya 220 ms
Bobot terbuka (E4B (4B)) 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

Gemma 4 E2B · Strengths & Key Advantages

Jendela konteks 131K dengan kapasitas output maksimum hingga 8K token (MMLU-Pro 67.2% · Arena Elo 1260)
Kecepatan generasi tinggi 260 token/detik dengan latensi token pertama (TTFT) hanya 180 ms
Bobot terbuka (E2B (2B)) 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 4 E4B	Gemma 4 E2B
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	4K	2K
Reasoning Tokens / Task Thinking tokens burned per task	1K	1K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Gemma 4 E4B vs Gemma 4 E2B: Jawaban Cepat (FAQ)

Which is more intelligent, Gemma 4 E4B or Gemma 4 E2B?

Gemma 4 E4B is more intelligent. It scores 65 compared with 55 on the composite Intelligence Index.

Which model is faster?

Gemma 4 E2B is faster, generating 260 tokens per second compared with 190 tokens per second.

Which model has lower latency?

Gemma 4 E2B has lower latency, with a time to first token of 0.18s compared with 0.22s.

Kesimpulan

Baik Gemma 4 E4B (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Gemma 4 E2B (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Gemma 4 E4B unggul dalam penalaran dan pemrograman, Gemma 4 E2B memberikan kecepatan generasi token lebih tinggi, dan Gemma 4 E4B lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Gemma 4 E4B atau Gemma 4 E2B?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Gemma 4 E4B memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Gemma 4 E4B vs Gemma 4 E2B?+

Gemma 4 E4B mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan Gemma 4 E2B sebesar $0.00 / $0.00. Secara keseluruhan, Gemma 4 E4B lebih hemat biaya.

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

Gemma 4 E4B mendukung konteks 131.1K token dibandingkan 131.1K token pada Gemma 4 E2B, sementara Gemma 4 E2B 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)

Feature	Gemma 4 E4B	Gemma 4 E2B
Official API Model ID (SDK)	models/gemma-4-e4b	models/gemma-4-e2b
API Protocol	Google Gemini API (generateContent) + OpenAI-Compatible SDK	Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date	2026-04	2026-04
Knowledge Cutoff	January 2026	January 2026
Architecture	Edge-optimized dense open transformer	Ultra-compact edge transformer
Parameters	E4B (4B)	E2B (2B)
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)	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	Built-in Google Search Grounding + Managed Python Code Execution Sandbox	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)	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) · 9 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 2 GB VRAM (4-bit Quantized) · 4 GB VRAM (Native BF16)
Safety & Frontier Classification	Google DeepMind Frontier Safety Framework Verified	Google DeepMind Frontier Safety Framework Verified
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 4 E4B vs Gemma 4 E2B

Ganti & Bandingkan Model Apa Saja

Gemma 4 E4B

Gemma 4 E2B

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

Gemma 4 E4B · Strengths & Key Advantages

Considerations & Trade-offs

Gemma 4 E2B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

Gemma 4 E4B vs Gemma 4 E2B: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

Related reading