Head-to-Head Model Comparison

ERNIE Lite vs Gemma 2

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.

ERNIE LiteProfile

Gemma 2Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Baidu

ERNIE Lite

Model Ringan & Hemat Biaya

ERNIE Lite dari Baidu adalah model AI yang dirancang untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, menghadirkan jendela konteks 128K, kecepatan 190 token/detik, indeks kecerdasan 65/100, serta akses grati

Intelligence Index65 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

N/A

Google

Gemma 2

Model Dasar Bobot Terbuka

Gemma 2 dari Google adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 8K, kecepatan 110 token/detik, indeks kecerdasan 58/100, serta akses gratis open-w

Intelligence Index58 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

8.2K tokens

SWE-bench Verified:

N/A

Perbedaan Utama Sekilas

Both models cost the same under this workload — choose on capability, not price.
ERNIE Lite has a larger context window (128K vs 8K tokens).
ERNIE Lite is faster at 190 tok/s vs 110 tok/s.

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

Feature	ERNIE Lite	Gemma 2
Intelligence Index Composite reasoning & knowledge score (0-100)	65	58
Provider	Baidu	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	128K tokens~192 A4 pages of text	8.2K tokens~12 A4 pages of text
Max Output Tokens Per single request	4K	4K
Throughput Speed Tokens generated per second	190 tok/s	110 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

ERNIE Lite

+73% faster generation

Context

ERNIE Lite

128K tokens window

Skor Benchmark Terverifikasi

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

ERNIE LiteGemma 2

MMLU-Pro (Multi-task Language Understanding)

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

61%vs68.5%

LMSYS Chatbot Arena Elo

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

1180ptsvs1250pts

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

190 tok/s vs110 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

ERNIE Lite0.20s

Gemma 20.30s

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

ERNIE Lite2.83s

Gemma 24.85s

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)

ERNIE Lite Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Gemma 2 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

ERNIE Lite · Strengths & Key Advantages

Jendela konteks 128K dengan kapasitas output maksimum hingga 4K token (MMLU-Pro 61% · Arena Elo 1180)
Kecepatan generasi tinggi 190 token/detik dengan latensi token pertama (TTFT) hanya 200 ms
Bobot terbuka (Confidential / 3B Scale) 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 2 · Strengths & Key Advantages

Jendela konteks 8K dengan kapasitas output maksimum hingga 4K token (MMLU-Pro 68.5% · Arena Elo 1250)
Kecepatan generasi tinggi 110 token/detik dengan latensi token pertama (TTFT) hanya 300 ms
Bobot terbuka (2B / 9B / 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

Token Use & Task Economics

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

Metric	ERNIE Lite	Gemma 2
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	6K	3K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Feature	ERNIE Lite	Gemma 2
Official API Model ID (SDK)	ernie-lite	models/gemma-2
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date	2024-03	2024-06
Knowledge Cutoff	December 2023	May 2024
Architecture	Ultra-Lightweight Edge-Ready Transformer	Dense transformer with sliding window attention and soft-capping
Parameters	Confidential / 3B Scale	2B / 9B / 27B
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	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)	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	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)	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token Billing	Text BPE (~4 chars/token)	Text BPE (~4 chars/token)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 2 GB VRAM (4-bit Quantized) · 6 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	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	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalities	text	text
Output Modalities	text	text

ERNIE Lite vs Gemma 2: Jawaban Cepat (FAQ)

Which is more intelligent, ERNIE Lite or Gemma 2?

ERNIE Lite is more intelligent. It scores 65 compared with 58 on the composite Intelligence Index.

Which model is faster?

ERNIE Lite is faster, generating 190 tokens per second compared with 110 tokens per second.

Which model has lower latency?

ERNIE Lite has lower latency, with a time to first token of 0.20s compared with 0.30s.

Which model has a larger context window?

ERNIE Lite has a larger context window, supporting 128K tokens compared with 8.2K tokens.

Kesimpulan

Baik ERNIE Lite (Sangat cocok untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Gemma 2 (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: ERNIE Lite unggul dalam penalaran dan pemrograman, ERNIE Lite memberikan kecepatan generasi token lebih tinggi, dan ERNIE Lite lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, ERNIE Lite atau Gemma 2?+

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

Berapa perbandingan harga API ERNIE Lite vs Gemma 2?+

ERNIE Lite mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan Gemma 2 sebesar $0.00 / $0.00. Secara keseluruhan, ERNIE Lite lebih hemat biaya.

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

ERNIE Lite mendukung konteks 128K token dibandingkan 8.2K token pada Gemma 2, sementara ERNIE Lite 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)

ERNIE Lite vs Gemma 2

Ganti & Bandingkan Model Apa Saja

ERNIE Lite

Gemma 2

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

ERNIE Lite · Strengths & Key Advantages

Considerations & Trade-offs

Gemma 2 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

ERNIE Lite vs Gemma 2: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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