ERNIE Lite vs Gemma 2
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
ERNIE Lite by Baidu and Gemma 2 by Google represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, ERNIE Lite provides an expanded memory horizon with 128K tokens. In latency-sensitive production environments, ERNIE Lite leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
원하는 AI 모델 선택 및 실시간 비교
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
ERNIE Lite
오픈 웨이트 모델Baidu의 ERNIE Lite 모델은 128K 토큰 컨텍스트 윈도우와 입력 $0/1M 토큰, 출력 $0/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.00 / 1M
$0.00 / 1M
128K tokens
N/A
Gemma 2
오픈 웨이트 모델Google의 Gemma 2 모델은 8K 토큰 컨텍스트 윈도우와 입력 $0/1M 토큰, 출력 $0/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.00 / 1M
$0.00 / 1M
8.2K tokens
N/A
한눈에 보는 핵심 차이점
- 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.
| Feature | ERNIE Lite | Gemma 2 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 65 | 58 |
Provider | Baidu | |
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 |
부문별 우위 모델
At-a-glance decision frameworkEvaluations ongoing
검증된 벤치마크 점수 비교
Official evaluation metrics verified by independent third-party research labs.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Tokens emitted per second during active generation streaming.
지연 시간(Latency) 및 응답 속도
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
실시간 API 토큰 비용 시뮬레이터
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
ERNIE Lite · Strengths & Key Advantages
- Baidu의 첨단 Ultra-Lightweight Edge-Ready Transformer 아키텍처와 최대 128K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0/1M, 출력 $0/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Gemma 2 · Strengths & Key Advantages
- Google의 첨단 Dense transformer with sliding window attention and soft-capping 아키텍처와 최대 8K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0/1M, 출력 $0/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
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 |
기술 사양 (Specifications)
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: 핵심 요약 문답 (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.
종합 평가
Verdict & Model Selection Recommendation: Choose ERNIE Lite if your priority is High-frequency keyword tagging, basic intent routing, and embedded device NLP. Its performance in demanding tasks justifies its operational token cost. Choose Gemma 2 if your workflow prioritizes Local developer assistants, private enterprise fine-tuning, and offline inference. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
자주 묻는 질문 (FAQ)
How does ERNIE Lite compare to Gemma 2 in API token pricing and cost per token?+
Both ERNIE Lite and Gemma 2 are open-weights foundation models available at $0.00 direct API cost when self-hosted, or offered at identical baseline rates across major inference providers. Model selection depends on GPU deployment sizing and specialized benchmark performance rather than raw token price.
Which model has a larger context window: ERNIE Lite or Gemma 2?+
ERNIE Lite supports a context window of 128K tokens (~96k words), while Gemma 2 supports 8K tokens (~6k words). Therefore, ERNIE Lite can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: ERNIE Lite or Gemma 2?+
ERNIE Lite scores at the frontier of coding benchmarks, while Gemma 2 scores competitively for software generation. For complex autonomous debugging and agentic engineering, ERNIE Lite provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between ERNIE Lite and Gemma 2?+
ERNIE Lite is optimized for low-latency direct generation. In contrast, Gemma 2 focuses on standard fast inference.
How does inference speed (tokens per second) compare between ERNIE Lite and Gemma 2?+
ERNIE Lite generates approximately 190 tokens per second, while Gemma 2 delivers 110 tokens per second. In latency-sensitive production environments, ERNIE Lite provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining ERNIE Lite and Gemma 2 to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to ERNIE Lite, and route deep architectural or complex reasoning queries to ERNIE Lite. This balanced routing strategy maximizes system throughput and answer quality.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
공식 출처: GetInfoUs Independent AI Benchmarks & Official API Specs