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Head-to-Head Model Comparison

ERNIE Speed vs Gemini 4 Argon

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs 연구팀 검토 완료최종 검토일: October 2026편집 정책
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BaiduGooglelightweightflagship

Compare ERNIE Speed by Baidu against Gemini 4 Argon by Google across real-world coding, reasoning, token economics, and latency.

1:1 모델 맞대결 비교 (VS)

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Live comparison: no in-depth editorial review has been published for this pair yet.

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Baidu

ERNIE Speed

초고속 저지연 추론 모델

Baidu의 ERNIE Speed 모델은 초저지연 실시간 대화, 고속 라우팅 및 대규모 트래픽 처리에 최적화된 AI 모델로, 128K 컨텍스트 윈도우, 초당 175 토큰의 생성 속도, 종합 지능 지수 68/100점(무료 오픈 웨이트 제공)을 제공합니다.

Intelligence Index68 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

N/A

Google

Gemini 4 Argon

네이티브 멀티모달 AI

Google의 Gemini 4 Argon 모델은 텍스트와 이미지의 네이티브 멀티모달 이해 및 복합 문서·차트 분석에 최적화된 AI 모델로, 1M 컨텍스트 윈도우, 초당 115 토큰의 생성 속도, 종합 지능 지수 97/100점(1M 토큰당 입력 $2 / 출력 $10)을 제공합니다.

Intelligence Index97 / 100
Input Token:

$2.00 / 1M

Output Token:

$10.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

81.9%

한눈에 보는 핵심 차이점

  • ERNIE Speed is 1.0x cheaper for API usage (input + output tokens combined).
  • Gemini 4 Argon has a larger context window (1M vs 128K tokens).
  • ERNIE Speed is faster at 175 tok/s vs 115 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureERNIE SpeedGemini 4 Argon
Intelligence Index
Composite reasoning & knowledge score (0-100)
6897
Provider
BaiduGoogle
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
128K tokens~192 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
4K1000K
Throughput Speed
Tokens generated per second
175 tok/s115 tok/s
SWE-bench Verified
Real-world software engineering
N/A81.9%
GPQA Diamond
Graduate-level reasoning
N/A81.5%
Cached Input Price
Prompt caching discount per 1M tokens
$0.00 / 1M$0.10 / 1M

부문별 우위 모델

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
ERNIE Speed
1.0x cheaper monthly runtime
Speed
ERNIE Speed
+52% faster generation
Context
Gemini 4 Argon
1M tokens window

검증된 벤치마크 점수 비교

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

ERNIE SpeedGemini 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.

64%vs90.8%
LMSYS Chatbot Arena Elo

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

1205ptsvs1525pts
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.

N/Avs96.4%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

175 tok/s vs115 tok/s

지연 시간(Latency) 및 응답 속도

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 Speed0.23s
Gemini 4 Argon0.85s
Est. End-to-End Response (500 tokens)TTFT + generation time
ERNIE Speed3.09s
Gemini 4 Argon5.20s

실시간 API 토큰 비용 시뮬레이터

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
ERNIE Speed 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
ERNIE Speed saves approximately $12.15 / month (100%) under this workload.1.0x Savings

ERNIE Speed · Strengths & Key Advantages

  • 128K 컨텍스트 윈도우 및 최대 4K 출력 토큰 생성 지원 (MMLU-Pro 64% · Arena Elo 1205)
  • 초당 175 토큰의 빠른 처리 속도와 230ms에 불과한 첫 토큰 지연 시간(TTFT)
  • 오픈 웨이트 아키텍처 (Confidential / 8B Scale)를 통해 vLLM, SGLang, Ollama 기반 자체 GPU 서버 구축 및 파인튜닝 가능
Considerations & Trade-offs
  • 비양자화 전체 정밀도로 로컬 서버에서 구동하려면 충분한 GPU VRAM 용량이 필요합니다
  • 초고난도 학술 연구 및 수학 증명 과제에서는 상위 대형 추론 특화 모델이 더 깊은 분석을 제공할 수 있습니다

Gemini 4 Argon · Strengths & Key Advantages

  • 1M 컨텍스트 윈도우 및 최대 1M 출력 토큰 생성 지원 (SWE-bench Verified 81.9% · GPQA Diamond 81.5%)
  • 초당 115 토큰의 빠른 처리 속도와 850ms에 불과한 첫 토큰 지연 시간(TTFT)
  • 합리적인 API 요금 체계(1M 토큰당 입력 $2 / 출력 $10)와 프롬프트 캐싱, 함수 호출 및 정형화된 JSON 출력 완벽 지원
Considerations & Trade-offs
  • 독점 클라우드 전용 모델로 로컬 오프라인 서버에 가중치를 다운로드하여 실행할 수 없습니다
  • 대규모 프로덕션 호출 환경에서는 비용 절감을 위해 프롬프트 캐싱과 배치 API(Batch API) 활용이 권장됩니다

Token Use & Task Economics

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

MetricERNIE SpeedGemini 4 Argon
Cost per Task
Estimated USD to finish one task
$0.00$2.45
Output Tokens / Task
Tokens generated per task
8K75K
Reasoning Tokens / Task
Thinking tokens burned per task
0K36K

기술 사양 (Specifications)

Architecture, release timeline, capabilities, and modalities.

FeatureERNIE SpeedGemini 4 Argon
Official API Model ID (SDK)ernie-speedmodels/gemini-4-argon
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIGoogle Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date2024-032026-09
Knowledge CutoffDecember 2023June 2026
ArchitectureDistilled High-Throughput TransformerPathways Multimodal Sparse Mixture-of-Experts
ParametersConfidential / 8B ScaleConfidential
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud10,000 – 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 & TTLAutomatic prefix KV-caching in vLLM / SGLang (0 cost)Implicit + Explicit Context Caching at $0.1/1M with configurable TTL
Computer Use / GUI AgentAPI / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsBuilt-in Google Search Grounding + Managed Python Code Execution Sandbox
Official Fine-Tuning SupportFull Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)Vertex AI Supervised Adapter Tuning & Context Distillation
Multimodal Token BillingText BPE (~4 chars/token)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 VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 5 GB VRAM (4-bit Quantized) · 17 GB VRAM (Native BF16)Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier ClassificationOpen-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 ControlSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestexttext, vision, audio, video
Output Modalitiestexttext

ERNIE Speed vs Gemini 4 Argon: 핵심 요약 문답 (FAQ)

Which is more intelligent, ERNIE Speed or Gemini 4 Argon?

Gemini 4 Argon is more intelligent. It scores 97 compared with 68 on the composite Intelligence Index.

Which model is faster?

ERNIE Speed is faster, generating 175 tokens per second compared with 115 tokens per second.

Which model is cheaper to run?

ERNIE Speed 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?

ERNIE Speed has lower latency, with a time to first token of 0.23s compared with 0.85s.

Which model has a larger context window?

Gemini 4 Argon has a larger context window, supporting 1M tokens compared with 128K tokens.

종합 평가

Choose ERNIE Speed for 초저지연 실시간 대화, 고속 라우팅 및 대규모 트래픽 처리, 엔터프라이즈 rag 지식베이스 및 대규모 프로덕션 api 연동에 최적., or choose Gemini 4 Argon for 텍스트와 이미지의 네이티브 멀티모달 이해 및 복합 문서·차트 분석, 엔터프라이즈 rag 지식베이스 및 대규모 프로덕션 api 연동에 최적..

자주 묻는 질문 (FAQ)

Which is cheaper: ERNIE Speed or Gemini 4 Argon?+

ERNIE Speed 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: ERNIE Speed or Gemini 4 Argon?+

ERNIE Speed supports 128K 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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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: Baidu & Google Official Documentation