Head-to-Head Model Comparison

DeepSeek V4 vs Gemma 4 E2B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado por el equipo de investigación de GetInfoUsÚltima revisión: October 2026Política editorial

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DeepSeek V4Profile

Gemma 4 E2BProfile

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Popular Matchups:

DeepSeek

DeepSeek V4

Modelo Base de Pesos Abiertos

DeepSeek V4 de DeepSeek es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 1M, velocidad de 78 tokens/s, índice de inteligencia de 56/100 y tari

Intelligence Index56 / 100

Input Token:

$0.30 / 1M

Output Token:

$1.20 / 1M

Context Window:

1M tokens

SWE-bench Verified:

62.8%

Google

Gemma 4 E2B

Modelo Base de Pesos Abiertos

Gemma 4 E2B de Google es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 131K, velocidad de 260 tokens/s, índice de inteligencia de 55/100 y tar

Intelligence Index55 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

N/A

Diferencias Clave de un Vistazo

Gemma 4 E2B is 1.0x cheaper for API usage (input + output tokens combined).
DeepSeek V4 has a larger context window (1M vs 131K tokens).
Gemma 4 E2B is faster at 260 tok/s vs 78 tok/s.

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

Característica	DeepSeek V4	Gemma 4 E2B
Intelligence Index Composite reasoning & knowledge score (0-100)	56	55
Provider	DeepSeek	Google
Input Price Cost per million input tokens	$0.30 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$1.20 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.20 / 1M	$0.00 / 1M
Context Window Maximum input + output tokens	1M tokens~1,500 A4 pages of text	131.1K tokens~197 A4 pages of text
Max Output Tokens Per single request	66K	8K
Throughput Speed Tokens generated per second	78 tok/s	260 tok/s
SWE-bench Verified Real-world software engineering	62.8%	N/A
GPQA Diamond Graduate-level reasoning	78.4%	N/A
Cached Input Price Prompt caching discount per 1M tokens	$0.03 / 1M	$0.00 / 1M

¿Quién gana en cada área?

At-a-glance decision framework

Código

Evaluations ongoing

Costo

Gemma 4 E2B

1.0x cheaper monthly runtime

Velocidad

Gemma 4 E2B

+233% faster generation

Contexto

DeepSeek V4

1M tokens window

Puntuaciones de Benchmarks Verificadas

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

DeepSeek V4Gemma 4 E2B

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

62.8%vsN/A

GPQA Diamond (Doctorate-Level Science & Math)

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

78.4%vsN/A

MMLU-Pro (Multi-task Language Understanding)

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

87.5%vs67.2%

LMSYS Chatbot Arena Elo

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

1415ptsvs1260pts

Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

14.5%vsN/A

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

42%vsN/A

SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

39.5%vsN/A

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

65%vsN/A

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

89%vsN/A

Long-Context Reasoning

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

88%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

78 tok/s vs260 tok/s

Latencia y Tiempo de Respuesta

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

DeepSeek V40.60s

Gemma 4 E2B0.18s

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

DeepSeek V47.01s

Gemma 4 E2B2.10s

Simulador Interactivo de Costos de Tokens

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)

DeepSeek V4 Monthly API Bill

$1.57 / month

$0.3/1M in · $1.2/1M out

Gemma 4 E2B Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Gemma 4 E2B saves approximately $1.57 / month (100%) under this workload.1.0x Savings

DeepSeek V4 · Strengths & Key Advantages

Ventana de contexto de 1M con hasta 66K tokens de salida máxima (SWE-bench Verified 62.8% · GPQA Diamond 78.4%)
Rendimiento ágil de 78 tokens/segundo con latencia inicial (TTFT) de solo 600 ms
Pesos abiertos (MoE (Colossal)) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama

Considerations & Trade-offs

Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad

Gemma 4 E2B · Strengths & Key Advantages

Ventana de contexto de 131K con hasta 8K tokens de salida máxima (MMLU-Pro 67.2% · Arena Elo 1260)
Rendimiento ágil de 260 tokens/segundo con latencia inicial (TTFT) de solo 180 ms
Pesos abiertos (E2B (2B)) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama

Considerations & Trade-offs

Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad

Token Use & Task Economics

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

Metric	DeepSeek V4	Gemma 4 E2B
Cost per Task Estimated USD to finish one task	$0.38	$0.00
Output Tokens / Task Tokens generated per task	48K	2K
Reasoning Tokens / Task Thinking tokens burned per task	22K	1K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	DeepSeek V4	Gemma 4 E2B
Official API Model ID (SDK)	deepseek-reasoner	models/gemma-4-e2b
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date	2026-07	2026-04
Knowledge Cutoff	April 2026	January 2026
Architecture	Next-Gen Sparse MoE with MLA	Ultra-compact edge transformer
Parameters	MoE (Colossal)	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)	Flat rate ($0.3/1M in) across full 1000K window	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Automatic prefix caching at $0.03/1M (no extra cache-write fee)	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)	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	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) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 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, vision
Output Modalities	text	text

DeepSeek V4 vs Gemma 4 E2B: Respuestas Rápidas (FAQ)

Which is more intelligent, DeepSeek V4 or Gemma 4 E2B?

DeepSeek V4 is more intelligent. It scores 56 compared with 55 on the composite Intelligence Index.

Which model is faster?

Gemma 4 E2B is faster, generating 260 tokens per second compared with 78 tokens per second.

Which model is cheaper to run?

Gemma 4 E2B is cheaper at $0.00 per 1M tokens blended, compared with $0.20 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemma 4 E2B has lower latency, with a time to first token of 0.18s compared with 0.60s.

Which model has a larger context window?

DeepSeek V4 has a larger context window, supporting 1M tokens compared with 131.1K tokens.

Veredicto

Tanto DeepSeek V4 (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) como Gemma 4 E2B (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) destacan en producción: DeepSeek V4 lidera en inteligencia general y programación, Gemma 4 E2B ofrece mayor velocidad de generación de tokens y Gemma 4 E2B resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, DeepSeek V4 o Gemma 4 E2B?+

En las pruebas de inteligencia compuesta y resolución de código, DeepSeek V4 obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de DeepSeek V4 frente a Gemma 4 E2B?+

DeepSeek V4 cuesta $0.30 / $1.20 por millón de tokens de entrada/salida, mientras que Gemma 4 E2B cuesta $0.00 / $0.00. En costo combinado, Gemma 4 E2B es la opción más económica.

¿Qué modelo tiene mayor ventana de contexto y menor latencia?+

DeepSeek V4 cuenta con 1M tokens de contexto frente a 131.1K tokens de Gemma 4 E2B. En velocidad de inferencia, Gemma 4 E2B ofrece un rendimiento superior.

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)

DeepSeek V4 vs Gemma 4 E2B

Cambia y Compara Cualquier Modelo

DeepSeek V4

Gemma 4 E2B

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

DeepSeek V4 · Strengths & Key Advantages

Considerations & Trade-offs

Gemma 4 E2B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

DeepSeek V4 vs Gemma 4 E2B: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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