Head-to-Head Model Comparison

DeepSeek V4 vs Gemma 4 E2B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale

Cambia e Confronta Qualsiasi Modello

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

DeepSeek V4Profile

Gemma 4 E2BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

DeepSeek

DeepSeek V4

Modello Base a Pesi Aperti

DeepSeek V4 di DeepSeek è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 1M, velocità di 78 token/s, indice di intelligenza di 56/100 e ta

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

Modello Base a Pesi Aperti

Gemma 4 E2B di Google è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 131K, velocità di 260 token/s, indice di intelligenza di 55/100 e t

Intelligence Index55 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

N/A

Differenze Chiave a Colpo d’Occhio

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 →

Feature	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

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Gemma 4 E2B

1.0x cheaper monthly runtime

Speed

Gemma 4 E2B

+233% faster generation

Context

DeepSeek V4

1M tokens window

Punteggi Benchmark Verificati

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

Latenza e Tempo di Risposta

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

Simulatore Interattivo dei Costi Token

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

Finestra di contesto da 1M con capacità massima di output fino a 66K token (SWE-bench Verified 62.8% · GPQA Diamond 78.4%)
Throughput elevato di 78 token/secondo con latenza al primo token (TTFT) di soli 600 ms
Pesi aperti (MoE (Colossal)) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama

Considerations & Trade-offs

Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

Gemma 4 E2B · Strengths & Key Advantages

Finestra di contesto da 131K con capacità massima di output fino a 8K token (MMLU-Pro 67.2% · Arena Elo 1260)
Throughput elevato di 260 token/secondo con latenza al primo token (TTFT) di soli 180 ms
Pesi aperti (E2B (2B)) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama

Considerations & Trade-offs

Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

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

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	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: Risposte Rapide (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.

Verdetto

Sia DeepSeek V4 (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) sia Gemma 4 E2B (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: DeepSeek V4 guida nei benchmark di ragionamento e programmazione, Gemma 4 E2B garantisce una generazione più rapida e Gemma 4 E2B offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, DeepSeek V4 o Gemma 4 E2B?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale DeepSeek V4. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra DeepSeek V4 e Gemma 4 E2B?+

DeepSeek V4 costa $0.30 / $1.20 per milione di token di input/output, mentre Gemma 4 E2B costa $0.00 / $0.00. Nel costo combinato, Gemma 4 E2B risulta più economico.

Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+

DeepSeek V4 supporta 1M token di contesto contro 131.1K token di Gemma 4 E2B, mentre Gemma 4 E2B offre una velocità di generazione superiore.

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 e Confronta Qualsiasi Modello

DeepSeek V4

Gemma 4 E2B

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

DeepSeek V4 · Strengths & Key Advantages

Considerations & Trade-offs

Gemma 4 E2B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

DeepSeek V4 vs Gemma 4 E2B: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

Related reading