Head-to-Head Model Comparison

Gemma 4 E2B vs Gemma 4 31B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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Gemma 4 E2BProfile

Gemma 4 31BProfile

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Popular Matchups:

Google

Gemma 4 E2B

Open-Weights-Basismodell

Gemma 4 E2B von Google ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 131K Kontextfenster, 260 Token/s Durchsatz, einem Intelligenz-Index von 55/100 und kostenlosem Open-Weights-Zuga

Intelligence Index55 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

N/A

Google

Gemma 4 31B

Open-Weights-Reasoning-Modell

Gemma 4 31B von Google ist ein KI-Modell für Open-Weights-Reasoning mit transparenter Gedankenketten-Verarbeitung (Chain-of-Thought) mit 262K Kontextfenster, 80 Token/s Durchsatz, einem Intelligenz-Index von 81/100 und kostenlosem Open-Weig

Intelligence Index81 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

68.5%

Wichtigste Unterschiede auf einen Blick

Both models cost the same under this workload — choose on capability, not price.
Gemma 4 31B has a larger context window (262K vs 131K tokens).
Gemma 4 E2B is faster at 260 tok/s vs 80 tok/s.

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

Feature	Gemma 4 E2B	Gemma 4 31B
Intelligence Index Composite reasoning & knowledge score (0-100)	55	81
Provider	Google	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	131.1K tokens~197 A4 pages of text	262.1K tokens~393 A4 pages of text
Max Output Tokens Per single request	8K	33K
Throughput Speed Tokens generated per second	260 tok/s	80 tok/s
SWE-bench Verified Real-world software engineering	N/A	68.5%
GPQA Diamond Graduate-level reasoning	N/A	77%

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Tied

Identical monthly runtime cost

Speed

Gemma 4 E2B

+225% faster generation

Context

Gemma 4 31B

262.1K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Gemma 4 E2BGemma 4 31B

SWE-bench Verified (Autonomous Coding)

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

N/Avs68.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

N/Avs77%

MMLU-Pro (Multi-task Language Understanding)

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

67.2%vs84.8%

LMSYS Chatbot Arena Elo

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

1260ptsvs1410pts

AIME (Advanced Math Competition)

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

N/Avs86%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

260 tok/s vs80 tok/s

Latenz & Antwortzeit

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

Gemma 4 E2B0.18s

Gemma 4 31B0.48s

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

Gemma 4 E2B2.10s

Gemma 4 31B6.73s

Interaktiver Token-Kosten-Simulator

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)

Gemma 4 E2B Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Gemma 4 31B 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

Gemma 4 E2B · Strengths & Key Advantages

131K Kontextfenster und bis zu 8K maximale Output-Token (MMLU-Pro 67.2% · Arena Elo 1260)
Hohe Generierungsgeschwindigkeit von 260 Token/Sekunde bei nur 180 ms Erstlatenz (TTFT)
Offene Modellgewichte (E2B (2B)) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama

Considerations & Trade-offs

Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Gemma 4 31B · Strengths & Key Advantages

262K Kontextfenster und bis zu 33K maximale Output-Token (SWE-bench Verified 68.5% · GPQA Diamond 77%)
Hohe Generierungsgeschwindigkeit von 80 Token/Sekunde bei nur 480 ms Erstlatenz (TTFT)
Offene Modellgewichte (31B) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama

Considerations & Trade-offs

Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Token Use & Task Economics

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

Metric	Gemma 4 E2B	Gemma 4 31B
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	2K	12K
Reasoning Tokens / Task Thinking tokens burned per task	1K	6K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Gemma 4 E2B vs Gemma 4 31B: Schnelle Antworten (FAQ)

Which is more intelligent, Gemma 4 E2B or Gemma 4 31B?

Gemma 4 31B is more intelligent. It scores 81 compared with 55 on the composite Intelligence Index.

Which model is faster?

Gemma 4 E2B is faster, generating 260 tokens per second compared with 80 tokens per second.

Which model has lower latency?

Gemma 4 E2B has lower latency, with a time to first token of 0.18s compared with 0.48s.

Which model has a larger context window?

Gemma 4 31B has a larger context window, supporting 262.1K tokens compared with 131.1K tokens.

Fazit

Sowohl Gemma 4 E2B (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Gemma 4 31B (Optimal für Open-Weights-Reasoning mit transparenter Gedankenketten-Verarbeitung (Chain-of-Thought), Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Gemma 4 31B führt bei komplexem Reasoning und Coding, Gemma 4 E2B liefert den höheren Token-Durchsatz und Gemma 4 E2B ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Gemma 4 E2B oder Gemma 4 31B?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Gemma 4 31B vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Gemma 4 E2B und Gemma 4 31B?+

Gemma 4 E2B kostet $0.00 / $0.00 pro 1M Input-/Output-Tokens, Gemma 4 31B liegt bei $0.00 / $0.00. Beim gewichteten Gesamtpreis ist Gemma 4 E2B günstiger.

Welches Modell hat das größere Kontextfenster und die höhere Geschwindigkeit?+

Gemma 4 E2B bietet 131.1K Tokens Kontext gegenüber 262.1K Tokens bei Gemma 4 31B. Bei der Generierungsgeschwindigkeit liegt Gemma 4 E2B vorn.

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)

Feature	Gemma 4 E2B	Gemma 4 31B
Official API Model ID (SDK)	models/gemma-4-e2b	models/gemma-4-31b
API Protocol	Google Gemini API (generateContent) + OpenAI-Compatible SDK	Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date	2026-04	2026-04
Knowledge Cutoff	January 2026	January 2026
Architecture	Ultra-compact edge transformer	Dense open reasoning transformer with native thinking
Parameters	E2B (2B)	31B
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	Supported (Screen coordinate grounding, browser & desktop GUI action loops)	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	Built-in Google Search Grounding + Managed Python Code Execution Sandbox	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) · Vision (~258–765 tokens per image tile)	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) · 2 GB VRAM (4-bit Quantized) · 4 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 19 GB VRAM (4-bit Quantized) · 67 GB VRAM (Native BF16)
Safety & Frontier Classification	Google DeepMind Frontier Safety Framework Verified	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, vision	text, vision
Output Modalities	text	text

Gemma 4 E2B vs Gemma 4 31B

Beliebige Modelle wechseln & vergleichen

Gemma 4 E2B

Gemma 4 31B

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

Gemma 4 E2B · Strengths & Key Advantages

Considerations & Trade-offs

Gemma 4 31B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

Gemma 4 E2B vs Gemma 4 31B: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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