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Head-to-Head Model Comparison

Gemma 4 E2B vs Llama 5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

تمت المراجعة بواسطة فريق أبحاث GetInfoUsآخر مراجعة: October 2026السياسة التحريرية
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GoogleMetaopen-sourceopen-source

Compare Gemma 4 E2B by Google against Llama 5 by Meta across real-world coding, reasoning, token economics, and latency.

مقارنة مباشرة (VS)

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

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Google

Gemma 4 E2B

نموذج مفتوح الأوزان

نموذج Gemma 4 E2B المقدم من Google يوفر نافذة سياق تصل إلى 131K رمز، بتكلفة $0 لكل مليون رمز إدخال و$0 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.

Intelligence Index55 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

N/A

Meta

Llama 5

نموذج مفتوح الأوزان

نموذج Llama 5 المقدم من Meta يوفر نافذة سياق تصل إلى 5M رمز، بتكلفة $0 لكل مليون رمز إدخال و$0 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.

Intelligence Index94 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

5M tokens

SWE-bench Verified:

74%

أهم الفروقات في لمحة

  • Both models cost the same under this workload — choose on capability, not price.
  • Llama 5 has a larger context window (5M vs 131K tokens).
  • Gemma 4 E2B is faster at 260 tok/s vs 65 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGemma 4 E2BLlama 5
Intelligence Index
Composite reasoning & knowledge score (0-100)
5594
Provider
GoogleMeta
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 text5M tokens~7,500 A4 pages of text
Max Output Tokens
Per single request
8K128K
Throughput Speed
Tokens generated per second
260 tok/s65 tok/s
SWE-bench Verified
Real-world software engineering
N/A74%
GPQA Diamond
Graduate-level reasoning
N/A84%

من يتفوق في كل مجال؟

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Tied
Identical monthly runtime cost
Speed
Gemma 4 E2B
+300% faster generation
Context
Llama 5
5M tokens window

نتائج الاختبارات المعيارية الموثقة

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

Gemma 4 E2BLlama 5
SWE-bench Verified (Autonomous Coding)

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

N/Avs74%
GPQA Diamond (Doctorate-Level Science & Math)

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

N/Avs84%
MMLU-Pro (Multi-task Language Understanding)

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

67.2%vs87%
LMSYS Chatbot Arena Elo

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

1260ptsvs1485pts
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs68%
AIME (Advanced Math Competition)

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

N/Avs88%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

260 tok/s vs65 tok/s

زمن الاستجابة والسرعة

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
Llama 50.85s
Est. End-to-End Response (500 tokens)TTFT + generation time
Gemma 4 E2B2.10s
Llama 58.54s

محاكي تكلفة الرموز التفاعلي

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)
Gemma 4 E2B Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
Llama 5 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

  • بنية Ultra-compact edge transformer متطورة من Google مع سعة سياق تبلغ 131K رمز
  • كفاءة عالية في التكلفة ($0/1M إدخال و$0/1M إخراج) وسرعة استجابة ممتازة
  • أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
  • يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
  • قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة

Llama 5 · Strengths & Key Advantages

  • بنية Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) متطورة من Meta مع سعة سياق تبلغ 5M رمز
  • كفاءة عالية في التكلفة ($0/1M إدخال و$0/1M إخراج) وسرعة استجابة ممتازة
  • أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
  • يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
  • قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة

Token Use & Task Economics

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

MetricGemma 4 E2BLlama 5
Cost per Task
Estimated USD to finish one task
$0.00$0.00
Output Tokens / Task
Tokens generated per task
2K40K
Reasoning Tokens / Task
Thinking tokens burned per task
1K20K

المواصفات التقنية

Architecture, release timeline, capabilities, and modalities.

FeatureGemma 4 E2BLlama 5
Official API Model ID (SDK)models/gemma-4-e2bmeta-llama/Llama-5
API ProtocolGoogle Gemini API (generateContent) + OpenAI-Compatible SDKOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2026-042026-04
Knowledge CutoffJanuary 2026January 2026
ArchitectureUltra-compact edge transformerMixture-of-Experts with recursive self-improvement (~600B total, 40-80B active)
ParametersE2B (2B)600B+ total (~40-80B active)
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (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 & TTLAutomatic prefix KV-caching in vLLM / SGLang (0 cost)Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)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 & SandboxBuilt-in Google Search Grounding + Managed Python Code Execution SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull 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 BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)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) · 2 GB VRAM (4-bit Quantized) · 4 GB VRAM (Native BF16)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 372 GB VRAM (4-bit Quantized) · 1290 GB VRAM (Native BF16)
Safety & Frontier ClassificationGoogle DeepMind Frontier Safety Framework VerifiedOpen-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestext, visiontext, vision, video, audio
Output Modalitiestexttext

Gemma 4 E2B vs Llama 5: إجابات سريعة (FAQ)

Which is more intelligent, Gemma 4 E2B or Llama 5?

Llama 5 is more intelligent. It scores 94 compared with 55 on the composite Intelligence Index.

Which model is faster?

Gemma 4 E2B is faster, generating 260 tokens per second compared with 65 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 0.85s.

Which model has a larger context window?

Llama 5 has a larger context window, supporting 5M tokens compared with 131.1K tokens.

الحكم النهائي

Choose Gemma 4 E2B for أتمتة الأعمال، تطوير البرمجيات، الوكلاء الأذكياء (ai agents)، وتطبيقات google الإنتاجية, or choose Llama 5 for أتمتة الأعمال، تطوير البرمجيات، الوكلاء الأذكياء (ai agents)، وتطبيقات meta الإنتاجية.

الأسئلة الشائعة (FAQ)

Which is cheaper: Gemma 4 E2B or Llama 5?+

Gemma 4 E2B costs $0/1M input and $0/1M output tokens, while Llama 5 costs $0/1M input and $0/1M output tokens.

Which model has a larger context window: Gemma 4 E2B or Llama 5?+

Gemma 4 E2B supports 131K tokens whereas Llama 5 supports 5M 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.

المصادر الرسمية: Google & Meta Official Documentation