Gemma 4 E4B vs Llama 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Gemma 4 E4B by Google against Llama 5 by Meta across real-world coding, reasoning, token economics, and latency.
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Gemma 4 E4B
Open-Weights-BasismodellGemma 4 E4B von Google ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 131K Kontextfenster, 190 Token/s Durchsatz, einem Intelligenz-Index von 65/100 und kostenlosem Open-Weights-Zuga
$0.00 / 1M
$0.00 / 1M
131.1K tokens
N/A
Llama 5
Open-Weights-BasismodellLlama 5 von Meta ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 5M Kontextfenster, 65 Token/s Durchsatz, einem Intelligenz-Index von 94/100 und kostenlosem Open-Weights-Zugang.
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
Wichtigste Unterschiede auf einen Blick
- 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 E4B is faster at 190 tok/s vs 65 tok/s.
| Feature | Gemma 4 E4B | Llama 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 65 | 94 |
Provider | Meta | |
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 | 5M tokens~7,500 A4 pages of text |
Max Output Tokens Per single request | 16K | 128K |
Throughput Speed Tokens generated per second | 190 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 74% |
GPQA Diamond Graduate-level reasoning | N/A | 84% |
Wer gewinnt in welcher Disziplin?
At-a-glance decision frameworkEvaluations ongoing
Geprüfte Benchmark-Ergebnisse im Vergleich
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Tokens emitted per second during active generation streaming.
Latenz & Antwortzeit
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Interaktiver Token-Kosten-Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemma 4 E4B · Strengths & Key Advantages
- 131K Kontextfenster und bis zu 16K maximale Output-Token (MMLU-Pro 74% · Arena Elo 1320)
- Hohe Generierungsgeschwindigkeit von 190 Token/Sekunde bei nur 220 ms Erstlatenz (TTFT)
- Offene Modellgewichte (E4B (4B)) 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
Llama 5 · Strengths & Key Advantages
- 5M Kontextfenster und bis zu 128K maximale Output-Token (SWE-bench Verified 74% · GPQA Diamond 84%)
- Hohe Generierungsgeschwindigkeit von 65 Token/Sekunde bei nur 850 ms Erstlatenz (TTFT)
- Offene Modellgewichte (600B+ total (~40-80B )) 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 E4B | Llama 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 4K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 1K | 20K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemma 4 E4B | Llama 5 |
|---|---|---|
| Official API Model ID (SDK) | models/gemma-4-e4b | meta-llama/Llama-5 |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-04 | 2026-04 |
| Knowledge Cutoff | January 2026 | January 2026 |
| Architecture | Edge-optimized dense open transformer | Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) |
| Parameters | E4B (4B) | 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 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| 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) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 2 GB VRAM (4-bit Quantized) · 9 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 Classification | Google DeepMind Frontier Safety Framework Verified | Open-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 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text, vision, video, audio |
| Output Modalities | text | text |
Gemma 4 E4B vs Llama 5: Schnelle Antworten (FAQ)
Which is more intelligent, Gemma 4 E4B or Llama 5?
Llama 5 is more intelligent. It scores 94 compared with 65 on the composite Intelligence Index.
Which model is faster?
Gemma 4 E4B is faster, generating 190 tokens per second compared with 65 tokens per second.
Which model has lower latency?
Gemma 4 E4B has lower latency, with a time to first token of 0.22s 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.
Fazit
Choose Gemma 4 E4B for optimal für self-hosting im eigenen rechenzentrum, fine-tuning und volle datensouveränität, enterprise-rag-systeme und skalierbare api-pipelines., or choose Llama 5 for optimal für self-hosting im eigenen rechenzentrum, fine-tuning und volle datensouveränität, enterprise-rag-systeme und skalierbare api-pipelines..
Häufig gestellte Fragen (FAQ)
Which is cheaper: Gemma 4 E4B or Llama 5?+
Gemma 4 E4B 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 E4B or Llama 5?+
Gemma 4 E4B supports 131K tokens whereas Llama 5 supports 5M tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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.
Offizielle Quellen: Google & Meta Official Documentation