Gemma 3n vs Llama 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Gemma 3n by Google against Llama 5 by Meta across real-world coding, reasoning, token economics, and latency.
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Gemma 3n
音声・スピーチAIモデルGoogle の Gemma 3n は、自然な音声合成（TTS）、リアルタイム音声対話、高精度な文字起こし向けに設計されたAIモデルです。32K のコンテキストウィンドウ、毎秒 150 トークンの生成速度、知能指数 52/100（無料のオープンウェイト提供）を実現しています。
$0.00 / 1M
$0.00 / 1M
32K tokens
N/A
Llama 5
オープンウェイト基盤モデルMeta の Llama 5 は、オンプレミス環境でのセルフホスト、ファインチューニング、完全なデータ主権の確保向けに設計されたAIモデルです。5M のコンテキストウィンドウ、毎秒 65 トークンの生成速度、知能指数 94/100（無料のオープンウェイト提供）を実現しています。
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
ひと目でわかる主な違い
- Both models cost the same under this workload — choose on capability, not price.
- Llama 5 has a larger context window (5M vs 32K tokens).
- Gemma 3n is faster at 150 tok/s vs 65 tok/s.
| Feature | Gemma 3n | Llama 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 52 | 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 | 32K tokens~48 A4 pages of text | 5M tokens~7,500 A4 pages of text |
Max Output Tokens Per single request | 4K | 128K |
Throughput Speed Tokens generated per second | 150 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 74% |
GPQA Diamond Graduate-level reasoning | N/A | 84% |
分野別勝者スコアカード
At-a-glance decision frameworkEvaluations ongoing
検証済みベンチマークスコア比較
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.
レイテンシ＆応答時間
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
リアルタイムAPIトークン料金シミュレーター
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemma 3n · Strengths & Key Advantages
- 32K のコンテキストウィンドウと最大 4K トークンの出力容量（MMLU-Pro 64% · Arena Elo 1220）
- 毎秒 150 トークンの高速スループットと、初回トークン到達時間（TTFT）わずか 220ms の低遅延
- オープンウェイトモデル (2B / 5B)として vLLM・SGLang・Ollama を用いた自社GPUでのローカル運用が可能
Considerations & Trade-offs
- 非量子化のフル精度でローカル推論を行うには十分なGPU VRAM容量が必要です
- 極めて高度な専門科学の証明問題では、上位の大型推論特化モデルの方がより深い洞察を提供する場合があります
Llama 5 · Strengths & Key Advantages
- 5M のコンテキストウィンドウと最大 128K トークンの出力容量（SWE-bench Verified 74% · GPQA Diamond 84%）
- 毎秒 65 トークンの高速スループットと、初回トークン到達時間（TTFT）わずか 850ms の低遅延
- オープンウェイトモデル (600B+ total (~40-80B ))として vLLM・SGLang・Ollama を用いた自社GPUでのローカル運用が可能
Considerations & Trade-offs
- 非量子化のフル精度でローカル推論を行うには十分なGPU VRAM容量が必要です
- 極めて高度な専門科学の証明問題では、上位の大型推論特化モデルの方がより深い洞察を提供する場合があります
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemma 3n | Llama 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 3K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 20K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemma 3n | Llama 5 |
|---|---|---|
| Official API Model ID (SDK) | models/gemma-3n | meta-llama/Llama-5 |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-06 | 2026-04 |
| Knowledge Cutoff | March 2025 | January 2026 |
| Architecture | Edge-optimized multimodal transformer with audio support | Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) |
| Parameters | 2B / 5B | 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) · Audio (~25–32 tokens/sec) | 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) · 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 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, audio | text, vision, video, audio |
| Output Modalities | text | text |
Gemma 3n vs Llama 5: よくある質問 (FAQ)
Which is more intelligent, Gemma 3n or Llama 5?
Llama 5 is more intelligent. It scores 94 compared with 52 on the composite Intelligence Index.
Which model is faster?
Gemma 3n is faster, generating 150 tokens per second compared with 65 tokens per second.
Which model has lower latency?
Gemma 3n 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 32K tokens.
総合評価
Choose Gemma 3n for 自然な音声合成（tts）、リアルタイム音声対話、高精度な文字起こし、エンタープライズrag基盤、スケーラブルな本番api統合に最適。, or choose Llama 5 for オンプレミス環境でのセルフホスト、ファインチューニング、完全なデータ主権の確保、エンタープライズrag基盤、スケーラブルな本番api統合に最適。.
よくある質問 (FAQ)
Which is cheaper: Gemma 3n or Llama 5?+
Gemma 3n 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 3n or Llama 5?+
Gemma 3n supports 32K 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.
公的ソース: Google & Meta Official Documentation