Gemini 1.5 Flash vs Llama 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Gemini 1.5 Flash by Google against Llama 5 by Meta across real-world coding, reasoning, token economics, and latency.
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Gemini 1.5 Flash
超高速・低遅延レスポンスGoogle の Gemini 1.5 Flash は、超低遅延のリアルタイム対話、高速ルーティング、大量リクエスト処理向けに設計されたAIモデルです。1.0M のコンテキストウィンドウ、毎秒 180 トークンの生成速度、知能指数 38/100（1Mトークンあたり入力$0.075・出力$0.3）を実現しています。
$0.07 / 1M
$0.30 / 1M
1.05M tokens
20%
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
- Llama 5 is 1.0x cheaper for API usage (input + output tokens combined).
- Llama 5 has a larger context window (5M vs 1M tokens).
- Gemini 1.5 Flash is faster at 180 tok/s vs 65 tok/s.
- Llama 5 leads on SWE-bench coding (74.0% vs 20.0%).
| Feature | Gemini 1.5 Flash | Llama 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 38 | 94 |
Provider | Meta | |
Input Price Cost per million input tokens | $0.07 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.30 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.06 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 1.05M tokens~1,573 A4 pages of text | 5M tokens~7,500 A4 pages of text |
Max Output Tokens Per single request | 8K | 128K |
Throughput Speed Tokens generated per second | 180 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | 20% | 74% |
GPQA Diamond Graduate-level reasoning | 42.1% | 84% |
Cached Input Price Prompt caching discount per 1M tokens | $0.02 / 1M | $0.00 / 1M |
分野別勝者スコアカード
At-a-glance decision framework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.
Accuracy when reasoning over very large inputs that fill the model context window.
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.
Gemini 1.5 Flash · Strengths & Key Advantages
- 1.0M のコンテキストウィンドウと最大 8K トークンの出力容量（SWE-bench Verified 20% · GPQA Diamond 42.1%）
- 毎秒 180 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 320ms の低遅延
- 優れたAPI経済性（1Mトークンあたり入力$0.075・出力$0.3）に加え、プロンプトキャッシュや構造化JSON出力・関数呼び出しに完全対応
Considerations & Trade-offs
- クラウド専用のプロプライエタリモデルのため、モデル重みをダウンロードしてオフライン実行することはできません
- 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 | Gemini 1.5 Flash | Llama 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.20 | $0.00 |
Output Tokens / Task Tokens generated per task | 24K | 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 | Gemini 1.5 Flash | Llama 5 |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-1-5-flash | meta-llama/Llama-5 |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-05 | 2026-04 |
| Knowledge Cutoff | May 2024 | January 2026 |
| Architecture | Distilled Sparse MoE Multimodal | Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) |
| Parameters | Confidential | 600B+ total (~40-80B active) |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0.01875/1M with configurable TTL | 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 | Vertex AI Supervised Adapter Tuning & Context Distillation | 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) · Video (~258–300 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 | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | 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) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision, audio, video | text, vision, video, audio |
| Output Modalities | text | text |
Gemini 1.5 Flash vs Llama 5: よくある質問 (FAQ)
Which is more intelligent, Gemini 1.5 Flash or Llama 5?
Llama 5 is more intelligent. It scores 94 compared with 38 on the composite Intelligence Index.
Which model is faster?
Gemini 1.5 Flash is faster, generating 180 tokens per second compared with 65 tokens per second.
Which model is cheaper to run?
Llama 5 is cheaper at $0.00 per 1M tokens blended, compared with $0.06 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemini 1.5 Flash has lower latency, with a time to first token of 0.32s compared with 0.85s.
Which model has a larger context window?
Llama 5 has a larger context window, supporting 5M tokens compared with 1.05M tokens.
総合評価
Choose Gemini 1.5 Flash for 超低遅延のリアルタイム対話、高速ルーティング、大量リクエスト処理、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: Gemini 1.5 Flash or Llama 5?+
Gemini 1.5 Flash costs $0.075/1M input and $0.3/1M output tokens, while Llama 5 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: Gemini 1.5 Flash or Llama 5?+
Gemini 1.5 Flash supports 1.0M 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