o3 vs Llama 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare o3 by OpenAI against Llama 5 by Meta across real-world coding, reasoning, token economics, and latency.
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o3
ディープ推論・論理エンジンOpenAI の o3 は、高度な数学的推論、科学技術計算、多段階の思考連鎖（Chain-of-Thought）向けに設計されたAIモデルです。200K のコンテキストウィンドウ、毎秒 75 トークンの生成速度、知能指数 56/100（1Mトークンあたり入力$2・出力$8）を実現しています。
$2.00 / 1M
$8.00 / 1M
200K tokens
69.1%
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 200K tokens).
- o3 is faster at 75 tok/s vs 65 tok/s.
- Llama 5 leads on SWE-bench coding (74.0% vs 69.1%).
| Feature | o3 | Llama 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 56 | 94 |
Provider | OpenAI | Meta |
Input Price Cost per million input tokens | $2.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $8.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.55 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 200K tokens~300 A4 pages of text | 5M tokens~7,500 A4 pages of text |
Max Output Tokens Per single request | 100K | 128K |
Throughput Speed Tokens generated per second | 75 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | 69.1% | 74% |
GPQA Diamond Graduate-level reasoning | 76.5% | 84% |
Cached Input Price Prompt caching discount per 1M tokens | $0.50 / 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.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
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.
o3 · Strengths & Key Advantages
- 200K のコンテキストウィンドウと最大 100K トークンの出力容量（SWE-bench Verified 69.1% · GPQA Diamond 76.5%）
- 毎秒 75 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 2800ms の低遅延
- 優れたAPI経済性（1Mトークンあたり入力$2・出力$8）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
- 大量のトークンを処理する本番環境では、プロンプトキャッシュや Batch API の併用によるコスト最適化が推奨されます
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 | o3 | Llama 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.35 | $0.00 |
Output Tokens / Task Tokens generated per task | 48K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 26K | 20K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | o3 | Llama 5 |
|---|---|---|
| Official API Model ID (SDK) | o3 | meta-llama/Llama-5 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-04 | 2026-04 |
| Knowledge Cutoff | June 2024 | January 2026 |
| Architecture | Reasoning transformer with adjustable thinking effort | 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) · 800,000 – 2,000,000 TPM | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($2/1M in) across full 200K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.5/1M (no extra cache-write fee) | 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | 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 | 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 | OpenAI Preparedness Framework Audited & System Card Published | 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 | text, vision, video, audio |
| Output Modalities | text | text |
o3 vs Llama 5: よくある質問 (FAQ)
Which is more intelligent, o3 or Llama 5?
Llama 5 is more intelligent. It scores 94 compared with 56 on the composite Intelligence Index.
Which model is faster?
o3 is faster, generating 75 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 $1.55 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama 5 has lower latency, with a time to first token of 0.85s compared with 2.80s.
Which model has a larger context window?
Llama 5 has a larger context window, supporting 5M tokens compared with 200K tokens.
総合評価
Choose o3 for 高度な数学的推論、科学技術計算、多段階の思考連鎖（chain-of-thought）、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: o3 or Llama 5?+
o3 costs $2/1M input and $8/1M output tokens, while Llama 5 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: o3 or Llama 5?+
o3 supports 200K 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: OpenAI & Meta Official Documentation