Grok 4.20 (Reasoning) vs Llama 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Grok 4.20 (Reasoning) by xAI against Llama 5 by Meta across real-world coding, reasoning, token economics, and latency.
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Grok 4.20 (Reasoning)
高度な推論モデルxAI が提供する Grok 4.20 (Reasoning) は、最大 1M トークンのコンテキストウィンドウを備え、入力 $1.25/100万トークン・出力 $2.5/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$3.00 / 1M
$15.00 / 1M
1M tokens
68.2%
Llama 5
オープンウェイトモデルMeta が提供する Llama 5 は、最大 5M トークンのコンテキストウィンドウを備え、入力 $0/100万トークン・出力 $0/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$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).
- Grok 4.20 (Reasoning) is faster at 95 tok/s vs 65 tok/s.
- Llama 5 leads on SWE-bench coding (74.0% vs 68.2%).
| Feature | Grok 4.20 (Reasoning) | Llama 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 68 | 94 |
Provider | xAI | Meta |
Input Price Cost per million input tokens | $3.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $15.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $2.63 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 5M tokens~7,500 A4 pages of text |
Max Output Tokens Per single request | 66K | 128K |
Throughput Speed Tokens generated per second | 95 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | 68.2% | 74% |
GPQA Diamond Graduate-level reasoning | 72.8% | 84% |
Cached Input Price Prompt caching discount per 1M tokens | $0.75 / 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.
Grok 4.20 (Reasoning) · Strengths & Key Advantages
- xAI の高度な Colossus-Scale Reasoning Sparse MoE アーキテクチャと 1M トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $1.25/1M、出力 $2.5/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Llama 5 · Strengths & Key Advantages
- Meta の高度な Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) アーキテクチャと 5M トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0/1M、出力 $0/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Grok 4.20 (Reasoning) | Llama 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.45 | $0.00 |
Output Tokens / Task Tokens generated per task | 45K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 28K | 20K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | Grok 4.20 (Reasoning) | Llama 5 |
|---|---|---|
| Official API Model ID (SDK) | grok-4-20-reasoning-latest | meta-llama/Llama-5 |
| API Protocol | xAI REST API (OpenAI-Compatible) | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-03 | 2026-04 |
| Knowledge Cutoff | November 2025 | January 2026 |
| Architecture | Colossus-Scale Reasoning Sparse MoE | Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) |
| Parameters | Confidential | 600B+ total (~40-80B active) |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 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) | Flat rate ($3/1M in) across full 1000K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Cached input at $0.75/1M tokens | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | 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 | Real-time Web & X (Twitter) Search Grounding + Code Execution | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | API Prompt Engineering & RAG 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) | 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 | xAI Production Safety & Alignment 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 | text, vision, video, audio |
| Output Modalities | text | text |
Grok 4.20 (Reasoning) vs Llama 5: よくある質問 (FAQ)
Which is more intelligent, Grok 4.20 (Reasoning) or Llama 5?
Llama 5 is more intelligent. It scores 94 compared with 68 on the composite Intelligence Index.
Which model is faster?
Grok 4.20 (Reasoning) is faster, generating 95 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 $2.63 (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 0.98s.
Which model has a larger context window?
Llama 5 has a larger context window, supporting 5M tokens compared with 1M tokens.
総合評価
Choose Grok 4.20 (Reasoning) for エンタープライズ自動化、ソフトウェア開発、ai エージェント構築、xai エコシステムの統合, or choose Llama 5 for エンタープライズ自動化、ソフトウェア開発、ai エージェント構築、meta エコシステムの統合.
よくある質問 (FAQ)
Which is cheaper: Grok 4.20 (Reasoning) or Llama 5?+
Grok 4.20 (Reasoning) costs $3/1M input and $15/1M output tokens, while Llama 5 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: Grok 4.20 (Reasoning) or Llama 5?+
Grok 4.20 (Reasoning) supports 1M 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: xAI & Meta Official Documentation