Claude Opus 5.5 vs Llama 4 Behemoth
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Claude Opus 5.5 by Anthropic against Llama 4 Behemoth by Meta across real-world coding, reasoning, token economics, and latency.
任意のAIモデルを切り替えて比較
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Live comparison: no in-depth editorial review has been published for this pair yet.
Claude Opus 5.5
高度な推論モデルAnthropic が提供する Claude Opus 5.5 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 $4.0/100万トークン・出力 $20.0/100万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。
$4.00 / 1M
$20.00 / 1M
1M tokens
84.6%
Llama 4 Behemoth
オープンウェイトモデルMeta が提供する Llama 4 Behemoth は、最大 10M 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
10M tokens
75%
ひと目でわかる主な違い
- Llama 4 Behemoth is 1.0x cheaper for API usage (input + output tokens combined).
- Llama 4 Behemoth has a larger context window (10M vs 1M tokens).
- Claude Opus 5.5 is faster at 78 tok/s vs 45 tok/s.
- Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 75.0%).
| Feature | Claude Opus 5.5 | Llama 4 Behemoth |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 94 | 93 |
Provider | Anthropic | Meta |
Input Price Cost per million input tokens | $4.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $20.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $3.08 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 10M tokens~15,000 A4 pages of text |
Max Output Tokens Per single request | 128K | 64K |
Throughput Speed Tokens generated per second | 78 tok/s | 45 tok/s |
SWE-bench Verified Real-world software engineering | 84.6% | 75% |
GPQA Diamond Graduate-level reasoning | 82.3% | 82% |
Cached Input Price Prompt caching discount per 1M tokens | $0.40 / 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.
Claude Opus 5.5 · Strengths & Key Advantages
- Anthropic の高度な Extended Autonomous Hybrid Reasoning Transformer 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 $4.0/1M、出力 $20.0/1M）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
Llama 4 Behemoth · Strengths & Key Advantages
- Meta の高度な Colossal sparse MoE architecture (288B active / 2T total, 16-of-2 MoE) アーキテクチャと 10M 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 | Claude Opus 5.5 | Llama 4 Behemoth |
|---|---|---|
Cost per Task Estimated USD to finish one task | $3.85 | $0.00 |
Output Tokens / Task Tokens generated per task | 68K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 42K | 15K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | Claude Opus 5.5 | Llama 4 Behemoth |
|---|---|---|
| Official API Model ID (SDK) | claude-opus-5-5-20260901 | meta-llama/Llama-4-Behemoth |
| API Protocol | Anthropic Messages API + AWS Bedrock + Vertex AI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-09 | 2026-05 |
| Knowledge Cutoff | June 2026 | February 2026 |
| Architecture | Extended Autonomous Hybrid Reasoning Transformer | Colossal sparse MoE architecture (288B active / 2T total, 16-of-2 MoE) |
| Parameters | Confidential | 2T total / 288B active (16-of-2 MoE) |
| Enterprise Rate Limits (RPM / TPM) | 4,000 – 10,000 RPM (Tier 4 + Priority 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) | 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 | $0.4/1M cached read (90% off); 5-min default & 1-hour extended 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 | Native Web Search Citations + Code Execution Tool + MCP Connectors | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Custom Model Tuning via Amazon Bedrock & Prompt 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) |
| 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) · 1240 GB VRAM (4-bit Quantized) · 4300 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated | 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 |
| Output Modalities | text | text |
Claude Opus 5.5 vs Llama 4 Behemoth: よくある質問 (FAQ)
Which is more intelligent, Claude Opus 5.5 or Llama 4 Behemoth?
Claude Opus 5.5 is more intelligent. It scores 94 compared with 93 on the composite Intelligence Index.
Which model is faster?
Claude Opus 5.5 is faster, generating 78 tokens per second compared with 45 tokens per second.
Which model is cheaper to run?
Llama 4 Behemoth is cheaper at $0.00 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama 4 Behemoth has lower latency, with a time to first token of 1.20s compared with 1.40s.
Which model has a larger context window?
Llama 4 Behemoth has a larger context window, supporting 10M tokens compared with 1M tokens.
総合評価
Choose Claude Opus 5.5 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、anthropic エコシステムの統合, or choose Llama 4 Behemoth 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: Claude Opus 5.5 or Llama 4 Behemoth?+
Claude Opus 5.5 costs $4/1M input and $20/1M output tokens, while Llama 4 Behemoth costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: Claude Opus 5.5 or Llama 4 Behemoth?+
Claude Opus 5.5 supports 1M tokens whereas Llama 4 Behemoth supports 10M tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
- Aion-3.0 vs Aion-Mini: Benchmarks, Pricing & Speed Comparison比較
- Aion-3.0 vs Gemini 3.6 Flash: Benchmarks, Pricing & Speed Comparison比較
- Aion-Mini vs Laguna XS 2.1: Benchmarks, Pricing & Speed Comparison比較
- Aya Expanse 32B vs Llama 3.2 90B Vision: Benchmarks, Pricing & Speed Comparison比較
- Aya Expanse 8B vs Command R+: Benchmarks, Pricing & Speed Comparison比較
- Aya Expanse 8B vs Qwen 2.5 7B Instruct: Benchmarks, Pricing & Speed Comparison比較
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: Anthropic & Meta Official Documentation