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.
Changez et Comparez les Modèles
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Live comparison: no in-depth editorial review has been published for this pair yet.
Claude Opus 5.5
Titan d'intelligence de pointed'Anthropic pinnacle modèle de pointe conçu pour à long terme agentic ingénierie logicielle, dense multimodal document synthèse, et autonome recherche flux de travail.
$4.00 / 1M
$20.00 / 1M
1M tokens
84.6%
Llama 4 Behemoth
Géant open source de 2Tde Meta gargantuan 2-billions paramètre ouvert modèle pushing le de pointe d'ouvert-weight capacité et 10M contexte.
$0.00 / 1M
$0.00 / 1M
10M tokens
75%
Différences Clés en un Coup d’Œil
- 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%).
| Caractéristique | 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 |
Qui gagne où ?
At-a-glance decision frameworkScores de Benchmarks Vérifiés
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.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Claude Opus 5.5 · Strengths & Key Advantages
- inégalé autonome multi-étapes codage agentique et refactorisation
- Flawless multidocument raisonnement sur 1M token contexte avec no long-prompt surcharge
- le plus élevé Artificial analyse intelligence Index ever measured (58/100), leading SWE-bench Pro à 89.9%
- Precise outil exécution avec robust error-recovery boucles
Considerations & Trade-offs
- Premium sortie tarification ($20.00/M) plus heavy raisonnement-token usage (~119K sortie tokens par index tâche) keeps long agentic fonctionne expensive
- long pensée times à maximum effort peut push un unique hard agentic tâche toward ~$13
Llama 4 Behemoth · Strengths & Key Advantages
- sans égal 2T paramètre ouvert capacité
- Staggering ~10 millions token contexte horizon
- d'avant-garde codage et raisonnement scores
Considerations & Trade-offs
- Demands élevé-end datacenter multinœud GPU clusters (8x H100 minimum)
- Slow inférence débit par rapport à smaller modèles
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 |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | 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: Réponses Rapides (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.
Verdict
Choose Claude Opus 5.5 for grand-échelle logiciel development agents, d'entreprise codebases, et mission critique scientifique synthèse, or choose Llama 4 Behemoth for national laboratories, grand d'entreprise ai clusters, et extreme scientifique modeling.
Questions fréquentes
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.
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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: Anthropic & Meta Official Documentation