Anthropic Claude (foundation models) vs Meta Llama 4 family
Anthropic Claude (foundation models) vs Meta Llama 4 family: side-by-side comparison of two foundation models (substrate reference) systems — architecture, taxonomy, license, pricing, MCP/A2A support, and direct edges.
Cost & capability
| Anthropic Claude (foundation models) | Meta Llama 4 family | |
|---|---|---|
| Capability band | frontier | frontier |
| Capability composite | 94 | 80 |
| Cost tier | premium | mid |
| $/Mtok input | 5 | 0.20 |
| $/Mtok output | 25 | 3.00 |
| Use cases | Scoped Agentic, Code Generation Focused, Analytical Summarization, Memory Augmented Chat | Scoped Agentic, Code Generation Focused, Analytical Summarization, Memory Augmented Chat, Offline Capable |
Where they differ (14)
Rows where both sides have data and the values disagree — the shortlist of dimensions that actually distinguish these two systems.
| Anthropic Claude (foundation models) | Meta Llama 4 family | |
|---|---|---|
| Capability composite | 94 | 80 |
| Cost tier | premium | mid |
| $/Mtok input | 5 | 0.20 |
| $/Mtok output | 25 | 3.00 |
| Use cases | Scoped Agentic, Code Generation Focused, Analytical Summarization, Memory Augmented Chat | Scoped Agentic, Code Generation Focused, Analytical Summarization, Memory Augmented Chat, Offline Capable |
| Type | Frontier foundation model family (Sonnet 4 / Opus 4.5 / Haiku 4.5) | Open-weights frontier model family (Llama 4 Scout / Maverick / Behemoth) |
| Created | 2023-03 (Claude 1); 2024-06 (Claude 3 Opus/Sonnet/Haiku); 2024-10 (Claude 3.5 Sonnet); 2025-02 (3.7 Sonnet); 2025-05 (Claude 4 Sonnet/Opus); 2025-09 (Sonnet 4.5); 2025-11 (Opus 4.5 + Haiku 4.5) | 2023-02 (Llama 1); 2023-07 (Llama 2); 2024-04 (Llama 3); 2024-12 (Llama 3.3); 2025-04 (Llama 4) |
| Latest release | Opus 4.5 + Haiku 4.5 (2025-11) | Llama 4 Scout + Maverick (2025-04); Behemoth in preview as of 2025 |
| Pricing | Pay-per-token API (Opus 4.5 $5/$25 per 1M; Sonnet 4.5 $3/$15; Haiku 4.5 $0.80/$4); Claude.ai Free + Pro ($20/mo) + Team + Enterprise | Free for self-hosting (Llama community license, <700M MAU); hosted via Together $0.20-$3/M (Maverick); AWS Bedrock + Azure AI consumption |
| Funding | $23B+ total raised; $183B+ valuation (Series F-G 2025-26); Amazon $8B + Google $2B strategic investments | Meta (META) public; ~$1.5T market cap; AI infra capex $60-65B 2025 |
| Deployment | Managed-only (Anthropic API + AWS Bedrock + GCP Vertex) | Self-hostable (open-weights) + hosted via AWS Bedrock + Azure AI + Together AI + Groq + Fireworks + Replicate + Databricks |
| API surface | REST + SDK (Python, TS, Java, Go, Ruby); also via AWS Bedrock + GCP Vertex AI APIs | HTTP via Together / Bedrock / Azure; native via HuggingFace Transformers, vLLM, llama.cpp |
| Optimised for | reasoning, code, agentic tool-use, long-form writing, safety/alignment | open-weights frontier-tier deployers; cost-efficient inference via MoE; on-prem regulated deployments |
| Anti-fit | not for image generation; not for audio synthesis (text-output only); high cost at Opus tier; 200k context smaller than Gemini 3 (10M) | 700M MAU license trigger; not for users wanting weight transparency on dataset (Meta non-disclosing); Behemoth requires extreme GPU (2T MoE) |
At a glance
| Anthropic Claude (foundation models) | Meta Llama 4 family | |
|---|---|---|
| Section | Foundation models (substrate reference) | Foundation models (substrate reference) |
| Tier | T1 | T1 |
| Type | Frontier foundation model family (Sonnet 4 / Opus 4.5 / Haiku 4.5) | Open-weights frontier model family (Llama 4 Scout / Maverick / Behemoth) |
| Created | 2023-03 (Claude 1); 2024-06 (Claude 3 Opus/Sonnet/Haiku); 2024-10 (Claude 3.5 Sonnet); 2025-02 (3.7 Sonnet); 2025-05 (Claude 4 Sonnet/Opus); 2025-09 (Sonnet 4.5); 2025-11 (Opus 4.5 + Haiku 4.5) | 2023-02 (Llama 1); 2023-07 (Llama 2); 2024-04 (Llama 3); 2024-12 (Llama 3.3); 2025-04 (Llama 4) |
| Latest release | Opus 4.5 + Haiku 4.5 (2025-11) | Llama 4 Scout + Maverick (2025-04); Behemoth in preview as of 2025 |
| License | — | Llama 4 community license (Meta-custom; commercial OK <700M MAU) |
| GitHub | — | github.com/meta-llama/llama (combined Llama repos); ~58k stars |
| Pricing | Pay-per-token API (Opus 4.5 $5/$25 per 1M; Sonnet 4.5 $3/$15; Haiku 4.5 $0.80/$4); Claude.ai Free + Pro ($20/mo) + Team + Enterprise | Free for self-hosting (Llama community license, <700M MAU); hosted via Together $0.20-$3/M (Maverick); AWS Bedrock + Azure AI consumption |
| Funding | $23B+ total raised; $183B+ valuation (Series F-G 2025-26); Amazon $8B + Google $2B strategic investments | Meta (META) public; ~$1.5T market cap; AI infra capex $60-65B 2025 |
| Deployment | Managed-only (Anthropic API + AWS Bedrock + GCP Vertex) | Self-hostable (open-weights) + hosted via AWS Bedrock + Azure AI + Together AI + Groq + Fireworks + Replicate + Databricks |
| API surface | REST + SDK (Python, TS, Java, Go, Ruby); also via AWS Bedrock + GCP Vertex AI APIs | HTTP via Together / Bedrock / Azure; native via HuggingFace Transformers, vLLM, llama.cpp |
| Optimised for | reasoning, code, agentic tool-use, long-form writing, safety/alignment | open-weights frontier-tier deployers; cost-efficient inference via MoE; on-prem regulated deployments |
| Anti-fit | not for image generation; not for audio synthesis (text-output only); high cost at Opus tier; 200k context smaller than Gemini 3 (10M) | 700M MAU license trigger; not for users wanting weight transparency on dataset (Meta non-disclosing); Behemoth requires extreme GPU (2T MoE) |
Taxonomy
| Axis | Anthropic Claude (foundation models) | Meta Llama 4 family |
|---|---|---|
| storage | parametric | parametric |
| retrieval | parametric-recall | parametric-recall |
| persistence | parametric-permanent | parametric-permanent |
| update | read-only | read-only |
| unit | weight | weight |
| governance | opaque | opaque |
| conflict | n/a | n/a |
Pros & cons
Anthropic Claude (foundation models)
Pros: Frontier coding capability (Opus 4.5); strong safety profile via Constitutional AI; native computer-use + tool-use; broad SDK coverage; AWS Bedrock + GCP Vertex distribution.
Cons: 200k context smaller than Gemini 3's 10M; Opus pricing at premium tier; no open-weights option; no image / audio output; rate-limited at API tier without enterprise contract.
Meta Llama 4 family
Pros: Open weights (community license) — only frontier-tier family runnable on-prem; MoE architecture cheap to serve (17B active params); 10M context (Scout) matches Gemini 3.
Cons: License excludes 700M+ MAU products; dataset opacity; multimodal less mature than GPT-4o / Gemini 3; Behemoth not yet released as of 2025-12.