LLM Comparison
Llama 4 Scout vs Claude Opus 4.7
Side-by-side specs, pricing & capabilities · Updated May 2026
Price vs Intelligence
Add to comparison
2/6 modelsSame tier:
| Organization | ||
| OpenTools Score | 17 87.4 | 71 4.7 |
| Family | Llama | Claude |
| Status | Current | Current |
| Release Date | Apr 2025 | Apr 2026 |
| Context Window | 328K tokens | 1.0M tokens |
| Input Price | $0.08/M tokens | $5.00/M tokens |
| Output Price | $0.30/M tokens | $25.00/M tokens |
| Pricing Notes | — | Cache read: $0.5000/M tokens |
| Capabilities | textvisioncode | textvisioncodetool-use |
| Max Output | 16K tokens | 128K tokens |
| API Identifier | meta-llama/llama-4-scout | anthropic/claude-opus-4.7 |
| Benchmarks | ||
| MMLU | 79.6meta | 84.7anthropic |
| MMLU Pro | 74.3meta | — |
| GPQA | 57.2meta | — |
| MATH | 50.3meta | — |
| LiveCodeBench | 32.8meta | — |
| MMMU | 69.4meta | — |
| MGSM | 91meta | — |
| MMLU-Pro | — | 78.1anthropic |
| MMMLU | — | 92anthropic |
| GPQA Diamond | — | 94.2anthropic |
| HLE | — | 54.7artificial-analysis |
| SWE-bench Verified | — | 87.6anthropic |
| SWE-bench Pro | — | 64.3anthropic |
| SWE-bench Multilingual+Multimodal | — | 80.5anthropic |
| Terminal-Bench | — | 69.4anthropic |
| MCP-Atlas | — | 77.3anthropic |
| Berkeley Function Calling | — | 77.3anthropic |
| OSWorld-Verified | — | 78anthropic |
| BrowseComp | — | 79.3anthropic |
| CharXiv-R | — | 91anthropic |
| DocVQA | — | 93.1anthropic |
| CyberGym | — | 73.1anthropic |
| GDPVal-AA Elo | — | 1753artificial-analysis |
| View Llama 4 Scout | View Claude Opus 4.7 | |
Cost Calculator
Enter your expected monthly token usage to compare costs.
| Model | Input | Output | Total / mo | vs Best |
|---|---|---|---|---|
| Llama 4 ScoutCheapest | $0.08 | $0.15 | $0.23 | — |
| Claude Opus 4.7 | $5.00 | $12.50 | $17.50 | +7509% |
Meta
Llama 4 Scout
Llama 4 Scout is a multimodal llm from Meta. Supports up to 327,680 token context window. Achieves 82.6% on MMLU. Available from $0.08/M input tokens.
Anthropic
Claude Opus 4.7
Claude Opus 4.7 is Anthropic's most capable generally available model, with significant improvements in advanced software engineering, agentic tool use, and vision resolution. Achieves 87.6% on SWE-bench Verified and 94.2% on GPQA Diamond. Supports up to 1,000,000 token context window with 3.3x higher-resolution vision than Opus 4.6.
More Comparisons
Looking for more AI models?
Browse All LLMs