LLM Comparison
Claude Opus 4.7 vs Llama Guard 4 12B
Side-by-side specs, pricing & capabilities · Updated October 2026
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2/6 modelsSame tier:
| Organization | ||
| OpenTools Score | 70 4.7 | |
| Family | Claude | Llama |
| Status | Current | Current |
| Release Date | Apr 2026 | Apr 2025 |
| Context Window | 1.0M tokens | 164K tokens |
| Input Price | $5.00/M tokens | $0.18/M tokens |
| Output Price | $25.00/M tokens | $0.18/M tokens |
| Pricing Notes | Cache read: $0.5000/M tokens | — |
| Capabilities | textvisioncodetool-use | textvisioncode |
| Max Output | 128K tokens | 40 tokens |
| API Identifier | anthropic/claude-opus-4.7 | meta-llama/llama-guard-4-12b |
| Benchmarks | ||
| MMLU | 84.7anthropic | — |
| 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 | — |
| Terminal-Bench 2.1 | 83.1461artificial-analysis | — |
| LG4 English output-filter F1 | — | 61meta |
| LG4 multilingual output-filter F1 | — | 51meta |
| LG4 single-image output-filter F1 | — | 38meta |
| LG4 multi-image output-filter F1 | — | 52meta |
| View Claude Opus 4.7 | View Llama Guard 4 12B | |
Cost Calculator
Enter your expected monthly token usage to compare costs.
| Model | Input | Output | Total / mo | vs Best |
|---|---|---|---|---|
| Llama Guard 4 12BCheapest | $0.18 | $0.09 | $0.27 | — |
| Claude Opus 4.7 | $5.00 | $12.50 | $17.50 | +6381% |
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.
Meta
Llama Guard 4 12B
Llama Guard 4 12B is a Meta multimodal safety-classification model for text and image prompt or response moderation using MLCommons hazard categories.
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