LLM Comparison
Llama 4 Maverick vs GPT-5.5
Side-by-side specs, pricing & capabilities · Updated May 2026
Price vs Intelligence
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2/6 modelsSame tier:
| Organization | ||
| OpenTools Score | 38 101 | 90 5.1 |
| Family | Llama | GPT |
| Status | Current | Current |
| Release Date | Apr 2025 | Apr 2026 |
| Context Window | 1.0M tokens | 1.1M tokens |
| Input Price | $0.15/M tokens | $5.00/M tokens |
| Output Price | $0.60/M tokens | $30.00/M tokens |
| Pricing Notes | — | Cached input: $0.50/M tokens. Long context (>272K tokens): 2x input, 1.5x output. Batch API: 50% discount. Priority: 2.5x standard. |
| Capabilities | textvisioncode | textvisioncodetool-useextended-thinkingcomputer-useweb-search |
| Training Cutoff | — | December 2025 |
| Max Output | 16K tokens | 128K tokens |
| API Identifier | meta-llama/llama-4-maverick | openai/gpt-5.5 |
| Benchmarks | ||
| MMLU | 85.5meta | 92.4openai |
| MMLU Pro | 80.5meta | — |
| GPQA | 69.8meta | — |
| MATH | 61.2meta | — |
| LiveCodeBench | 43.4meta | — |
| MMMU | 73.4meta | — |
| GPQA Diamond | — | 93.6openai |
| ARC-AGI-2 | — | 85openai |
| Terminal-Bench 2.0 | — | 82.7openai |
| SWE-bench Pro | — | 58.6openai |
| OSWorld-Verified | — | 78.7openai |
| BrowseComp | — | 84.4openai |
| MMMU-Pro | — | 81.2openai |
| FrontierMath Tier 4 | — | 35.4openai |
| HLE (with tools) | — | 52.2openai |
| GDPval | — | 84.9openai |
| Toolathlon | — | 55.6openai |
| CyberGym | — | 81.8openai |
| MRCR v2 512K-1M | — | 74openai |
| View Llama 4 Maverick | View GPT-5.5 | |
Cost Calculator
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| Model | Input | Output | Total / mo | vs Best |
|---|---|---|---|---|
| Llama 4 MaverickCheapest | $0.15 | $0.30 | $0.45 | — |
| GPT-5.5 | $5.00 | $15.00 | $20.00 | +4344% |
Meta
Llama 4 Maverick
Llama 4 Maverick is a multimodal llm from Meta. Supports up to 1,048,576 token context window. Achieves 88.4% on MMLU. Available from $0.15/M input tokens.
OpenAI
GPT-5.5
GPT-5.5 is OpenAI's smartest and most intuitive model, built for agentic work like coding, research, and data analysis. It matches GPT-5.4 per-token latency while delivering higher intelligence with significantly fewer tokens. Supports a 1,050,000 token context window and five reasoning effort levels (none through xhigh).
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