\n\n| 1<\/td>\n | GPT\u20115 (OpenAI)<\/td>\n | OpenAI<\/td>\n | Multimodal; released 2025; state-of-the-art reasoning. (Wikipedia<\/a>)<\/td>\n | Leading performance on broad tasks; very strong reasoning & multimodal.<\/td>\n | Likely highest cost; proprietary; black-box characteristics.<\/td>\n<\/tr>\n |
\n| 2<\/td>\n | GPT\u20114o (OpenAI)<\/td>\n | OpenAI<\/td>\n | 175B+ parameters; large context window (128 k tokens or more) in some reports. (Stratlogtech<\/a>)<\/td>\n | Very versatile; strong all-rounder; many integrations.<\/td>\n | Slightly behind the very latest frontier model; cost and licensing.<\/td>\n<\/tr>\n |
\n| 3<\/td>\n | Gemini 2.5 Pro (Google DeepMind)<\/td>\n | Google DeepMind<\/td>\n | Context window ~1 M tokens (or higher according to some sources) (Unite.AI<\/a>)<\/td>\n | Excellent multimodal capacity; large context support; strong reasoning benchmarks.<\/td>\n | Proprietary; integration may favour Google ecosystem; cost\/licensing trade-offs.<\/td>\n<\/tr>\n |
\n| 4<\/td>\n | Claude 4 Opus (Anthropic)<\/td>\n | Anthropic<\/td>\n | Large context window (~200k tokens or more) according to reports. (codedesign.ai<\/a>)<\/td>\n | Very good for high-stakes domains (enterprise, legal, research) where safety\/consistency matter.<\/td>\n | Possibly slightly weaker in ultra-frontier benchmarks; proprietary.<\/td>\n<\/tr>\n |
\n| 5<\/td>\n | Llama 4 (Meta)<\/td>\n | Meta Platforms<\/td>\n | Open-source versions (Scout \/ Maverick) with large context windows; multimodal. (Reuters<\/a>)<\/td>\n | Open-source flexibility; self-hosting possible; good for customisation.<\/td>\n | Might lag slightly top-tier on some benchmarks; requires more infrastructure to self-host at top size.<\/td>\n<\/tr>\n |
\n| 6<\/td>\n | Qwen 3 (Alibaba Cloud)<\/td>\n | Alibaba Cloud<\/td>\n | Chinese\/International multilingual, open model family. (techtarget.com<\/a>)<\/td>\n | Strong support for multilingual \/ Asian-language tasks; cost-effective options.<\/td>\n | May have less ecosystem maturity in some regions; licensing\/licence terms may vary.<\/td>\n<\/tr>\n |
\n| 7<\/td>\n | DeepSeek R1 (DeepSeek)<\/td>\n | DeepSeek AI<\/td>\n | Reportedly 671B parameters Mixture-of-Experts (MoE) architecture in some sources. (Champaign Magazine<\/a>)<\/td>\n | Very cost-efficient architecture; strong in math\/coding tasks in some benchmarks.<\/td>\n | Less mature ecosystem; possibly less widely adopted; enterprise support may be smaller.<\/td>\n<\/tr>\n |
\n| 8<\/td>\n | Command R+ (Cohere)<\/td>\n | Cohere<\/td>\n | ~104B parameters (as per one source) in 2024. (nurix-web.webflow.io<\/a>)<\/td>\n | Good performance for retrieval-augmented generation (RAG) \/ enterprise knowledge base tasks.<\/td>\n | Smaller scale than the largest frontier models; fewer multimodal features (depending on version).<\/td>\n<\/tr>\n |
\n| 9<\/td>\n | StableLM 2 (Stability AI)<\/td>\n | Stability AI<\/td>\n | Open-source model series; e.g., 12B parameter model supporting multiple languages. (techtarget.com<\/a>)<\/td>\n | Very good for cost-constrained or local-hosting scenarios; flexible licensing.<\/td>\n | Smaller scale; may underperform frontier models in some heavy reasoning \/ long-context benchmarks.<\/td>\n<\/tr>\n |
\n| 10<\/td>\n | Falcon 180B (Falcon series)<\/td>\n | TII \/ Falcon \/ Open models<\/td>\n | 180B parameter open-model; high quality open research model. (arXiv<\/a>)<\/td>\n | Leading open-model in many open-source comparisons; good trade-off between performance & flexibility.<\/td>\n | Still open vs proprietary trade-offs; may need more tuning\/infrastructure for enterprise deployment.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
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