Openai/696a677f-b378-800c-b260-be955277fa54

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gpt-5-2

User: Can you tell me what the top 10 LLM's are?

Can you tell me what the top 10 LLM's are? And what their best at as compared to the others in the Top 10?

Assistant: Here’s a current 2026 snapshot of the leading Large Language Models LLMs with a Top 10 ranking and what each is best at…

Here’s a current 2026 snapshot of the leading Large Language Models (LLMs) with a Top 10 ranking and what each is best at relative to others. Rankings reflect general performance, breadth of capability, and real-world usage based on recent AI industry comparisons and benchmarks. Botpress

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Best at: All-around intelligence, reasoning, multimodal understanding, code generation, and large-context tasks.

• Industry standard for general reasoning, creative generation, dialogue, coding help, and multimodal inputs (text, image, audio).
• Excellent API ecosystem, deep integrations (e.g., GitHub Copilot).
• Strong language translation quality. clarifai.com

Best at: Safe and controlled outputs, nuanced reasoning, long-context memory.

• Particularly good for enterprise chatbots and applications where safety, consistency, and ethical constraints matter.
• Often yields more reflective, less “edgy” outputs compared to others. Zapier

Best at: Real-world knowledge, search-integrated factuality, multimodal reasoning.

• Integrated with Google Search makes it strong for real-time factual tasks and broad knowledge retrieval.
• Fast results on everyday questions. clarifai.com

Best at: Conversational engagement, real-time cultural context, humor style.

• Conversationally fluent and engaging; attracts users for chat experiences with personality.
• Can be less precise than leading models on strict reasoning tasks. Botpress

Best at: Open-source adaptability and custom deployment.

• Strong open-source choice with robust performance across tasks.
• Flexible usage for researchers and developers; supports fine-tuning and self-hosting.
• Latest versions incorporate multimodal capabilities. clarifai.com

Best at: Multilingual support and enterprise deployment at scale.

• Competitive alternative in Asian markets and multilingual scenarios.
• Often used where strong Chinese/English bilingual support and cost efficiency are priorities. clarifai.com

Best at: Efficient reasoning performance and cost-effective inference.

• Noted for strong benchmarks in classification and specific reasoning tasks.
• Can outperform some mainstream models in narrow evaluation categories. arXiv

Best at: Compact reasoning and efficient deployment.

• Known for high performance per compute cost and quick inference.
• Useful when compute is constrained. clarifai.com

Best at: Advanced reasoning and coding among open-source models.

• Competitive open-weight model with long-context ability and strong benchmarks at a fraction of proprietary costs. DataCamp

Best at: Practical, smaller footprint deployments with chain-of-thought support.

• Enables local or private inference without cloud dependency.
• Not as powerful as proprietary flagship models but strong among open weights. DataCamp

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- Best “all-purpose”: GPT-4o/GPT-5 — broad capability across domains.

• Best for controlled, enterprise contexts: Claude 4 — safety and alignment.
• Best factual query/knowledge access: Gemini — search-aware intelligence.
• Best for open-source development: Llama 4, GLM 4.6, GPT-OSS — no licensing costs.
• Best conversational style: Grok 3 — engaging tone. Zapier

If you would like, I can deepen this comparison into a table showing specific benchmark strengths (e.g., reasoning, coding, translation, multimodality), licensing differences (open-source vs proprietary), or typical costs to deploy each model in an enterprise scenario.

User: Yes, please ad that as well.

Yes, please ad that as well.

Assistant: Here is a comprehensive comparison of the Top 10 Large Language Models LLMs in 2026 — including what each is best at ac…

Here is a comprehensive comparison of the Top 10 Large Language Models (LLMs) in 2026 — including what each is best at across key technical categories such as reasoning, coding, translation, multimodal understanding, context handling, and cost/efficiency. Where benchmarks or comparative performance details are publicly available, they are cited. Vellum

Top 10 LLMs in 2026 + What Each Excels At

| Rank | Model | Best Strengths Compared to Others | | ---------------------------------------- | ------------------------------------------------------------------------------------------------------------------------ | --------------------------------- | | 1. GPT-5 / GPT-5.2 (OpenAI) | General intelligence & balance – leading broad reasoning, dynamic task adaptation, code generation, natural language understanding, and robust factuality. Excellent all-purpose performance across benchmarks. LLM Stats | | | 2. Claude 4 / Claude Opus 4 (Anthropic) | Coding & extended reasoning – standout in coding benchmarks and long context handling, with safety-oriented outputs and consistency on multi-step workflows. Tom's Guide +1 | | | 3. Gemini 3 Pro (Google) | Multimodal & factual reasoning – top scores in complex multimodal tasks (text + vision + reasoning), excels in handling real-world search-integrated queries and cross-modal challenges. IT Pro | | | 4. Grok 4 (xAI) | Conversational engagement & benchmark relevance – strong reasoning and coding results; highly interactive and engaging dialogue behavior. DataCamp | | | 5. Llama 4 Series (Meta) | Open-source flexibility & large context – excellent for developers who need customization, self-hosting, long-document analysis, and multilingual generative tasks. Vellum | | | 6. DeepSeek V/R Series | Efficient reasoning & task optimization – competitive reasoning accuracy and cost performance for classification and structured problem solving. Azumo | | | 7. Qwen 3 Family (Alibaba) | Multilingual & enterprise deployment – good for broad language support and scalable deployments in enterprise and marketplace contexts. Vellum | | | 8. Mistral Large 2 | Efficient performance per compute cost – strong inference efficiency, making it practical for lean production environments. Vellum | | | 9. GLM 4.6 (Open-Source) | Open-source reasoning & developer friendliness – strong for community projects; often performs well on specialized reasoning tests after fine-tuning. BentoML | | | 10. GPT-OSS / OpenAI Open-Source lineage | Private/local deployment – good for low-latency local inference and privacy-constrained setups; chain-of-thought support enhances reasoning in modular scenarios. BentoML | |

Detailed Comparative Strengths by Task

Below is a task-focused comparison to help you choose the best model for a specific need:

Reasoning

• Top Performers: Gemini 3 Pro and GPT-5 variants generally lead broad reasoning benchmarks. Gemini scores highly across multimodal reasoning tests; GPT-5 delivers robust performance across math, logic, and complex tasks. DataCamp
• Strong Runner-Ups: Claude Opus 4 excels in extended reasoning with safety constraints. Tom's Guide

Coding & Software Generation

• Leaders: Claude Opus 4 often tops coding benchmarks; GPT-5 is highly capable for real-time coding assistance and IDE workflows. The Verge
• Close Contenders: Grok 4 and Gemini 3 Pro also deliver strong coding outputs in many languages. DataCamp

Multimodal Understanding (Text, Image, Audio)

• Best: Gemini 3 Pro has been reported to outperform other models on multimodal reasoning benchmarks (visual + language). IT Pro
• Others: GPT-5 and Grok 4 also support advanced multimodal inputs, with high performance in combined text + image tasks. DataCamp

Translation & Multilingual Tasks

• Strong: Gemini 3 and Qwen 3 families support broad multilingual capability due to large diversified training corpora. Vellum
• Good: Llama 4 and GLM 4.6 are often chosen for open-source multilingual deployments when customization is required. BentoML

Long Context & Document Processing

• Best: Claude and Llama 4 Series support very long context windows, enabling deep document summarization and analysis of large corpora. Vellum

Cost & Efficient Deployment

• Efficient: Mistral Large 2 and DeepSeek V/R deliver performance that balances speed and cost. Vellum
• Open-Source: GLM 4.6 and GPT-OSS provide cost-efficient models for local or private cloud. BentoML

Summary Guidance: Best Fit by Use Case

| Use Case | Best Model Choice | | ----------------------------- | --------------------- | | All-around performance | GPT-5 / Gemini 3 Pro | | Enterprise reasoning & safety | Claude Opus 4 | | Coding assistance | Claude Opus 4 / GPT-5 | | Multimodal apps | Gemini 3 Pro / GPT-5 | | Custom local deployment | Llama 4 / GPT-OSS | | Cost-efficient inference | Mistral Large 2 |

Notes on Benchmarks & Performance

• Benchmark platforms like LMArena and independent leaderboards show Gemini 3 Pro often scoring top overall on aggregated human preference metrics and multimodal reasoning tests. DataCamp
• Enterprise and safety-critical benchmarks still give Claude and GPT-5 top marks for controlled deployment. Azumo
• Open-source models vary more by customization and fine-tuning than by raw out-of-the-box scores. BentoML

If it would help, I can segment this into a decision table for specific enterprise, developer, or research contexts — including approximate pricing and typical API requirements for each model.