Editing Openai/696a677f-b378-800c-b260-be955277fa54 (section)

==== 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<ref>{{cite web|title=DataCamp|url=https://www.datacamp.com/tutorial/llm-benchmarks|publisher=datacamp.com|access-date=2026-01-17}}</ref>
* Strong Runner-Ups: Claude Opus 4 excels in extended reasoning with safety constraints. Tom's Guide<ref>{{cite web|title=Tom's Guide|url=https://www.tomsguide.com/ai/what-is-claude-everything-you-need-to-know-about-anthropics-ai-powerhouse|publisher=Tom's Guide|access-date=2026-01-17}}</ref>

===== 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<ref>{{cite web|title=The Verge|url=https://www.theverge.com/news/672705/anthropic-claude-4-ai-ous-sonnet-availability|publisher=The Verge|access-date=2026-01-17}}</ref>
* Close Contenders: Grok 4 and Gemini 3 Pro also deliver strong coding outputs in many languages. DataCamp<ref>{{cite web|title=DataCamp|url=https://www.datacamp.com/tutorial/llm-benchmarks|publisher=datacamp.com|access-date=2026-01-17}}</ref>

===== Multimodal Understanding (Text, Image, Audio) =====
* Best: Gemini 3 Pro has been reported to outperform other models on multimodal reasoning benchmarks (visual + language). IT Pro<ref>{{cite web|title=IT Pro|url=https://www.itpro.com/technology/artificial-intelligence/google-launches-flagship-gemini-3-model-and-google-antigravity-a-new-agentic-ai-development-platform|publisher=IT Pro|access-date=2026-01-17}}</ref>
* Others: GPT-5 and Grok 4 also support advanced multimodal inputs, with high performance in combined text + image tasks. DataCamp<ref>{{cite web|title=DataCamp|url=https://www.datacamp.com/tutorial/llm-benchmarks|publisher=datacamp.com|access-date=2026-01-17}}</ref>

===== Translation & Multilingual Tasks =====
* Strong: Gemini 3 and Qwen 3 families support broad multilingual capability due to large diversified training corpora. Vellum<ref>{{cite web|title=Vellum|url=https://www.vellum.ai/llm-leaderboard|publisher=vellum.ai|access-date=2026-01-17}}</ref>
* Good: Llama 4 and GLM 4.6 are often chosen for open-source multilingual deployments when customization is required. BentoML<ref>{{cite web|title=BentoML|url=https://www.bentoml.com/blog/navigating-the-world-of-open-source-large-language-models|publisher=bentoml.com|access-date=2026-01-17}}</ref>

===== 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<ref>{{cite web|title=Vellum|url=https://www.vellum.ai/llm-leaderboard|publisher=vellum.ai|access-date=2026-01-17}}</ref>

===== Cost & Efficient Deployment =====
* Efficient: Mistral Large 2 and DeepSeek V/R deliver performance that balances speed and cost. Vellum<ref>{{cite web|title=Vellum|url=https://www.vellum.ai/llm-leaderboard|publisher=vellum.ai|access-date=2026-01-17}}</ref>
* Open-Source: GLM 4.6 and GPT-OSS provide cost-efficient models for local or private cloud. BentoML<ref>{{cite web|title=BentoML|url=https://www.bentoml.com/blog/navigating-the-world-of-open-source-large-language-models|publisher=bentoml.com|access-date=2026-01-17}}</ref>