Oyinbobola Owojori

{"user_id":11659,"source":"https://hostbor.com/o3-vs-o4-mini-vs-claude-vs-gemini/","source_type":"link","title":"New o3 vs o4-mini vs Claude vs Gemini: Which AI is Best Now? - Hostbor - Tech Reviews, Home Labs \u0026 AI Computing Guide","description":"The AI landscape has shifted dramatically in early 2025, with several groundbreaking reasoning models pushing the boundaries of artificial intelligence. After extensively testing these models...","html":"\u003c!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.0 Transitional//EN\" \"http://www.w3.org/TR/REC-html40/loose.dtd\"\u003e\n\u003chtml\u003e\u003cbody\u003e\u003cdiv\u003e\u003cdiv\u003e\n\u003cp\u003eThe AI landscape has shifted dramatically in early 2025, with several groundbreaking reasoning models pushing the boundaries of artificial intelligence.\u003c/p\u003e\n\u003cp\u003eAfter extensively testing these models across various domains, I’m breaking down the differences between OpenAI’s newly released \u003ca href=\"https://openai.com/index/introducing-o3-and-o4-mini/\"\u003eo3 and o4-mini\u003c/a\u003e versus Claude 3.7 Sonnet and Google’s Gemini 2.5 Pro.\u003c/p\u003e\n\u003cp\u003eIf you’re trying to decide which AI model is best for your specific needs, this comparison will guide you through the increasingly complex options available.\u003c/p\u003e\n\u003cdiv\u003e💡\u003cp\u003eThis analysis compares all key aspects of these powerful AI reasoning models, including benchmark performance, practical use cases, and overall value. I’ve personally tested each model extensively to provide insights beyond what benchmarks alone reveal.\u003c/p\u003e\n\u003c/div\u003e Understanding Reasoning Models: What Makes Them Special \u003cp\u003eReasoning models represent a significant evolution in \u003ca href=\"https://hostbor.com/ai-instruments-workflow/\"\u003eAI capabilities\u003c/a\u003e, employing complex internal processes to tackle intricate problems across diverse domains.\u003c/p\u003e\n\u003cp\u003eWhat sets these models apart is their ability to employ step-by-step analysis or “chain of thought” reasoning, approaching problems methodically like a human would.\u003c/p\u003e\n\u003cp\u003eIn my experience, these reasoning capabilities translate to remarkable improvements in areas like STEM problem-solving, coding, and visual understanding.\u003c/p\u003e Internal chain-of-thought reasoning (often invisible to the user)Enhanced ability to utilize tools to solve problemsBetter performance on complex, multi-step tasksImproved accuracy on challenging benchmarksMore consistent and reliable outputs for technical tasks Model Specifications and Technical Details OpenAI o3 \u003cp\u003eOpenAI’s o3 is their most powerful reasoning model to date, excelling across coding, mathematics, science, and visual perception domains.\u003c/p\u003e\n\u003cp\u003eOne of o3’s most impressive features is its agentic use of tools, seamlessly integrating web search, Python execution, file analysis, image generation, and visual reasoning.\u003c/p\u003e\n\u003cp\u003eO3 can integrate images directly into its reasoning chain, analyzing and “thinking with” visual content in ways previous models couldn’t.\u003c/p\u003e\n\u003cp\u003eIt has a context window of 200,000 tokens (approximately 150,000 words) and a knowledge cutoff date of June 1, 2024.\u003c/p\u003e OpenAI o4-mini \u003cp\u003eO4-mini is a smaller, highly optimized model designed for speed and cost-efficiency while maintaining impressively strong reasoning performance.\u003c/p\u003e\n\u003cp\u003eLike o3, it can agentically use the full suite of \u003ca href=\"https://hostbor.com/chatgpt-secrets-ai/\"\u003eChatGPT tools\u003c/a\u003e and effectively deploy them without specific prompting.\u003c/p\u003e\n\u003cp\u003eIt shares the same 200,000 token context window as o3 and the same knowledge cutoff date (June 2024), with the primary difference being speed and cost.\u003c/p\u003e Anthropic Claude 3.7 Sonnet \u003cp\u003eClaude 3.7 Sonnet distinguishes itself as Anthropic’s first “hybrid reasoning” model, operating in either standard mode for fast responses or “Extended Thinking” mode for deeper analysis.\u003c/p\u003e\n\u003cp\u003eWhen using Claude’s Extended Thinking mode, the model shows its thinking process, making it more transparent than other reasoning models.\u003c/p\u003e\n\u003cp\u003eClaude 3.7 Sonnet features a 200,000 token context window, October 2024 knowledge cutoff, and comes with “Claude Code,” a command-line tool for developers.\u003c/p\u003e Google Gemini 2.5 Pro \u003cp\u003eGemini 2.5 Pro is Google’s flagship “thinking model,” explicitly designed to reason step-by-step before responding.\u003c/p\u003e\n\u003cp\u003eWhat sets Gemini 2.5 Pro apart is its massive context window – starting at 1 million tokens with plans for 2 million – a game-changer for tasks involving large codebases or lengthy documents.\u003c/p\u003e\n\u003cp\u003eIt’s natively multimodal, handling text, code, image, audio, and video inputs with impressive fluency, and has a knowledge cutoff of January 2025.\u003c/p\u003e\n\u003cdiv\u003e✔️\u003cp\u003eIn my testing, I found Gemini 2.5 Pro’s context handling to be exceptional. When working with extremely long documents (over 500 pages), it maintained coherence and accuracy throughout the analysis in ways other models simply couldn’t match.\u003c/p\u003e\n\u003c/div\u003e Benchmark Performance Analysis \u003cp\u003eBenchmarks provide a quantitative way to compare these models across various skills, though it’s important to note that \u003ca href=\"https://hostbor.com/asus-rog-flow-z13-2025-review/\"\u003ebenchmark performance\u003c/a\u003e doesn’t always translate directly to real-world usefulness.\u003c/p\u003e Comparative Benchmark Tables \u003cp\u003eThe following tables provide a comprehensive view of how these models perform across different benchmark categories, from general knowledge to specialized tasks like coding and math.\u003c/p\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e\n\u003cdiv\u003e\n\u003cp\u003eAI MODEL PERFORMANCE\u003c/p\u003e\n\u003cp\u003eTOP AI MODELS COMPARISON\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\u003c/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\n\u003cp\u003eMathematics\u003c/p\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\n\u003cp\u003eAIME 2024 Competition Math\u003c/p\u003e\n\u003cp\u003eAccuracy %\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\n\u003cp\u003eOpenAI o4-mini (with python)\u003c/p\u003e\n\u003cp\u003e98.7%\u003c/p\u003e\n\u003c/div\u003e\u003c/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\n\u003cp\u003eOpenAI o3 (with python)\u003c/p\u003e\n\u003cp\u003e95.2%\u003c/p\u003e\n\u003c/div\u003e\u003c/div\u003e\n\u003cdiv\u003e\u003c/div\u003e\n\u003cdiv\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\n\u003cp\u003eAIME 2025 Competition Math\u003c/p\u003e\n\u003cp\u003eAccuracy %\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\n\u003cp\u003eOpenAI o4-mini (with python)\u003c/p\u003e\n\u003cp\u003e99.5%\u003c/p\u003e\n\u003c/div\u003e\u003c/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\n\u003cp\u003eOpenAI o3 (with python)\u003c/p\u003e\n\u003cp\u003e98.4%\u003c/p\u003e\n\u003c/div\u003e\u003c/div\u003e\n\u003cdiv\u003e\u003c/div\u003e\n\u003cdiv\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003cp\u003eGeneral Knowledge \u0026amp; Reasoning\u003c/p\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\n\u003cp\u003eGPQA Diamond PhD-Level Science\u003c/p\u003e\n\u003cp\u003eAccuracy %\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\u003c/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\n\u003cp\u003eOpenAI o3 (no tools)\u003c/p\u003e\n\u003cp\u003e83.3%\u003c/p\u003e\n\u003c/div\u003e\u003c/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\n\u003cp\u003eOpenAI o4-mini (no tools)\u003c/p\u003e\n\u003cp\u003e81.4%\u003c/p\u003e\n\u003c/div\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\n\u003cp\u003eGlobal MMLU (Lite)\u003c/p\u003e\n\u003cp\u003eAccuracy %\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\u003c/div\u003e\n\u003cdiv\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003cp\u003eCoding\u003c/p\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\n\u003cp\u003eSWE-Lancer: IC SWE Diamond Freelance Coding Tasks\u003c/p\u003e\n\u003cp\u003e$ earned\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\u003c/div\u003e\n\u003cdiv\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\n\u003cp\u003eAider Polyglot Code Editing\u003c/p\u003e\n\u003cp\u003eAccuracy % – Whole\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\u003c/div\u003e\n\u003cdiv\u003e\u003c/div\u003e\n\u003cdiv\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e Top Performers By Category \u003cdiv\u003e\n\u003cdiv\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eOpenAI o4-mini\u003c/p\u003e\n\u003cp\u003e\nAIME 2024: 98.7% \nAIME 2025: 99.5%\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eOpenAI o3\u003c/p\u003e\n\u003cp\u003e\nSWE-Bench: 69.1% \nCode Editing: 81.3%\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eGemini 2.5 Pro\u003c/p\u003e\n\u003cp\u003e\nGPQA: 84.0% \nMMLU: 89.8%\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\nAI Models Benchmark Comparison | Visualization created by hostbor\nInteractive visualization highlighting performance data across key AI benchmarks.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eThe table below provides a different perspective on performance, showing category averages across various domains—a more holistic view of each model’s strengths across different skill areas.\u003c/p\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e\n\u003cdiv\u003e\n\u003cp\u003eFLAGSHIP AI MODELS\u003c/p\u003e\n\u003cp\u003ePERFORMANCE COMPARISON\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\u003c/div\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv\u003e\n\u003cp\u003eo3 High\u003c/p\u003e\n\u003cp\u003eo4-Mini High\u003c/p\u003e\n\u003cp\u003eGemini 2.5 Pro\u003c/p\u003e\n\u003cp\u003eo1 High\u003c/p\u003e\n\u003cp\u003eo3 Mini High\u003c/p\u003e\n\u003cp\u003eClaude 3.7 Sonnet\u003c/p\u003e\n\u003cp\u003eGrok 3 Mini Beta\u003c/p\u003e\n\u003cp\u003eDeepSeek R1\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003c/div\u003e\u003c/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cp\u003e\n#2 Overall\u003c/p\u003e\u003c/div\u003e\n\u003cdiv\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cp\u003e\n#3 Overall\u003c/p\u003e\u003c/div\u003e\n\u003cdiv\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003c/div\u003e\u003c/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cp\u003e\n#5 Overall\u003c/p\u003e\u003c/div\u003e\n\u003cdiv\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\n\u003cp\u003eClaude 3.7 Sonnet\u003c/p\u003e\n\u003cp\u003eAnthropic\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\n#6 Overall\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cp\u003e\n#7 Overall\u003c/p\u003e\u003c/div\u003e\n\u003cdiv\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cp\u003e\n#8 Overall\u003c/p\u003e\u003c/div\u003e\n\u003cdiv\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\nAI Model Performance Comparison | Visualization created by hostbor\nInteractive visualization comparing flagship AI models across key performance metrics | Source: Livebench\u003c/p\u003e\n\u003c/div\u003e General Reasoning and Knowledge \u003cp\u003eIn general reasoning benchmarks, the picture is mixed and highly dependent on the specific test.\u003c/p\u003e\n\u003cp\u003eMMLU (Massive Multitask Language Understanding) tests broad knowledge across 57 subjects using multiple-choice questions, with Gemini 2.5 Pro (89.8%) and o3 (88.8%) slightly outperforming Claude 3.7 Sonnet (88.3%) and o4-mini (85.2%).\u003c/p\u003e\n\u003cp\u003eGPQA Diamond evaluates PhD-level scientific reasoning in fields like physics and chemistry, where Gemini 2.5 Pro leads at 84.0%, closely followed by o3 at 83.3%, with Claude 3.7 in Extended Thinking mode reaching 84.8% with enhanced evaluation techniques.\u003c/p\u003e\n\u003cp\u003eOn Humanity’s Last Exam (HLE), which tests frontier knowledge across numerous disciplines, o3 outperforms competitors with 20.32% without tools and up to 26.6% with Deep Research capabilities, compared to Gemini’s 18.8% and Claude’s 8.9%.\u003c/p\u003e\n\u003cp\u003eSimpleQA, a straightforward factual knowledge retrieval benchmark, is dominated by Gemini 2.5 Pro at 52.9%, highlighting its strong factual grounding capabilities.\u003c/p\u003e\n\u003cp\u003eSimilarly, Vibe-Eval (Reka) measures a model’s stylistic coherence and contextual appropriateness, with Gemini 2.5 Pro achieving 69.4%, though comparative data for other models isn’t available.\u003c/p\u003e Mathematics Performance \u003cp\u003eIn mathematics benchmarks, o4-mini demonstrates surprisingly exceptional performance, especially on competitive math problems.\u003c/p\u003e\n\u003cp\u003eAIME (American Invitational Mathematics Examination) features challenging high-school competition math problems, where o4-mini leads with 93.4% accuracy on AIME 2024 and 92.7% on AIME 2025 without tools (rising to 98.7% and 99.5% respectively with Python), ahead of both Gemini 2.5 Pro and o3.\u003c/p\u003e\n\u003cp\u003eThe Mathematics Average column in the category breakdown shows Gemini 2.5 Pro actually leading with 89.16%, followed by o4-Mini High at 84.90% and o3 High at 84.67%, indicating Gemini may perform better across a broader range of mathematical tasks beyond the \u003ca href=\"https://hostbor.com/rtx-5090-vs-4090-comparison/\"\u003especific competition\u003c/a\u003e benchmarks highlighted here.\u003c/p\u003e\n\u003cp\u003eWhen these models are allowed to use tools, particularly Python for computation, their performance improves dramatically, with tool-enhanced scores approaching perfect solutions in many cases.\u003c/p\u003e Coding Capabilities \u003cp\u003eThe coding landscape reveals different strengths emerging across various coding benchmarks and real-world applications.\u003c/p\u003e\n\u003cp\u003eSWE-bench Verified, which tests software engineering abilities by resolving real GitHub issues, shows o3 leading at 69.1%, closely followed by o4-mini at 68.1%, with Claude 3.7 Sonnet achieving 70.3% with High Compute/Scaffold and Gemini at 63.8%.\u003c/p\u003e\n\u003cp\u003eFor competitive programming measured by Codeforces, o4-mini slightly edges out o3 with Elo ratings of 2719 and 2706 respectively, a massive improvement over previous models.\u003c/p\u003e\n\u003cp\u003eAider Polyglot evaluates code editing across multiple languages, with o3-high significantly outperforming others at 81.3%/79.6% accuracy, followed by Gemini 2.5 Pro at 74.0%/68.6%.\u003c/p\u003e\n\u003cp\u003eSWE-Lancer measures performance on freelance coding tasks in dollar values, with o3 earning a simulated $65,250 compared to o4-mini’s $56,375.\u003c/p\u003e\n\u003cp\u003eLiveCodeBench v5 measures real-time coding performance, with Gemini 2.5 Pro achieving 70.4%, though comparative data for OpenAI models isn’t available.\u003c/p\u003e\n\u003cp\u003eThe Coding Average column shows o4-Mini High actually leading with 74.33%, followed by o3 High at 73.33%, with Gemini 2.5 Pro significantly behind at 58.09% – indicating that while Gemini performs well on certain coding benchmarks, it may be less consistent across the full spectrum of coding tasks.\u003c/p\u003e\n\u003cdiv\u003e💪\u003cp\u003eWhen working with o3 on coding projects, I found it particularly shines with large, complex codebases. Its ability to understand project structure, identify bugs, and suggest improvements across multiple files makes it invaluable for professional development work.\u003c/p\u003e\n\u003c/div\u003e Multimodal Understanding \u003cp\u003eMultimodal capabilities show significant \u003ca href=\"https://hostbor.com/rtx-5070-vs-4070s-comparison/\"\u003edifferences between\u003c/a\u003e models in how they understand and reason with images, charts, and diagrams.\u003c/p\u003e\n\u003cp\u003eMMMU evaluates understanding across text and images in college-level problems, with o3 leading at 82.9%, followed closely by Gemini 2.5 Pro (81.7%) and o4-mini (81.6%), with Claude 3.7 Sonnet at 75.0%.\u003c/p\u003e\n\u003cp\u003eMathVista tests mathematical problem-solving with visual inputs, where o3 leads with 86.8% accuracy and o4-mini follows at 84.3%.\u003c/p\u003e\n\u003cp\u003eCharXiv-Reasoning assesses interpretation of scientific figures, with o3 showing remarkable improvement at 75.4% compared to o1’s 55.1%.\u003c/p\u003e Long Context Performance \u003cp\u003eLong context handling shows clear differences, with Gemini 2.5 Pro demonstrating exceptional performance on the MRCR benchmark with 94.5% accuracy at 128k context and 83.1% at 1M context.\u003c/p\u003e\n\u003cp\u003eThis aligns with Gemini’s massive 1M+ token context window, far exceeding the 200K windows of o3, o4-mini, and \u003ca href=\"https://hostbor.com/claude-ai-max-plan-explained/\"\u003eClaude 3.7 Sonnet\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIn real-world testing with large documents, Gemini consistently maintained coherence throughout, while other models sometimes lost track of earlier information.\u003c/p\u003e Tool Use and Instruction Following \u003cp\u003eO3 leads in instruction following with 56.51% accuracy on Scale MultiChallenge, significantly outperforming o1 (44.93%) and o4-mini (42.99%).\u003c/p\u003e\n\u003cp\u003eFor agentic browsing on BrowseComp, o3 achieves 49.7% with tools, far ahead of o4-mini’s 28.3%.\u003c/p\u003e\n\u003cp\u003eTau-bench function calling scores show o3 and o1 tied at 70.8% for retail scenarios, with o3 slightly ahead in airline scenarios.\u003c/p\u003e\n\u003cp\u003eThe Instruction Following (IF) Average column shows o3, o4-mini, and o1 all scoring well above 80%, with o3 High leading at 86.17%, indicating strong overall performance in following detailed instructions.\u003c/p\u003e Tool Usage and Reasoning Approaches Agentic Capabilities \u003cp\u003eOpenAI’s o3 and o4-mini are explicitly designed for agentic tool use, combining web search, Python execution, file analysis, image generation, and more within a single reasoning process.\u003c/p\u003e\n\u003cp\u003eOne user reported o3 making up to 600 tool calls to solve a complex problem, showing its thoroughness in verification.\u003c/p\u003e\n\u003cp\u003eClaude 3.7 Sonnet also demonstrates strong agentic capabilities, especially in Extended Thinking mode, enhanced by \u003ca href=\"https://docs.anthropic.com/en/docs/agents-and-tools/claude-code/overview\"\u003eClaude Code\u003c/a\u003e for direct interaction with coding environments.\u003c/p\u003e\n\u003cp\u003eGemini 2.5 Pro supports tools including search, code execution, and function calling, though some users report its tool usage can be less reliable in certain integrations.\u003c/p\u003e Different Reasoning Approaches \u003cp\u003eClaude 3.7 Sonnet uniquely offers visible thinking, with the extended thinking process made transparent to the user, valuable for understanding complex solutions but sometimes overly verbose.\u003c/p\u003e\n\u003cp\u003eOpenAI’s o3 and o4-mini employ internal reasoning that remains invisible to the user, with performance scaling with allocated thinking time/compute.\u003c/p\u003e\n\u003cp\u003eGemini 2.5 Pro similarly uses internal thinking processes not exposed to the end-user.\u003c/p\u003e\n\u003cdiv\u003e❌\u003cp\u003eReasoning models significantly increase token consumption and processing times. Claude’s Extended Thinking can use 5-10x more tokens than standard mode, with some users reporting unexpectedly high costs for o3 on complex tasks.\u003c/p\u003e\n\u003c/div\u003e Use Case Analysis: Which Model for Which Purpose \u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e\n\u003cdiv\u003e\n\u003cp\u003eAI MODEL SELECTOR\u003c/p\u003e\n\u003cp\u003eMATCHING MODELS TO SPECIFIC USE CASES\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\u003c/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e Finding Your Ideal AI Model \u003cp\u003eThese cutting-edge AI reasoning models excel in different areas. Understanding their unique strengths can help you select the perfect model for your specific needs, ensuring optimal performance and cost-effectiveness for your particular use case.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv\u003e\u003cdiv\u003e OpenAI o3 \u003cp\u003e\nMulti-Tool Integration\nVisual Reasoning\u003c/p\u003e\n\u003c/div\u003e\u003c/div\u003e\n\u003cp\u003eOpenAI’s most advanced reasoning model, designed to seamlessly integrate web search, code execution, and image analysis with exceptional multimodal reasoning capabilities.\u003c/p\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\nResearch tasks with diverse information sources\u003c/div\u003e\n\u003cdiv\u003e\nTechnical analysis with visual components\u003c/div\u003e\n\u003cdiv\u003e\nComplex coding requiring both implementation and explanation\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\nBest for Complex Multi-Tool Tasks\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv\u003e\u003cdiv\u003e OpenAI o4-mini \u003cp\u003e\nCost-Efficient\nFast Processing\u003c/p\u003e\n\u003c/div\u003e\u003c/div\u003e\n\u003cp\u003eA smaller, highly optimized reasoning model that balances advanced capabilities with efficiency, ideal for high-volume tasks where speed and cost are critical factors.\u003c/p\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\nMathematical problem-solving (99.5% AIME accuracy)\u003c/div\u003e\n\u003cdiv\u003e\nRoutine coding assistance (68.1% SWE-bench)\u003c/div\u003e\n\u003cdiv\u003e\nHigh-volume technical writing and analysis\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\nBest for Efficient Technical Tasks\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv\u003e\u003cdiv\u003e Claude 3.7 Sonnet \u003cp\u003e\nTransparent Reasoning\nExtended Thinking\u003c/p\u003e\n\u003c/div\u003e\u003c/div\u003e\n\u003cp\u003eAnthropic’s hybrid reasoning model with visible thinking process, making it ideal for tasks where understanding the reasoning is as important as the final answer.\u003c/p\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\nEducational contexts requiring step-by-step explanations\u003c/div\u003e\n\u003cdiv\u003e\nComplex problem decomposition for collaborative work\u003c/div\u003e\n\u003cdiv\u003e\nSoftware development with clean, documented code (70.3% SWE-bench)\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\nBest for Transparent Reasoning\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv\u003e\u003cdiv\u003e Gemini 2.5 Pro \u003cp\u003e\n1M+ Context Window\nMultimodal\u003c/p\u003e\n\u003c/div\u003e\u003c/div\u003e\n\u003cp\u003eGoogle’s flagship model with an unmatched 1M+ token context window, exceptional for tasks involving extremely large documents, extensive codebases, or prolonged conversations.\u003c/p\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\nAnalyzing lengthy research papers (94.5% MRCR)\u003c/div\u003e\n\u003cdiv\u003e\nReviewing large codebases across multiple files\u003c/div\u003e\n\u003cdiv\u003e\nMaintaining context in extended problem-solving sessions\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\nBest for Long-Context Analysis\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003cp\u003eKey Selection Guide:\u003c/p\u003e\n\u003cp\u003e\nSelect your AI model based on specific needs rather than general rankings. Choose o3 for complex multimodal research, o4-mini for cost-effective technical tasks, Claude 3.7 Sonnet for transparent reasoning processes, and Gemini 2.5 Pro for extensive document analysis or large codebases.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\nAI reasoning model selector comparison | Visualization created by hostbor\nModel capability assessment for determining which AI reasoning system is best matched to specific use cases and technical requirements.\u003c/p\u003e\n\u003c/div\u003e OpenAI o3: Best For Complex Multi-Tool Tasks \u003cp\u003eO3 excels at complex, multi-faceted queries requiring deep analysis across multiple modalities, with its seamless integration of web search, \u003ca href=\"https://hostbor.com/mailcow-servers-in-sync/\"\u003ecode execution\u003c/a\u003e, and image analysis.\u003c/p\u003e\n\u003cp\u003eIt’s particularly effective for research tasks integrating diverse information sources, technical problem-solving with visual and textual analysis, and coding projects requiring both implementation and explanation.\u003c/p\u003e\n\u003cp\u003eThe downside is its higher cost and occasionally slower processing, with some users reporting responses taking over a minute for complex reasoning.\u003c/p\u003e OpenAI o4-mini: Best For Efficient Technical Tasks \u003cp\u003eO4-mini offers an exceptional balance of capability and efficiency, ideal for high-volume, reasonably complex tasks where speed and cost are critical factors.\u003c/p\u003e\n\u003cp\u003eIt excels at mathematical problem-solving, routine coding assistance, and technical writing, with performance on math benchmarks making it excellent for quantitative fields.\u003c/p\u003e\n\u003cp\u003eMany users express surprise at how o4-mini often matches or exceeds larger models on specific tasks while being much faster and more cost-effective.\u003c/p\u003e Claude 3.7 Sonnet: Best For Transparent Reasoning \u003cp\u003eClaude’s hybrid approach with visible thinking makes it ideal for tasks where understanding the reasoning process is as important as the final answer, particularly valuable for educational contexts and collaborative coding.\u003c/p\u003e\n\u003cp\u003eMany developers praise Claude for its precision, clear reasoning, and reliability in generating clean, understandable code.\u003c/p\u003e\n\u003cp\u003eHowever, this transparency comes with verbosity and slower responses, with some reporting the thinking mode occasionally getting lost in complex tasks.\u003c/p\u003e Gemini 2.5 Pro: Best For Long-Context Analysis \u003cp\u003eGemini 2.5 Pro’s massive context window makes it unmatched for tasks involving extremely large documents, extensive codebases, or prolonged conversations.\u003c/p\u003e\n\u003cp\u003eUsers frequently cite its speed, context handling, and ability to generate complex working code in one shot, with some developers noting it can fix issues that stumped other models due to its context capabilities.\u003c/p\u003e\n\u003cp\u003eIts balanced performance across domains, combined with its exceptional context handling, makes it an excellent general-purpose reasoning model despite not always leading in specific benchmarks.\u003c/p\u003e\n\u003cdiv\u003e✔️\u003cp\u003eGemini 2.5 Pro’s ability to handle massive context windows makes it particularly valuable for developers working with large codebases. When analyzing a project with over 20,000 lines of code across multiple files, it maintained a coherent understanding of the architecture in ways that other models simply couldn’t match due to context limitations.\u003c/p\u003e\n\u003c/div\u003e Pricing Comparison and Cost-Effectiveness API Pricing Analysis \u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e\n\u003cdiv\u003e\n\u003cp\u003eAPI PRICING COMPARISON\u003c/p\u003e\n\u003cp\u003eCOST ANALYSIS OF LEADING AI REASONING MODELS\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\u003c/div\u003e\n\u003cdiv\u003e Input Token Pricing ($ per Million Tokens) \u003cdiv\u003e\u003cp\u003eOpenAI o3\u003c/p\u003e\u003c/div\u003e\n\u003cdiv\u003e\u003cp\u003eClaude 3.7 Sonnet\u003c/p\u003e\u003c/div\u003e\n\u003cdiv\u003e\n\u003cp\u003eGemini 2.5 Pro\u003c/p\u003e\nFREE TIER\u003c/div\u003e\n\u003cdiv\u003e\u003cp\u003eOpenAI o4-mini\u003c/p\u003e\u003c/div\u003e Output Token Pricing ($ per Million Tokens) \u003cdiv\u003e\u003cp\u003eOpenAI o3\u003c/p\u003e\u003c/div\u003e\n\u003cdiv\u003e\u003cp\u003eClaude 3.7 Sonnet\u003c/p\u003e\u003c/div\u003e\n\u003cdiv\u003e\n\u003cp\u003eGemini 2.5 Pro\u003c/p\u003e\nFREE TIER\u003c/div\u003e\n\u003cdiv\u003e\u003cp\u003eOpenAI o4-mini\u003c/p\u003e\u003c/div\u003e\n\u003cdiv\u003e Key Pricing Insights \u003cdiv\u003e\n\u003cp\u003eGemini 2.5 Pro offers exceptional value with its free tier access and competitive API pricing, making advanced AI accessible to developers on all budgets.\u003c/p\u003e\n\u003cp\u003eO4-mini delivers the best price-to-performance ratio among OpenAI models, costing 90% less than o3 while maintaining strong capabilities in mathematics and coding tasks.\u003c/p\u003e\n\u003cp\u003eConsider the total cost of operation rather than just base rates – factors like token consumption for complex reasoning, context window usage, and extended thinking modes can significantly impact actual expenses.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\n2025 AI Model Pricing Comparison | Visualization created by hostbor\nData sourced from official API pricing documentation and developer community feedback\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eOpenAI o3 is priced at $10 per million input tokens and $40 per million output tokens, positioning it as a \u003ca href=\"https://hostbor.com/razer-blade-16-2025-rtx-5090/\"\u003epremium model\u003c/a\u003e but approximately 25-50% lower cost than its predecessor o1.\u003c/p\u003e\n\u003cp\u003eOpenAI o4-mini offers significantly better value at $1.10 per million input tokens and $4.40 per million output tokens, a 90% reduction compared to o3.\u003c/p\u003e\n\u003cp\u003eClaude 3.7 Sonnet is priced at $3 per million input tokens and $15 per million output tokens (including thinking tokens), positioning it between o3 and o4-mini.\u003c/p\u003e\n\u003cp\u003eGoogle Gemini 2.5 Pro’s API is priced competitively around $1.25/M input and $10/M output tokens (standard usage), making it substantially cheaper than o3 as noted by users.\u003c/p\u003e\n\u003cp\u003eFurthermore, its availability through a free tier via Google AI Studio is a major advantage appreciated by the community.\u003c/p\u003e Value Considerations Beyond Price \u003cp\u003eReasoning models often use substantially more tokens than standard models, with Claude’s Extended Thinking potentially using 3-5x more tokens and significantly increasing costs.\u003c/p\u003e\n\u003cp\u003eSome users report surprisingly high costs when using o1-pro (up to $200 for complex tasks), with concerns that o3-high might have similar implications.\u003c/p\u003e\n\u003cp\u003eContext window efficiency also factors in—Gemini’s massive window enables solving problems with fewer back-and-forth exchanges, potentially reducing total token usage for document-heavy tasks.\u003c/p\u003e\n\u003cp\u003eBased on comparative analysis, o4-mini offers the best overall value for most technical tasks, while Gemini 2.5 Pro excels for tasks requiring extensive context handling.\u003c/p\u003e User Experience and Real-World Performance Coding Experience \u003cp\u003eIn real-world coding scenarios, user sentiment often diverges from benchmark rankings.\u003c/p\u003e\n\u003cp\u003eGemini 2.5 Pro earns praise for speed, context handling, and one-shot code generation, though some report occasional bugs or suboptimal code quality.\u003c/p\u003e\n\u003cp\u003eClaude 3.7 Sonnet is lauded for precision, clear reasoning, and reliable, clean code generation, particularly valuable for debugging complex issues despite occasional verbosity.\u003c/p\u003e\n\u003cp\u003eFeedback on o3 and o4-mini is mixed, with some reporting occasional slowness or usability issues in agentic modes while others are impressed with how o4-mini-high can anticipate coding contexts and generate error-free code.\u003c/p\u003e Model Selection Confusion \u003cp\u003eMany users express frustration with the proliferation of model options and unclear naming conventions, with comments like “there are like 13 models now, when am I supposed to use each one?”\u003c/p\u003e\n\u003cp\u003eThe naming scheme draws criticism, with one user noting “the naming of the models is so bad, it’s insane.”\u003c/p\u003e\n\u003cp\u003eThis confusion is compounded by rapid release cycles, with multiple users noting that recommended models changed completely within weeks.\u003c/p\u003e Visual Processing Capabilities \u003cp\u003eThe enhanced visual reasoning of these models impresses many users, particularly o3 and o4-mini’s ability to transform and analyze images by zooming, cropping, or enhancing text in photographs.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://blog.google/technology/google-deepmind/gemini-model-thinking-updates-march-2025/\"\u003eGemini 2.5 Pro\u003c/a\u003e receives praise for its ability to handle video inputs, a feature not available in o3 or o4-mini.\u003c/p\u003e\n\u003cp\u003eThe ability to “think with images” represents a significant advancement many find valuable for professional work involving visual data.\u003c/p\u003e FAQ: Common Questions About AI Reasoning Models Is OpenAI o3 better than Gemini 2.5 Pro? \u003cp\u003eThe comparison isn’t straightforward—o3 leads on visual reasoning (MMMU, MathVista) and software engineering (SWE-bench), while Gemini 2.5 Pro excels in long-context tasks with its 1M token window and leads on GPQA Diamond, with o3 offering superior integrated tool use while Gemini provides better value, making the “better” choice entirely dependent on your specific use case.\u003c/p\u003e What are the usage limits for OpenAI o3 and o4-mini models? \u003cp\u003eWith a ChatGPT Plus subscription, you can access OpenAI o3 for 50 messages per week, o4-mini for 150 messages per day, and o4-mini-high for 50 messages per day.\u003c/p\u003e\n\u003cp\u003eThe ChatGPT Pro plan offers near unlimited access to these reasoning models, making it ideal for users who need extensive AI interaction for their projects or daily work.\u003c/p\u003e Is o4-mini good for coding? \u003cp\u003eYes, o4-mini demonstrates excellent coding capabilities, particularly for algorithmic and mathematical programming tasks, scoring 68.1% on SWE-bench Verified and achieving an impressive 2719 Elo rating on Codeforces, delivering strong coding support at significantly lower cost than o3 and receiving praise from developers for handling both routine tasks and complex problem-solving with impressive accuracy.\u003c/p\u003e Which AI model has the largest context window? \u003cp\u003eGemini 2.5 Pro has the largest context window, starting at 1 million tokens with plans for 2 million, far exceeding the 200,000 token windows of OpenAI’s o3/o4-mini and Claude 3.7 Sonnet, making it uniquely suited for analyzing very large documents, codebases, or maintaining coherence in extremely lengthy conversations.\u003c/p\u003e Which AI is best for math problems? \u003cp\u003eOpenAI’s o4-mini demonstrates the strongest performance on competitive mathematics benchmarks, achieving an extraordinary 93.4% accuracy on AIME 2024 and 92.7% on AIME 2025 without tools (rising to 98.7% and 99.5% respectively with Python), significantly outperforming other models and making it the clear leader for advanced mathematical tasks.\u003c/p\u003e Does o4-mini support image input? \u003cp\u003eYes, o4-mini supports image input and demonstrates strong multimodal reasoning capabilities, “thinking with images” by integrating visual content directly into its reasoning chain, analyzing charts, diagrams, photos, and other visual inputs, and manipulating images through tools including cropping, zooming, and rotation to extract information.\u003c/p\u003e Which AI model is most cost-effective for developers? \u003cp\u003eOpenAI’s o4-mini typically offers the best balance of capability and affordability for developers at $1.10/4.40 per million input/output tokens, while Gemini 2.5 Pro provides exceptional value for large projects due to its context window and free tier, with Claude 3.7 Sonnet’s standard mode \u003ca href=\"https://hostbor.com/minisforum-bd790i-x3d-review/\"\u003eoffering good value\u003c/a\u003e for transparent reasoning while its Extended Thinking mode should be used selectively due to higher token consumption.\u003c/p\u003e Conclusion: Choosing the Right AI Reasoning Model in 2025 \u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e\n\u003cdiv\u003e\n\u003cp\u003eFINAL ASSESSMENT\u003c/p\u003e\n\u003cp\u003eAI REASONING MODELS COMPARISON\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\u003c/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e\n\u003cp\u003e\n2025\nState-of-the-Art\n\u003c/p\u003e\n\u003cdiv\u003e Model Performance Overview \u003cdiv\u003e\u003cp\u003e\nOpenAI o3\nPremium Multi-Tool\u003c/p\u003e\u003c/div\u003e\n\u003cdiv\u003e\u003cp\u003e\nOpenAI o4-mini\nValue Leader\u003c/p\u003e\u003c/div\u003e\n\u003cdiv\u003e\u003cp\u003e\nClaude 3.7 Sonnet\nTransparency King\u003c/p\u003e\u003c/div\u003e\n\u003cdiv\u003e\u003cp\u003e\nGemini 2.5 Pro\nContext Champion\u003c/p\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e\u003c/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\n\u003cp\u003e\u003c/p\u003e Overall Standouts o4-mini’s exceptional math performance (99.5% AIME with Python)Gemini 2.5 Pro’s unmatched 1M+ token context windowo3’s leadership in visual reasoning and multi-tool integrationClaude 3.7’s transparent thinking process for complex tasksDramatic improvements across all models in tool usage \u003c/div\u003e\n\u003cdiv\u003e\n\u003cp\u003e\u003c/p\u003e Future Outlook Rapid pace of development continues at unprecedented speedTool integration becoming the defining feature of reasoning modelsContext window size becoming a key competitive advantagePrice-to-performance ratio increasingly important for adoptionBenchmarks increasingly saturated, new ones needed \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e Key Selection Guide \u003cdiv\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\n\u003cp\u003eChoose OpenAI o3\u003c/p\u003e\n\u003cp\u003eFor complex multi-modal research requiring deep analysis across text, code, and images\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\n\u003cp\u003eChoose OpenAI o4-mini\u003c/p\u003e\n\u003cp\u003eFor cost-effective technical tasks with outstanding math and coding performance\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\n\u003cp\u003eChoose Claude 3.7 Sonnet\u003c/p\u003e\n\u003cp\u003eFor transparent reasoning processes in educational contexts and collaborative coding\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\n\u003cp\u003eChoose Gemini 2.5 Pro\u003c/p\u003e\n\u003cp\u003eFor handling extremely large documents, codebases, or maintaining context in lengthy conversations\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003cp\u003eFinal Thought:\u003c/p\u003e\n\u003cp\u003e\nAfter extensive testing and analysis, it’s clear we’ve entered a new era of AI capabilities. These four models represent the current state-of-the-art, each pushing the boundaries of what artificial intelligence can accomplish in different ways. As the landscape continues to evolve rapidly, selecting models based on specific needs rather than general rankings will deliver the best results for your particular use cases.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\nAI reasoning models final assessment | Visualization created by hostbor\nComparative analysis of leading AI reasoning models highlighting their respective strengths, optimal use cases, and outlook for the future of intelligent systems.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eAfter extensive testing and analysis, it’s clear we’ve \u003ca href=\"https://hostbor.com/will-ai-eliminate-coders/\"\u003eentered a new era\u003c/a\u003e of AI capabilities, with each model offering distinct advantages for different use cases.\u003c/p\u003e\n\u003cp\u003eOpenAI o3 excels at complex multi-tool tasks with exceptional multimodal reasoning and tool integration, though at premium pricing.\u003c/p\u003e\n\u003cp\u003eOpenAI o4-mini delivers remarkable performance at a fraction of the cost, particularly in mathematics and coding, representing the best value proposition for most technical users.\u003c/p\u003e\n\u003cp\u003eClaude 3.7 Sonnet’s visible thinking provides unique transparency valuable for educational and collaborative contexts, especially for complex coding tasks.\u003c/p\u003e\n\u003cp\u003eGemini 2.5 Pro’s massive context window and balanced performance make it exceptionally versatile for tasks involving large documents or codebases.\u003c/p\u003e\n\u003cp\u003eAs we navigate this evolving landscape, selecting models based on \u003ca href=\"https://hostbor.com/minisforum-ai-x1-pro-review/\"\u003especific needs \u003c/a\u003erather than general rankings is most prudent—o3 for complex multimodal analysis, o4-mini for cost-effective technical tasks, Claude for transparent reasoning, and Gemini for extensive context handling.\u003c/p\u003e\n\u003cp\u003eThe future belongs to models that can think, reason, and deploy tools effectively—these four reasoning models offer a compelling glimpse of where AI is headed.\u003c/p\u003e\n\u003cp\u003eMeta description: Compare OpenAI’s new o3 \u0026amp; o4‑mini with Claude 3.7 Sonnet and Gemini 2.5 Pro to find the perfect AI reasoning model for your needs.\u003c/p\u003e\n\u003c/div\u003e\u003c/div\u003e\u003c/body\u003e\u003c/html\u003e\n","hash_id":"Cq71HvsYFreI","author":"Otabek Djuraev","reading_time":"1 min read","link_sharing":true,"archived":false,"favourited":false,"folder_id":16623,"tag_list":[]}