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 across various domains, I’m breaking down the differences between OpenAI’s newly released o3 and o4-mini versus Claude 3.7 Sonnet and Google’s Gemini 2.5 Pro.
If 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.
Understanding Reasoning Models: What Makes Them Special
Reasoning models represent a significant evolution in AI capabilities, employing complex internal processes to tackle intricate problems across diverse domains.
What 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.
In my experience, these reasoning capabilities translate to remarkable improvements in areas like STEM problem-solving, coding, and visual understanding.
- Internal chain-of-thought reasoning (often invisible to the user)
- Enhanced ability to utilize tools to solve problems
- Better performance on complex, multi-step tasks
- Improved accuracy on challenging benchmarks
- More consistent and reliable outputs for technical tasks
Model Specifications and Technical Details
OpenAI o3
OpenAI’s o3 is their most powerful reasoning model to date, excelling across coding, mathematics, science, and visual perception domains.
One 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.
O3 can integrate images directly into its reasoning chain, analyzing and “thinking with” visual content in ways previous models couldn’t.
It has a context window of 200,000 tokens (approximately 150,000 words) and a knowledge cutoff date of June 1, 2024.
OpenAI o4-mini
O4-mini is a smaller, highly optimized model designed for speed and cost-efficiency while maintaining impressively strong reasoning performance.
Like o3, it can agentically use the full suite of ChatGPT tools and effectively deploy them without specific prompting.
It 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.
Anthropic Claude 3.7 Sonnet
Claude 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.
When using Claude’s Extended Thinking mode, the model shows its thinking process, making it more transparent than other reasoning models.
Claude 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.
Google Gemini 2.5 Pro
Gemini 2.5 Pro is Google’s flagship “thinking model,” explicitly designed to reason step-by-step before responding.
What 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.
It’s natively multimodal, handling text, code, image, audio, and video inputs with impressive fluency, and has a knowledge cutoff of January 2025.
Benchmark Performance Analysis
Benchmarks provide a quantitative way to compare these models across various skills, though it’s important to note that benchmark performance doesn’t always translate directly to real-world usefulness.
Comparative Benchmark Tables
The 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.
The 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.
General Reasoning and Knowledge
In general reasoning benchmarks, the picture is mixed and highly dependent on the specific test.
MMLU (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%).
GPQA 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.
On 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%.
SimpleQA, a straightforward factual knowledge retrieval benchmark, is dominated by Gemini 2.5 Pro at 52.9%, highlighting its strong factual grounding capabilities.
Similarly, 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.
Mathematics Performance
In mathematics benchmarks, o4-mini demonstrates surprisingly exceptional performance, especially on competitive math problems.
AIME (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.
The 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 specific competition benchmarks highlighted here.
When 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.
Coding Capabilities
The coding landscape reveals different strengths emerging across various coding benchmarks and real-world applications.
SWE-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%.
For 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.
Aider 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%.
SWE-Lancer measures performance on freelance coding tasks in dollar values, with o3 earning a simulated $65,250 compared to o4-mini’s $56,375.
LiveCodeBench v5 measures real-time coding performance, with Gemini 2.5 Pro achieving 70.4%, though comparative data for OpenAI models isn’t available.
The 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.
Multimodal Understanding
Multimodal capabilities show significant differences between models in how they understand and reason with images, charts, and diagrams.
MMMU 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%.
MathVista tests mathematical problem-solving with visual inputs, where o3 leads with 86.8% accuracy and o4-mini follows at 84.3%.
CharXiv-Reasoning assesses interpretation of scientific figures, with o3 showing remarkable improvement at 75.4% compared to o1’s 55.1%.
Long Context Performance
Long 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.
This aligns with Gemini’s massive 1M+ token context window, far exceeding the 200K windows of o3, o4-mini, and Claude 3.7 Sonnet.
In real-world testing with large documents, Gemini consistently maintained coherence throughout, while other models sometimes lost track of earlier information.
Tool Use and Instruction Following
O3 leads in instruction following with 56.51% accuracy on Scale MultiChallenge, significantly outperforming o1 (44.93%) and o4-mini (42.99%).
For agentic browsing on BrowseComp, o3 achieves 49.7% with tools, far ahead of o4-mini’s 28.3%.
Tau-bench function calling scores show o3 and o1 tied at 70.8% for retail scenarios, with o3 slightly ahead in airline scenarios.
The 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.
Tool Usage and Reasoning Approaches
Agentic Capabilities
OpenAI’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.
One user reported o3 making up to 600 tool calls to solve a complex problem, showing its thoroughness in verification.
Claude 3.7 Sonnet also demonstrates strong agentic capabilities, especially in Extended Thinking mode, enhanced by Claude Code for direct interaction with coding environments.
Gemini 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.
Different Reasoning Approaches
Claude 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.
OpenAI’s o3 and o4-mini employ internal reasoning that remains invisible to the user, with performance scaling with allocated thinking time/compute.
Gemini 2.5 Pro similarly uses internal thinking processes not exposed to the end-user.
Use Case Analysis: Which Model for Which Purpose
OpenAI o3: Best For Complex Multi-Tool Tasks
O3 excels at complex, multi-faceted queries requiring deep analysis across multiple modalities, with its seamless integration of web search, code execution, and image analysis.
It’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.
The downside is its higher cost and occasionally slower processing, with some users reporting responses taking over a minute for complex reasoning.
OpenAI o4-mini: Best For Efficient Technical Tasks
O4-mini offers an exceptional balance of capability and efficiency, ideal for high-volume, reasonably complex tasks where speed and cost are critical factors.
It excels at mathematical problem-solving, routine coding assistance, and technical writing, with performance on math benchmarks making it excellent for quantitative fields.
Many users express surprise at how o4-mini often matches or exceeds larger models on specific tasks while being much faster and more cost-effective.
Claude 3.7 Sonnet: Best For Transparent Reasoning
Claude’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.
Many developers praise Claude for its precision, clear reasoning, and reliability in generating clean, understandable code.
However, this transparency comes with verbosity and slower responses, with some reporting the thinking mode occasionally getting lost in complex tasks.
Gemini 2.5 Pro: Best For Long-Context Analysis
Gemini 2.5 Pro’s massive context window makes it unmatched for tasks involving extremely large documents, extensive codebases, or prolonged conversations.
Users 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.
Its 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.
Pricing Comparison and Cost-Effectiveness
API Pricing Analysis
OpenAI o3 is priced at $10 per million input tokens and $40 per million output tokens, positioning it as a premium model but approximately 25-50% lower cost than its predecessor o1.
OpenAI 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.
Claude 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.
Google 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.
Furthermore, its availability through a free tier via Google AI Studio is a major advantage appreciated by the community.
Value Considerations Beyond Price
Reasoning models often use substantially more tokens than standard models, with Claude’s Extended Thinking potentially using 3-5x more tokens and significantly increasing costs.
Some users report surprisingly high costs when using o1-pro (up to $200 for complex tasks), with concerns that o3-high might have similar implications.
Context 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.
Based 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.
User Experience and Real-World Performance
Coding Experience
In real-world coding scenarios, user sentiment often diverges from benchmark rankings.
Gemini 2.5 Pro earns praise for speed, context handling, and one-shot code generation, though some report occasional bugs or suboptimal code quality.
Claude 3.7 Sonnet is lauded for precision, clear reasoning, and reliable, clean code generation, particularly valuable for debugging complex issues despite occasional verbosity.
Feedback 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.
Model Selection Confusion
Many 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?”
The naming scheme draws criticism, with one user noting “the naming of the models is so bad, it’s insane.”
This confusion is compounded by rapid release cycles, with multiple users noting that recommended models changed completely within weeks.
Visual Processing Capabilities
The 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.
Gemini 2.5 Pro receives praise for its ability to handle video inputs, a feature not available in o3 or o4-mini.
The ability to “think with images” represents a significant advancement many find valuable for professional work involving visual data.
FAQ: Common Questions About AI Reasoning Models
Is OpenAI o3 better than Gemini 2.5 Pro?
The 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.
What are the usage limits for OpenAI o3 and o4-mini models?
With 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.
The 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.
Is o4-mini good for coding?
Yes, 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.
Which AI model has the largest context window?
Gemini 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.
Which AI is best for math problems?
OpenAI’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.
Does o4-mini support image input?
Yes, 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.
Which AI model is most cost-effective for developers?
OpenAI’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 offering good value for transparent reasoning while its Extended Thinking mode should be used selectively due to higher token consumption.
Conclusion: Choosing the Right AI Reasoning Model in 2025
After extensive testing and analysis, it’s clear we’ve entered a new era of AI capabilities, with each model offering distinct advantages for different use cases.
OpenAI o3 excels at complex multi-tool tasks with exceptional multimodal reasoning and tool integration, though at premium pricing.
OpenAI 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.
Claude 3.7 Sonnet’s visible thinking provides unique transparency valuable for educational and collaborative contexts, especially for complex coding tasks.
Gemini 2.5 Pro’s massive context window and balanced performance make it exceptionally versatile for tasks involving large documents or codebases.
As we navigate this evolving landscape, selecting models based on specific needs rather 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.
The 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.
Meta description: Compare OpenAI’s new o3 & o4‑mini with Claude 3.7 Sonnet and Gemini 2.5 Pro to find the perfect AI reasoning model for your needs.
