Introduction

We’re taking a close look at five leading AI models—Claude 3.7 Sonnet, Claude 3.5 Sonnet, OpenAI o3-mini, DeepSeek R1, and Grok 3 Beta—to understand their capabilities across math, coding, and advanced reasoning tasks. 

Our aim is twofold: first, to show how each model performs under standardized benchmarks, and second, to highlight the trade-offs in cost, context window size, and reliability that might influence real-world adoption. With concrete data and direct comparisons, we hope readers can make more informed decisions about which model best suits their specific needs.

Below, we dive into each benchmark and examine how these models stack up, drawing on official performance metrics for a detailed and practical perspective.

Experimental Setup

In order to compare these models, we employed a suite of benchmarks that collectively measure a range of skills: mathematical problem-solving, advanced reasoning, software engineering, and the ability to interact with external systems (often called “agentic tool use”). Below, we describe each benchmark briefly, along with the rationale for including it in our evaluation.

AIME (High School Math Competition)

AIME tests the ability to solve competition-style math problems. The difficulty often exceeds what standard high school math courses cover, making it a good stress test for how well a model can handle non-routine, creative problem-solving.

MATH-500

In addition to AIME, we used a broader set of 500 math problems that focus on conceptual clarity, computational accuracy, and step-by-step reasoning. Unlike AIME, these problems tend to be more varied in their difficulty and domain coverage.

GPQA Diamond (Graduate-Level Reasoning)

GPQA are a challenging set of questions designed to probe whether a model can reason across multiple disciplines at a level comparable to graduate coursework. The topics can range from abstract algebra to philosophy, testing both the breadth and depth of a model’s understanding.

SWE-bench Verified (Software Engineering Tasks)

SWE-bench⁠—a benchmark for evaluating large language models’ (LLMs’) abilities to solve real-world software issues sourced from GitHub. A model's ability to produce, debug, and refactor code. A “pass@1” score is often used to indicate the likelihood that the first proposed solution is correct. We also consider how well the models can handle multi-step coding tasks, from clarifying requirements to implementing features.

TAU-bench (Agentic Tool Use)

(cc: sierra)

TAU-bench is a set of scenarios that measure how effectively a model can carry out tasks that require integration with external systems, such as managing retail inventory or booking airline tickets. The tasks are evaluated in terms of completion accuracy and the model’s ability to adapt when additional context or constraints are introduced.

We administered these benchmarks to each model under conditions intended to mimic real-world usage. 

For instance, coding tasks were given in a format that resembled typical software development queries. Math problems were presented in ways that tested not only correctness but also the clarity of the step-by-step reasoning process. 

The same instructions and prompts were used across all five models to ensure fairness.

Our primary goal is to illuminate, with concrete data, how these models perform. In the sections that follow, we will take a closer look at each benchmark result, analyzing not only the raw numbers but also the potential reasons behind the differences.

Observations

Graduate-Level Reasoning (GPQA Diamond)

(cc: openai)

When it comes to advanced academic queries, such as those covered in graduate-level courses, GPQA Diamond offers a reliable gauge of a model’s capacity to handle intricate logical chains. The overall scores for GPQA Diamond highlight a few interesting patterns:

• Claude 3.7 Sonnet: Achieves about 78.2% in standard mode, rising to 84.8% when allowed more time for extended thinking. This suggests that Claude 3.7 can adapt its reasoning depth based on how the user structures the prompt, potentially offering more thorough justifications if given leeway.

• Claude 3.5 Sonnet: Manages 65%, which is still respectable but noticeably lower than its successor. In many of the tasks, it provides decent coverage of the necessary reasoning steps but occasionally misses key logical links, particularly in philosophy-oriented questions.

• OpenAI o3-mini: Typically scores around 75.7% in its standard setting and 78% in a higher-compute mode. While it doesn’t surpass Claude 3.7’s top performance, it maintains a strong showing relative to its focus on cost-effectiveness.

• DeepSeek R1: Lands near 71.5%, benefiting from its extended context window but not always employing the nuanced reasoning that some graduate-level tasks require. Some prompts that reference large bodies of text play to DeepSeek R1’s strengths, but it can falter on short, abstract queries.

• Grok 3 Beta: Often leads the pack in advanced tasks, reaching 80.2% in standard usage and around 84.6% with extended reasoning. It occasionally edges out Claude 3.7 Sonnet in certain subsets of graduate-level questions, particularly those that demand multi-step logical inferences.

Overall, these results indicate that both Claude 3.7 Sonnet and Grok 3 Beta excel in high-level reasoning tasks, with OpenAI o3-mini trailing closely. DeepSeek R1 and Claude 3.5 Sonnet show slightly weaker performances but still handle many graduate-level queries competently.

High School Math (AIME) and Math500

(cc: openai)

AIME questions are known for their trickiness, often requiring more than just rote knowledge of formulas. Meanwhile, Math500 covers a broader spectrum of mathematical reasoning, from simple algebra to more complex geometry and number theory.

• Claude 3.7 Sonnet: Excels in the broader Math500 set with scores around 96.2%, reflecting strong capabilities in step-by-step solutions. However, it attains only 61.3% on AIME, suggesting that while it is adept at systematic problem-solving, it sometimes struggles with the more inventive leaps demanded by competition math.

• Claude 3.5 Sonnet: Scores are generally lower, with about 82.2% on Math500 and around 23.3%–41.3%(depending on the subset tested) on AIME. These results underscore the improvements made in Claude 3.7, especially for complex or non-standard math problems.

• OpenAI o3-mini: Performs surprisingly well on AIME, with pass rates reaching 79.2% or 83.3% in certain configurations. This model’s relatively streamlined architecture appears to handle math competition problems effectively, even though it’s optimized for affordability.

• DeepSeek R1: Typically matches or slightly surpasses OpenAI o3-mini in some sets, with results near 79.8% on AIME and 72.6% on certain other math tasks. The large context window can be advantageous for multi-part problems but may not always translate into higher accuracy for shorter, puzzle-like questions.

• Grok 3 Beta: Leads with about 93.3% on AIME, indicating an impressive ability to tackle the creative reasoning often required in competition settings. For Math500, it consistently ranks near the top, though sometimes it is closely matched by Claude 3.7 Sonnet, especially on problems that require more methodical, step-by-step solutions.

The pattern that emerges here is that Grok 3 Beta demonstrates a remarkable facility with inventive, competition-style math, while Claude 3.7 Sonnet is more consistent across a broader range of mathematical tasks. OpenAI o3-mini and DeepSeek R1 remain competitive, especially when factoring in cost or extended context needs, respectively.

Software Engineering (SWE-bench Verified)

(cc: anthropic)

In many practical deployments, coding assistance is a core function for large language models. SWE-bench Verified focuses on tasks such as debugging, generating new code from descriptions, and refactoring existing code. The results here are typically expressed in pass@1 metrics, reflecting the percentage of times a model’s first attempt is correct.

• Claude 3.7 Sonnet: Rises to the top with a 70.3% pass@1 score when given custom scaffolding. Its base score is around 62.3%, which still surpasses most of the competition. Many see this as a direct consequence of the model’s refined instruction-following capabilities and improved chain-of-thought processes.

• Claude 3.5 Sonnet: Hovers around 49% pass@1, a considerable drop from 3.7’s performance. While still usable for simpler coding tasks, it may require more user guidance or repeated attempts for complex or large-scale projects.

• OpenAI o3-mini: Falls in the same range as Claude 3.5 Sonnet, around 49%, indicating that while it can handle straightforward coding queries effectively, it does not excel in advanced tasks that require extended reasoning about complex codebases.

• DeepSeek R1: Also near 49% on SWE-bench Verified. The large context window can help in scenarios where the user includes extensive documentation or references in the prompt, but the base coding proficiency is not at the level of Claude 3.7.

• Grok 3 Beta: Not as extensively documented in this specific benchmark within our dataset. However, anecdotal results suggest it is capable of sophisticated reasoning about code. Future evaluations might reveal more details about how it compares with Claude 3.7 Sonnet in end-to-end software development tasks.

From these observations, it seems that Claude 3.7 Sonnet is currently the strongest option for coding. Its advantage becomes particularly noticeable when tasks involve multi-step logic or rely on contextual awareness of a project’s structure. For simpler or smaller-scale coding projects, OpenAI o3-mini or Claude 3.5 Sonnet may suffice, especially if cost is a concern.

Agentic Tool Use (TAU-bench)

(cc: anthropic)

A growing area of interest is whether models can effectively interact with external tools or systems to complete tasks like inventory management or flight bookings. TAU-bench measures this by presenting realistic scenarios where the model must parse additional constraints and integrate them into a solution.

• Claude 3.7 Sonnet: Scores 81.2% in retail tasks, significantly outpacing Claude 3.5 Sonnet at 71.5%. This suggests a marked improvement in how the newer model orchestrates multiple steps or tools. For airline tasks, it hovers around 58.4%, indicating that certain specialized scenarios might still pose a challenge.

• Claude 3.5 Sonnet: Achieves 71.5% in retail and lower percentages in airline tasks. It can handle straightforward sequences but occasionally missteps when the instructions involve multiple constraints or real-time updates.

• OpenAI o3-mini: Lands around 73.5% in retail contexts and 54.2% for airline-related tasks, making it a strong mid-range performer. It may not be the top choice for complex multi-step queries, but it remains reliable for simpler operational needs.

• DeepSeek R1: The data indicate results similar to Claude 3.5 Sonnet in agentic tasks, though the extended context can be beneficial if the user includes logs or reference documents in the prompt. The overall accuracy is respectable but not at the level of Claude 3.7’s best-case scenarios.

• Grok 3 Beta: Not thoroughly detailed in the agentic tool use benchmarks in our dataset. However, given its high-level reasoning abilities, it may handle these tasks well if integrated with the right scaffolding.

These scores emphasize the practical advantages of models that can reliably interpret instructions involving external actions. Claude 3.7 Sonnet’s strong performance in retail tasks, in particular, suggests it could be a good choice for large-scale inventory or order management applications.

Further Analyses

Extended Context and Large-Scale Text Processing

One aspect that often arises in real-world deployments is the need to process large volumes of text at once. Models with bigger context windows can, at least in theory, maintain a better global understanding of the entire text or conversation.

• DeepSeek R1: Advertised to support a 32K-token context, which can be particularly useful for tasks like legal document analysis or summarizing multiple references. Its performance on standard benchmarks may not always stand out, but in scenarios involving hundreds of lines of text, DeepSeek R1 can maintain coherence more effectively than models with smaller context windows.

• Claude 3.7 Sonnet: Supports a 128K-token output limit in some advanced settings, allowing it to produce more extended responses than many of its competitors. This can be invaluable for lengthy summaries or multi-chapter analyses, although it may incur higher computational costs.

• OpenAI o3-mini: Optimized for efficiency, it typically does not aim for extremely large context windows. For tasks requiring smaller-scale text processing, its performance is quite strong relative to its cost. In more extensive scenarios, it might need to rely on chunking or pagination strategies.

(cc: deepseek)

In the above diagram, we see that DeepSeek R1 has an advantage in tasks that require scanning long documents for relevant details. However, users with simpler or shorter prompts might not benefit from these extended context features.

Cost-Effectiveness vs. Performance

In certain practical applications—especially those that handle high volumes of requests—cost can be a deciding factor. While the exact pricing details can vary depending on usage agreements, the data we have suggests the following:

• OpenAI o3-mini: Often touted as having about a 95% cost reduction relative to more premium models, making it a compelling choice for budget-conscious deployments. Despite its lower cost, it can still score in the 70–80% range on many tasks.

• Claude 3.7 Sonnet: Tends to command higher pricing (e.g., $3 per million input tokens and $15 per million output tokens in some reported structures). However, many users find that its advanced capabilities, especially in coding and agentic tasks, justify the cost for enterprise use cases.

• Claude 3.5 Sonnet: Priced similarly to 3.7 in some contexts, though it lacks certain advanced features. In large-scale deployments where cost and performance must both be balanced, some organizations still prefer 3.5 if they do not need the extended reasoning of 3.7.

• DeepSeek R1: Details are less transparent, but anecdotal evidence suggests a higher cost tied to its extended context window. This might be an acceptable trade-off for domains like legal or medical analysis, where reading large documents at once is essential.

• Grok 3 Beta: Not all of its pricing details are disclosed, but its advanced reasoning capabilities and strong math performance suggest a premium model. If specialized tasks—such as complex research queries—are a priority, Grok 3 Beta could be worth the investment.

(cc: anthropic)

In the diagram above, we can have a rough comparison between cost vs. performance for each model. Notice how OpenAI o3-mini tends to cluster near the “high value” portion of the chart, while Claude 3.7 Sonnet and Grok 3 Beta occupy a space indicative of higher performance but also higher cost.

Reliability and Output Quality

Beyond raw scores, reliability plays a crucial role in whether a model is suitable for production environments. For example, a model that consistently produces coherent, logically sound answers—even if not always perfect—might be more useful than one that oscillates between brilliant responses and glaring mistakes.

• Claude 3.7 Sonnet: Many users report stable performance across different types of prompts, from short questions to multi-step tasks. Its extended thinking mode appears to reduce the chance of abrupt reasoning failures.

• Claude 3.5 Sonnet: Generally stable but occasionally prone to confusion when confronted with multi-part instructions or tasks that demand advanced reasoning. It remains a solid choice for simpler, well-defined questions.

• OpenAI o3-mini: Maintains a good balance of speed and reliability, though it might oversimplify certain tasks. This simplification, however, can be beneficial for users who need quick, cost-effective answers to moderately complex questions.

• DeepSeek R1: Demonstrates reliability when dealing with large blocks of text, but can sometimes produce partial solutions or incomplete reasoning if the user does not clearly structure the prompt. Its extended context window is a double-edged sword: powerful but requiring more careful prompt design.

• Grok 3 Beta: Shows robust performance on a variety of tasks, with advanced reasoning especially noticeable in math and logic-heavy domains. In some borderline cases, though, it might produce verbose explanations that need to be parsed carefully.

Caveats

Although the data presented here provide a useful window into each model’s capabilities, it is important to note a few limitations:

1. Domain-Specific Training: Some models may have been optimized for particular domains (like coding or advanced mathematics). Their overall scores might not reflect how they handle less common or highly specialized questions.

2. Benchmark Gaps: While benchmarks like AIME, MATH-500, GPQA Diamond, SWE-bench Verified, and TAU-bench are robust, they cannot fully capture the richness of real-world usage. Situations involving unusual language constructs, niche technical jargon, or rapidly evolving knowledge bases could reveal different strengths and weaknesses.

3. Prompt Engineering: The manner in which prompts are written can significantly affect model performance. A model that underperforms in a standard prompt environment might do much better if the user invests effort in carefully crafting instructions.

4. Updates and Versions: Large language models are evolving quickly. The data we discuss reflect performance at a particular point in time. Future releases of these models, or new entrants to the market, could shift the landscape in unpredictable ways.

5. Resource Constraints: Some of these models require substantial computational resources to run effectively, especially when employing extended context windows. Organizations with limited hardware capabilities might find that certain models are not practical at scale.

Keeping these caveats in mind helps ensure a balanced perspective on the results. Although these models can achieve impressive performance in structured benchmarks, real-world conditions often require deeper scrutiny and more specialized testing.

Conclusion

As large language models become more capable and more deeply integrated into a variety of workflows, it grows increasingly important to evaluate them against tangible metrics. In the data summarized here, below are our key takeaways.

• Claude 3.7 Sonnet stands out as a highly versatile choice, excelling in software engineering tasks (with a 70.3% pass@1 in SWE-bench Verified), agentic tool use (81.2% in retail scenarios), and general reasoning (up to 84.8% on GPQA Diamond in extended mode). Its cost is on the higher side, but the payoff in capability and reliability may be worth it for many users.

• Claude 3.5 Sonnet serves as a cost-effective alternative to 3.7 for simpler tasks, though its performance gap is evident in advanced coding and competition math. Organizations that don’t require the full power of 3.7 might still find 3.5 sufficient for routine questions or smaller-scale deployments.

• OpenAI o3-mini presents a compelling case for budget-conscious users, especially those interested in strong math performance on benchmarks like AIME (around 79.2%–83.3%). While it might not dominate every category, its balance of cost and capability is attractive.

• DeepSeek R1 leverages a large context window (32K tokens) to handle tasks that involve scanning extensive text. Though its core scores in coding and advanced reasoning do not outstrip Claude 3.7 Sonnet or Grok 3 Beta, it can excel in domains where reading and summarizing large documents is key.

• Grok 3 Beta frequently ranks at or near the top in advanced reasoning tasks (80.2%–84.6% on GPQA Diamond) and competition math (93.3% on AIME). This suggests that for users who need cutting-edge logical reasoning or complex problem-solving, Grok 3 Beta is a prime candidate.

The choice of which model to use will ultimately hinge on factors like budget, the complexity of tasks, the volume of requests, and the level of reliability required. A research institution exploring novel theoretical problems might opt for Grok 3 Beta, while a large company with a heavy volume of coding queries could favor Claude 3.7 Sonnet. A startup with limited resources might find OpenAI o3-mini’s affordability too good to pass up.

There is no single “best” model, but it also means that users can choose the one that best aligns with their specific needs.

It is our hope that this article, by summarizing the detailed findings of multiple benchmarks, provides a foundation upon which others can build. As new versions of these models emerge and additional metrics are introduced, the landscape is bound to shift. 

Still, the patterns observed here—regarding strengths in coding, math, or large-context reasoning—are likely to remain relevant in guiding decisions about how best to leverage advanced AI systems.

While no single metric can fully capture the potential of these models, collectively they paint a vivid picture of progress in AI. Each system, from Claude 3.7 Sonnet to Grok 3 Beta, embodies a different set of trade-offs, revealing how the field continues to branch out in pursuit of specialized capabilities.

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FAQ

What are the main differences between Claude 3.7 Sonnet and Claude 3.5 Sonnet?

Claude 3.7 Sonnet demonstrates superior performance in advanced reasoning, software engineering, and agentic tool use compared to Claude 3.5 Sonnet, which is better suited for simpler tasks due to its lower accuracy in complex scenarios.

Which AI model performs best in mathematical problem-solving tasks like AIME and Math500?

Grok 3 Beta excels in competition-style math (AIME) with a 93.3% success rate, while Claude 3.7 Sonnet shows consistent strength across broader math tasks like Math500 with a 96.2% score.

How does OpenAI o3-mini compare to other models in terms of cost-effectiveness?

OpenAI o3-mini offers a strong balance of affordability and capability, achieving competitive scores in math and reasoning tasks while being significantly more cost-effective than premium models like Claude 3.7 Sonnet.

What makes DeepSeek R1 unique among the compared AI models?

DeepSeek R1’s large context window (32K tokens) makes it particularly effective for tasks involving extensive text processing, such as legal document analysis or summarizing multiple references.

Which AI model is best for software engineering tasks?

Claude 3.7 Sonnet leads in software engineering benchmarks with a pass@1 score of 70.3%, making it the most reliable option for debugging, code generation, and multi-step coding tasks.

References: deepseek, openai, anthropic, x.ai