Google’s recent release of the Gemini 2.5 Pro AI model is headline news for most AI news blogs. The new model is Google’s most advanced and debuts at #1 on LMArena. But how good is it? How much does it cost? (And is it worth that cost?) How well does it compare to state-of-the-art models like the Claude 3.7 Sonnet and DeepSeek R1? Questions are many, but the answers are yet to be found. Let’s find out.
First, let’s get a detailed overview of the Gemini 2.5 Pro AI mode.
Gemini 2.5 Pro AI Model Overview
Gemini 2.5 Pro is Google’s most advanced AI model. It features thinking capabilities, reasoning through thoughts before responding. This enhances accuracy and performance. As per Google, a system’s ability to “reason” goes beyond just classifying and predicting. That means the system can analyze information, draw logical conclusions, understand context, and make informed decisions. What does this mean for the Gemini 2.5 Pro? Well, it achieves a new level of performance by combining a significantly enhanced base model with improved post-training.
Gemini 2.5 Pro Key Features Include:
- Performance Metrics: Tops the LMArena leaderboard with an Arena Elo score of 1443. It achieves 84.0% on GPQA Diamond, 92.0% on AIME 2024 (single attempt), and 63.8% on SWE-Bench Verified with a custom agent setup, as detailed in a recent analysis (RDWorldOnline: Gemini 2.5 Pro).
- Context Window: Ships with a 1 million token context window, with plans to expand to 2 million soon, making it suitable for handling vast amounts of information.
- Use Cases: Ideal for general-purpose AI assistance, coding, research, and tasks requiring large context, given its leading benchmark scores and expansive context window.
Gemini 2.5 Pro vs Claude 3.7 Sonnet Comparison
Claude 3.7 Sonnet, from Anthropic in February 2025, is a hybrid model with standard and extended thinking modes. (here’s an article comparing it with Open AI o3) It’s strong in coding, scoring 70.3% on SWE-Bench Verified in extended mode, better than Gemini’s 63.8%. Its Arena Elo is 1296/1304 (standard/extended), lower than Gemini’s 1443. For math, it scores 80.0% on AIME 2024 in extended mode, behind Gemini’s 92.0%. Science (GPQA Diamond) is close, with 84.8% vs Gemini’s 84.0%. It’s ideal for coding and problem-solving, with pricing at $3/M input, $15/M output.
Claude 3.7 Sonnet Key Highlights:
- Release Date: February 2025, as announced on Anthropic’s website (Anthropic: Claude 3.7 Sonnet).
- Performance Metrics: Achieves an Arena Elo score of 1296 in standard mode and 1304 in extended thinking mode (thinking-32k), according to the LMArena leaderboard (LMArena Leaderboard). On SWE-Bench Verified, it scores 70.3% in extended mode, outperforming Gemini in this metric. For GPQA Diamond, it reaches 84.8% in extended mode, and on AIME, it scores 80.0% in extended mode, as per comparative evaluations. It also has an MMLU score of 86.1%, indicating strong multitask accuracy.
- Context Window: Configurable up to 128K tokens, offering flexibility for various task sizes.
- Pricing: Input at $3 per million tokens, output at $15 per million tokens, as noted in pricing comparisons.
- Use Cases: Best suited for coding, software development, and complex problem-solving, particularly with its extended thinking mode for detailed analysis, as seen in its strong performance on coding benchmarks and reduced unnecessary refusals by 45% compared to predecessors.
Gemini 2.5 Pro vs DeepSeek R1 Comparison
DeepSeek R1, released in January 2025 by DeepSeek, is open-source under MIT license, costing $0.14/M input, $0.55/M output. (here’s a detailed comparison between OpenAI o3-mini and DeepSeek R1) Its Arena Elo is 1360, between Gemini and Claude. It performs well in math (71.0% on AIME 2024, 95.9% on MATH-500) but lags in science (73.3% on GPQA Diamond). Coding score (LMArena) is 1368, competitive but specific benchmarks like SWE-Bench are not detailed. It’s best for cost-sensitive, open-source projects, with a 128K context window.
DeepSeek R1 Key Highlights:
- Release Date: January 2025, as per DeepSeek’s API documentation (DeepSeek API Docs).
- Performance Metrics: Has an Arena Elo score of 1360, positioning it between Gemini and Claude on general performance. It excels in math, with 95.9% on MATH-500 and 71.0% Pass@1 on AIME 2024, and scores 73.3% on GPQA Diamond, as per benchmark comparisons. Its coding performance is competitive, with an LMArena coding score of 1368, but specific SWE-Bench scores are not detailed, though it is noted to beat OpenAI o1 on certain coding benchmarks.
- Context Window: 128K tokens, suitable for a range of tasks but smaller than Gemini’s.
- Pricing: Extremely cost-effective, with input at $0.14 per million tokens and output at $0.55 per million tokens, making it attractive for budget-conscious users.
- Use Cases: Ideal for open-source projects, cost-sensitive applications, and tasks requiring strong math and reasoning capabilities, given its open-source nature and low cost.
Detailed Comparison: Gemini 2.5 Pro vs Claude 3.7 Sonnet vs DeepSeek R1
To facilitate a clear comparison, the following table summarizes key metrics and use cases, ensuring all models are evaluated on common grounds where possible. Note that some scores, especially for DeepSeek R1 on SWE-Bench, are inferred from competitive statements against OpenAI o1, as specific numbers were not always available.
| Model | Release Date | Organization | License | Context Window | Arena EloCoding (LMArena) | Math (AIME 2024) | Science (GPQA Diamond) | Pricing (Input/Output) | Use Cases |
| Gemini 2.5 Pro | Mar 2025 | Proprietary | 1M tokens | 1443 | 1427 | 92.00% | Proprietary | General AI assistant, coding, research | |
| Claude 3.7 Sonnet | Feb 2025 | Anthropic | Proprietary | up to 128K | 1304* | 1338 | 80.0%* | $3/M, $15/M | Coding, software development, problem-solving |
| DeepSeek R1 | Jan 2025 | DeepSeek | MIT | 128K | 1360 | 1368 | 71.00% | $0.14/M, $0.55/M | Open-source projects, cost-sensitive apps, math |
Google’s Gemini 2.5 Pro distinguishes itself with a significantly larger context window and leading performance in coding and complex reasoning tasks like science, albeit with a proprietary model. In contrast, Anthropic’s Claude 3.7 Sonnet offers a strong, commercially-focused solution, particularly adept at coding and problem-solving, while DeepSeek R1 presents a compelling open-source alternative with competitive pricing and notable strengths in mathematical reasoning, catering to cost-sensitive and community-driven applications.
Case Study Comparison for Frontend Code Generation
What good is a comparison if it doesn’t have a case to back it up? We compared Google Gemini 2.5 Pro, Claude 3.7 Sonnet, and DeepSeek R1 against one another for a coding prompt to see which offers the best result. Here’s our prompt for this frontend code-generation comparison:
Now let’s take a look at our results:
1. Google Gemini 2.5 Pro
As you can see, the result is impressive, on par with Claude 3.7 Sonnet. (as you can see below) The ray-traced reflections look convincing.
2. Claude 3.7 Sonnet
Claude 3.7 Sonnet’s ver. of the scene is arguably the best of the bunch, but that’s expected given just how good it is for coding tasks. We used Bind AI’s IDE for the generation, which enhanced it further.
3. DeepSeek R1
DeepSeek R1’s ver. of the scene is the least impressive, which wasn’t necessarily expected.
Google Gemini 2.5 Pro and Claude 3.7 Sonnet generated a vibrant ray tracing scene, producing impressive results and convincing reflections. But DeepSeek R1’s output, while functional ‘enough’, did not quite reach the same level of visual sophistication as the other two models.
Here are some more frontend prompts you can try to test each of these models:
FAQ
Which model is the best overall?
Research suggests Gemini 2.5 Pro is the top performer based on Arena Elo (1443), leading in general and specific benchmarks like AIME and GPQA.
Which model is best for coding?
It seems likely that Claude 3.7 Sonnet is best for coding, with 70.3% on SWE-Bench Verified in extended mode, though Gemini’s LMArena coding score (1427) is higher, indicating strong performance in user-preferred coding tasks.
Which model is best for math and science?
The evidence leans toward Gemini 2.5 Pro for math (92.0% on AIME 2024) and is competitive in science (84.0% on GPQA Diamond), with Claude 3.7 Sonnet slightly ahead in science (84.8%).
Is there an open-source option?
Yes, DeepSeek R1 is open-source under the MIT license, offering transparency and accessibility for developers.
What are the context window sizes?
- Gemini 2.5 Pro: 1 million tokens, expanding to 2 million soon.
- Claude 3.7 Sonnet: Configurable up to 128K tokens.
- DeepSeek R1: 128K tokens, suitable for various tasks but smaller than Gemini’s.
How do the models compare in terms of cost?
DeepSeek R1 is the most cost-effective with $0.14/M input and $0.55/M output, while Claude 3.7 Sonnet is at $3/M input and $15/M output. Gemini 2.5 Pro’s pricing is proprietary and not specified, likely aligning with standard industry rates.
The Bottom Line
Each model caters to different needs:
- Gemini 2.5 Pro is the leader for overall performance, ideal for general AI assistance, coding, and research, especially with its large context window and top benchmark scores.
- Claude 3.7 Sonnet (which you can try here) excels in coding and complex problem-solving, particularly in extended mode, making it suitable for software development and detailed analysis tasks.
- DeepSeek R1 (which you can also try here) stands out for its cost-effectiveness and open-source nature, best for math-intensive tasks and projects with budget constraints, offering a viable alternative for open-source communities.
The choice depends on specific requirements, such as performance, cost, coding needs, or preference for open-source solutions.