# A Survey on the LLM Released in Q1 2025

Canonical URL: https://www.adaline.ai/blog/a-survey-on-the-llm-released-in-q1-2025
LLM text URL: https://www.adaline.ai/blog/a-survey-on-the-llm-released-in-q1-2025/llms.txt
Published: 2025-03-29T00:00:00.000Z
Modified: 2025-04-04T04:50:15.842Z
Author: Nilesh Barla
Category: Research
Visibility: public
Reading time: 6 min
Topics: Research, Adaline, AI agent observability, agent evals, self-improving agents

## Summary

A Comprehensive Analysis of Q1's Breakthrough Language Models and Their Strategic Implications

## Article

# Introduction

When OpenAI released o3 in late December 2024, it set a new bar for AI reasoning models. With its ability to "think" through multiple solution paths before producing an answer, o3 achieved breakthrough performance on complex tasks like mathematics and coding. Just weeks later, DeepSeek countered with R1 - an open-source model challenging o3's abilities at a fraction of the cost.

This face-off highlights the growing rivalry between open and closed-source AI approaches. Companies like OpenAI and Google keep their best models locked behind APIs, charging premium prices for access. Meanwhile, organizations like DeepSeek and Mistral make powerful models freely available for anyone to download and modify.

The tension goes beyond business models. 2024 data shows that 41% of[ ](https://www.linuxfoundation.org/blog/maintainer-motivations-challenges-and-best-practices-on-open-source-software-security-0)[organizations](https://www.linuxfoundation.org/blog/maintainer-motivations-challenges-and-best-practices-on-open-source-software-security-0) actively replace closed models with open alternatives, citing concerns about[ ](https://www.semanticscholar.org/paper/ccba6ae2de7eb608f918a10554b14dd127e93cd1)[data sovereignty](https://www.semanticscholar.org/paper/ccba6ae2de7eb608f918a10554b14dd127e93cd1),[ ](https://www.reddit.com/r/machinelearningnews/comments/1hqj3xp/meta_ai_introduces_a_paradigm_called_preference/)[customization freedom](https://www.reddit.com/r/machinelearningnews/comments/1hqj3xp/meta_ai_introduces_a_paradigm_called_preference/), and[ ](https://www.semanticscholar.org/paper/132babd29cbcff2181492be9cc343631b84e6e5a)[cost efficiency](https://www.semanticscholar.org/paper/132babd29cbcff2181492be9cc343631b84e6e5a). Yet closed models maintain advantages in raw performance, leading benchmarks by 5-15% on average tasks.

This pattern extends across both model types released in Q1 2025:

- Open-source leaders (DeepSeek R1, QwQ-32B, Mistral Small 3) offer impressive[ ](https://www.techtarget.com/searchenterpriseai/podcast/Closing-the-gap-between-open-source-and-closed-AI-models)[capability-to-cost ratios](https://www.techtarget.com/searchenterpriseai/podcast/Closing-the-gap-between-open-source-and-closed-AI-models)
- Closed-source leaders (GPT-4.5, Claude 3.7, Gemini 2.5) push[ ](https://www.dailysabah.com/business/tech/open-source-vs-closed-source-ai-battle-heats-up)[performance boundaries](https://www.dailysabah.com/business/tech/open-source-vs-closed-source-ai-battle-heats-up) at premium prices

In this article, we'll explore these models, compare their strengths, and provide practical guidance on which options make sense for different needs and budgets. Whether you're a researcher, business leader, or curious observer, this guide will help you navigate the rapidly evolving AI landscape of 2025.

# Open-Source Foundation Models: Democratizing Advanced AI

The first quarter of 2025 brought some significant surprises in open-source AI. The most shocking? Smaller models are now punching way above their weight class.

```csv
Model	Developer	Size & Architecture	Key Capabilities
DeepSeek R1	DeepSeek (China)	671B parameters (MoE with 37B active per token)	• Complex reasoning • Long-form content handling • Mathematics • Code generation • Pattern recognition for scientific data
Alibaba QwQ-32B	Alibaba (Qwen Team, China)	32B parameters, Apache 2.0 license	• Mathematical reasoning • Code generation • Tool-aware "agentic" behaviors • Performance comparable to much larger models
Mistral Small 3 (24B)	Mistral AI (France)	24B parameters, latency-optimized, Apache 2.0 license	• Quick, real-time responses (150 tokens/sec) • General instruction-following • Knowledge queries • Basic coding and math • Multi-turn dialogue
```

Take Mistral Small 3, a 24-billion parameter model from France. Despite being tiny compared to giants like GPT-4, it scores an impressive[ ](https://mistral.ai/news/mistral-small-3)[81% on the MMLU benchmark](https://mistral.ai/news/mistral-small-3) (a test of college-level knowledge). Even more impressive, it runs at[ ](https://www.shakudo.io/blog/top-9-large-language-models)[150 tokens per second](https://www.shakudo.io/blog/top-9-large-language-models) - about three times faster than models triple its size. This means it can actually run on your laptop if you have 32GB of RAM!

Alibaba's QwQ-32B shows similar efficiency. At just 32 billion parameters, it somehow[ ](https://qwenlm.github.io/blog/qwq-32b/)[matches the performance](https://qwenlm.github.io/blog/qwq-32b/) of DeepSeek R1 - a model 20 times larger - on complex question answering. How? Alibaba focused heavily on[ ](https://qwenlm.github.io/blog/qwq-32b/)[reinforcement learning](https://qwenlm.github.io/blog/qwq-32b/) during training, teaching the model to reason step-by-step rather than just memorize patterns.

Meanwhile, DeepSeek R1 represents the other approach - going big. At[ ](https://www.reuters.com/technology/artificial-intelligence/chinas-deepseek-releases-ai-model-upgrade-intensifies-rivalry-with-openai-2025-03-25/)[671 billion parameters](https://www.reuters.com/technology/artificial-intelligence/chinas-deepseek-releases-ai-model-upgrade-intensifies-rivalry-with-openai-2025-03-25/), it's the largest open-source model ever released. But it's not just big for show. DeepSeek uses a clever[ ](https://www.shakudo.io/blog/top-9-large-language-models)["Mixture-of-Experts" design](https://www.shakudo.io/blog/top-9-large-language-models), where only a tiny portion (37B) of the model activates for any given task. This makes it much more efficient than its size suggests.

What's really interesting is where these models come from - China and France, not the US tech giants. This regional diversity is pushing innovation in different directions:

- Chinese models focus on raw[ ](https://www.dailysabah.com/business/tech/open-source-vs-closed-source-ai-battle-heats-up)[capabilities and efficiency](https://www.dailysabah.com/business/tech/open-source-vs-closed-source-ai-battle-heats-up)
- European models prioritize[ ](https://mistral.ai/news/mistral-small-3)[speed and practical deployment](https://mistral.ai/news/mistral-small-3) options
- All prioritize open access under[ ](https://arxiv.org/abs/2305.06566)[permissive licenses](https://arxiv.org/abs/2305.06566)

The gap between open and closed models is narrowing fast. DeepSeek R1[ ](https://www.shakudo.io/blog/top-9-large-language-models)[ranked four on Chatbot Arena](https://www.shakudo.io/blog/top-9-large-language-models) (a blind comparison test)—higher than many commercial offerings. Both smaller models[ ](https://mistral.ai/news/mistral-small-3)[outperform proprietary systems](https://mistral.ai/news/mistral-small-3) from just a year ago.

Each model makes different tradeoffs:

- DeepSeek R1: Maximum capability, but needs[ ](https://www.shakudo.io/blog/top-9-large-language-models)[serious hardware](https://www.shakudo.io/blog/top-9-large-language-models)
- QwQ-32B: Great balance of[ ](https://qwenlm.github.io/blog/qwq-32b/)[reasoning power and practical size](https://qwenlm.github.io/blog/qwq-32b/)
- Mistral Small 3: Optimized for[ ](https://mistral.ai/news/mistral-small-3)[speed and everyday deployment](https://mistral.ai/news/mistral-small-3)

This means developers now have real choices rather than just taking whatever scraps big tech companies decide to release. Want to run AI locally? Mistral's your choice. Need maximum reasoning power but can't afford API fees? DeepSeek has you covered. Building a product that needs strong reasoning without breaking the bank? QwQ-32B fits perfectly.

> The open-source revolution is real - making advanced AI accessible to everyone.

# 3. Closed-Source Foundation Models: Pushing the Boundaries

The closed-source landscape in early 2025 reveals an intense battle among tech giants, with reasoning capabilities becoming the new frontier.

```csv
Model	Developer	Size & Architecture	Key Capabilities
OpenAI GPT-4.5 "Orion"	OpenAI (USA)	Undisclosed (largest OpenAI model to date), trained with 10× more compute/data than GPT-4	• Enhanced world knowledge • Improved pattern recognition • Advanced writing abilities • Refined personality • Better instruction following
Anthropic Claude 3.7 Sonnet	Anthropic (USA)	Undisclosed (hundreds of billions of parameters), 200K token context window	• "Hybrid reasoning" (fast vs. deliberate modes) • State-of-the-art coding skills • Long-context processing • Multimodal capabilities • Tool use
xAI Grok 3	xAI (USA)	Trained on "Colossus" supercomputer (10× compute vs Grok-2), integrated with DeepSearch	• Real-time knowledge access • "Think" and "Big Brain" reasoning models • Integrated internet search • Distinct witty personality • Image generation capabilities
Google DeepMind Gemini 2.5 Pro (Experimental)	Google DeepMind (USA)	Multi-hundred-billion to trillion+ parameters, multimodal, enormous context windows	• State-of-the-art reasoning • Mathematics excellence • Superior coding • Multimodal understanding • Tool use (search, code execution)
Alibaba Qwen2.5-Max	Alibaba Cloud (China)	Mixture-of-Experts (MoE), pretrained on 20+ trillion tokens	• Advanced reasoning • Knowledge-intensive tasks • Coding proficiency • Multilingual understanding • Multi-turn dialogue
```

Google DeepMind's Gemini 2.5 Pro leads the pack, claiming the[ ](https://blog.google/technology/google-deepmind/gemini-model-thinking-updates-march-2025)[#1 spot on LMArena](https://blog.google/technology/google-deepmind/gemini-model-thinking-updates-march-2025) with a significant margin. It's the first "thinking model" that deeply integrates chain-of-thought reasoning into its responses. With state-of-the-art math and coding skills, it achieved an impressive[ ](https://blog.google/technology/google-deepmind/gemini-model-thinking-updates-march-2025)[63.8% on SWE-Bench](https://blog.google/technology/google-deepmind/gemini-model-thinking-updates-march-2025), far exceeding competitors.

OpenAI's GPT-4.5 "Orion" represents an evolution rather than a revolution. Trained with 10x more compute than GPT-4, it shines in[ ](https://www.theverge.com/news/620021/openai-gpt-4-5-orion-ai-model-release)[reducing hallucinations](https://www.theverge.com/news/620021/openai-gpt-4-5-orion-ai-model-release) and following instructions more precisely. OpenAI calls it their "most knowledgeable model yet," but interestingly, they don't consider it a "frontier" breakthrough.

Anthropic's Claude 3.7 Sonnet introduces a fascinating innovation - "hybrid reasoning." It can respond instantly or engage an[ ](https://aws.amazon.com/blogs/aws/anthropics-claude-3-7-sonnet-the-first-hybrid-reasoning-model-is-now-available-in-amazon-bedrock/)[extended thinking mode](https://aws.amazon.com/blogs/aws/anthropics-claude-3-7-sonnet-the-first-hybrid-reasoning-model-is-now-available-in-amazon-bedrock/) for complex problems. With a massive 200K token context window and industry-leading[ ](https://aws.amazon.com/blogs/aws/anthropics-claude-3-7-sonnet-the-first-hybrid-reasoning-model-is-now-available-in-amazon-bedrock/)[70.3% on the SWE-Bench](https://aws.amazon.com/blogs/aws/anthropics-claude-3-7-sonnet-the-first-hybrid-reasoning-model-is-now-available-in-amazon-bedrock/) coding benchmark, it's particularly suited for software development and data analysis.

Meanwhile, Alibaba's Qwen2.5-Max shows China isn't just competing - it's leading in some areas. It scores[ ](https://venturebeat.com/ai/alibabas-qwen2-5-max-challenges-u-s-tech-giants-reshapes-enterprise-ai/)[89.4% on Arena-Hard](https://venturebeat.com/ai/alibabas-qwen2-5-max-challenges-u-s-tech-giants-reshapes-enterprise-ai/) (beating DeepSeek R1's 85.5%) and demonstrates impressive reasoning while requiring fewer computational resources, thanks to its Mixture-of-Experts design.

Elon Musk's xAI Grok 3 takes a different approach, focusing on[ ](https://www.shakudo.io/blog/top-9-large-language-models)[real-time knowledge](https://www.shakudo.io/blog/top-9-large-language-models) through its DeepSearch tool. It introduces specialized modes (like "Think" and "Big Brain") and maintains a distinctly witty personality, though performance-wise, it sits slightly behind the largest models on standard benchmarks.

Each model takes a slightly different approach:

- Google focuses on raw[ ](https://blog.google/technology/google-deepmind/gemini-model-thinking-updates-march-2025)[reasoning power and mathematics](https://blog.google/technology/google-deepmind/gemini-model-thinking-updates-march-2025)
- OpenAI prioritizes[ ](https://www.theverge.com/news/620021/openai-gpt-4-5-orion-ai-model-release)[reduced hallucinations and writing quality](https://www.theverge.com/news/620021/openai-gpt-4-5-orion-ai-model-release)
- Anthropic emphasizes[ ](https://aws.amazon.com/blogs/aws/anthropics-claude-3-7-sonnet-the-first-hybrid-reasoning-model-is-now-available-in-amazon-bedrock/)[hybrid modes and coding abilities](https://aws.amazon.com/blogs/aws/anthropics-claude-3-7-sonnet-the-first-hybrid-reasoning-model-is-now-available-in-amazon-bedrock/)
- Alibaba balances[ ](https://venturebeat.com/ai/alibabas-qwen2-5-max-challenges-u-s-tech-giants-reshapes-enterprise-ai/)[performance with computational efficiency](https://venturebeat.com/ai/alibabas-qwen2-5-max-challenges-u-s-tech-giants-reshapes-enterprise-ai/)
- xAI integrates[ ](https://www.shakudo.io/blog/top-9-large-language-models)[real-time knowledge and personality](https://www.shakudo.io/blog/top-9-large-language-models)

> The trend is clear - closed models are differentiated through specialized reasoning modes, real-time knowledge integration, and efficiency innovations, not just raw scale.

The[ ](https://www.dailysabah.com/business/tech/open-source-vs-closed-source-ai-battle-heats-up)[East-West AI competition](https://www.dailysabah.com/business/tech/open-source-vs-closed-source-ai-battle-heats-up) intensifies, with Chinese models increasingly matching or exceeding their Western counterparts.

# 4. Finding Your Match: Who Should Use What

With so many powerful models available, which one fits your needs? Let's break it down:

**For researchers with limited budgets:**

- DeepSeek R1 offers[ ](https://www.shakudo.io/blog/top-9-large-language-models)[cutting-edge capabilities](https://www.shakudo.io/blog/top-9-large-language-models) without API costs
- Fine-tune QwQ-32B for specialized research - it's smaller but[ ](https://qwenlm.github.io/blog/qwq-32b/)[surprisingly capable](https://qwenlm.github.io/blog/qwq-32b/)
- Access to model weights means[ ](https://arxiv.org/abs/2305.06566)[unlimited experimentation](https://arxiv.org/abs/2305.06566)

**For businesses building products:**

- API options: Claude 3.7 for[ ](https://www.anthropic.com/claude/sonnet)[complex reasoning](https://www.anthropic.com/claude/sonnet), GPT-4.5 for reliability
- Self-hosted options: Mistral Small 3 offers great[ ](https://mistral.ai/news/mistral-small-3)[speed-to-performance ratio](https://mistral.ai/news/mistral-small-3)
- Consider total cost: APIs can be cheaper for low volume, self-hosting for[ ](https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/tech-forward/open-source-in-the-age-of-ai)[high volume](https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/tech-forward/open-source-in-the-age-of-ai)

**For specialized needs:**

- Coding:[ ](https://blog.google/technology/google-deepmind/gemini-model-thinking-updates-march-2025)[Gemini 2.5 Pro](https://blog.google/technology/google-deepmind/gemini-model-thinking-updates-march-2025) (63.8% on SWE-Bench) or Claude 3.7 (70.3%)
- Math/reasoning: DeepSeek R1 excels at[ ](https://www.shakudo.io/blog/top-9-large-language-models)[complex problem-solving](https://www.shakudo.io/blog/top-9-large-language-models)
- Real-time data: Grok 3's[ ](https://www.shakudo.io/blog/top-9-large-language-models)[DeepSearch](https://www.shakudo.io/blog/top-9-large-language-models) provides up-to-the-minute responses
- Long document processing: Claude 3.7's[ ](https://aws.amazon.com/blogs/aws/anthropics-claude-3-7-sonnet-the-first-hybrid-reasoning-model-is-now-available-in-amazon-bedrock/)[200K token context](https://aws.amazon.com/blogs/aws/anthropics-claude-3-7-sonnet-the-first-hybrid-reasoning-model-is-now-available-in-amazon-bedrock/) is unmatched

**For deployment constraints:**

- Mobile/edge: Mistral Small 3 runs on[ ](https://www.shakudo.io/blog/top-9-large-language-models)[high-end laptops](https://www.shakudo.io/blog/top-9-large-language-models)
- On-premises: QwQ-32B offers great[ ](https://qwenlm.github.io/blog/qwq-32b/)[performance-to-resource ratio](https://qwenlm.github.io/blog/qwq-32b/)
- API-only: Most closed-source options requiring[ ](https://www.dailysabah.com/business/tech/open-source-vs-closed-source-ai-battle-heats-up)[no infrastructure](https://www.dailysabah.com/business/tech/open-source-vs-closed-source-ai-battle-heats-up)

**Performance vs. accessibility tradeoffs:**

- Closed models lead by[ ](https://www.dailysabah.com/business/tech/open-source-vs-closed-source-ai-battle-heats-up)[5-15% on most benchmarks](https://www.dailysabah.com/business/tech/open-source-vs-closed-source-ai-battle-heats-up) but cost more
- Open models offer[ ](https://www.techtarget.com/searchenterpriseai/podcast/Closing-the-gap-between-open-source-and-closed-AI-models)[80-90% of capabilities](https://www.techtarget.com/searchenterpriseai/podcast/Closing-the-gap-between-open-source-and-closed-AI-models) at a fraction of the cost
- The gap is[ ](https://www.techtarget.com/searchenterpriseai/podcast/Closing-the-gap-between-open-source-and-closed-AI-models)[narrowing every quarter](https://www.techtarget.com/searchenterpriseai/podcast/Closing-the-gap-between-open-source-and-closed-AI-models) - today's closed capabilities are tomorrow's open ones

Choose based on your specific needs rather than hype or size. Sometimes a smaller, more focused model outperforms giants on specialized tasks - and almost always runs faster and cheaper.

# Conclusion

As we look toward the rest of 2025, the data points to several clear trends in AI development. Open models are expected to close the performance gap with closed models. By year-end, they should be within[ ](https://www.semanticscholar.org/paper/ccba6ae2de7eb608f918a10554b14dd127e93cd1)[5% of closed-source capabilities](https://www.semanticscholar.org/paper/ccba6ae2de7eb608f918a10554b14dd127e93cd1), based on industry analyses.

Enterprise adoption is speeding up.[ ](https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/tech-forward/open-source-in-the-age-of-ai)[McKinsey data](https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/tech-forward/open-source-in-the-age-of-ai) shows that 73% of businesses use open-source models for internal processes. Also, 58% apply closed models for customer-facing applications. This hybrid approach will become the new standard.

The most significant shift will be AI's transition from general tools to specialized reasoning engines. Innovations in [model context protocol](https://www.anthropic.com/news/model-context-protocol), [test-time search](/blog/understading-test-time-compute-for-llms), transparent chain-of-thought, and hybrid reasoning will go beyond just technical benchmarks. They will change everyday applications too.

> The AI landscape will provide more options at lower costs for people and organizations. Differentiation will shift from basic skills tounique strengths. It will allow for flexible deployment and match specific needs.