Open source LLMs continue to compete with proprietary models on performance benchmarks for natural language tasks like text generation, code completion, and reasoning.
Despite having fewer resources than closed models, these open LLMs offer cutting-edge AI without the high costs and restrictions of proprietary models.
However, running these open-source models in production and at scale remains a challenge. Enter Serverless GPUs: a cost-effective, scalable way to deploy and fine-tune LLMs without managing complex infrastructure.
In this blog post, we’ll explore the best open LLMs available at the start of 2025, including: DeepSeek-R1, Mistral Small 3, and Qwen 2.5 Coder. After comparing their capabilities and ideal use cases for real-world AI applications, we’ll also share how to fine-tune and deploy them using serverless GPUs for optimized inference and training.
DeepSeek-R1 Qwen 32B
DeepSeek released two first-generation reasoning models: DeepSeek-R1-Zero and DeepSeek-R1.
DeepSeek-R1-Zero was trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT), allowing it to explore chain-of-thought (CoT) reasoning for complex problem-solving.
Although this approach led to impressive advancements, DeepSeek-R1-Zero faced challenges, such as: repetition, poor readability, and language mixing. To improve performance, DeepSeek developed DeepSeek-R1, with cold-start data incorporated before RL.
In addition to these two models, DeepSeek released six models of varying sizes based on Llama and Qwen, including DeepSeek-R1-Distill-Qwen-32B.
Distilled models are smaller models that have been trained with the reasoning patterns of larger, more complex models.
- Model Provider: DeepSeek
- Model Size: 32B
- Context Length: 131K tokens
- Comparison to Proprietary Models: DeepSeek-R1-Distill-Qwen-32B outperforms OpenAI-o1-mini across various benchmarks. Explore available benchmarks
- Skills: Strong in reasoning, mathematical reasoning, and general natural language tasks
- Languages Supported: Primarily trained in English and Chinese
- License: Apache 2.0
Mistral Small 3
Mistral AI is a leading provider for AI models, including multimodal models like Pixtral 12B and Large, edge models such as Ministral 3B and 8B, LLMs like Nemo Instruct, Codestral for code generation, Mathstral for mathematics, and more.
Released in January 2025, Mistral Small 3 Instruct is a 24-billion-parameter model that achieves state-of-the-art capabilities comparable to larger models. It is ideal for various text generation tasks, including fast-response conversational agents, low-latency function calling, and any other applications requiring robust language understanding and instruction-following performance.
This model is an instruction-fine-tuned version of the base model: Mistral-Small-24B-Base-2501.
- Model Provider: Mistral AI
- Model Size: 24B parameters
- Context Window: 32K tokens
- Comparison to Proprietary Models: Competitive with larger models like Llama 3.3 70B and Qwen 32B. Explore available benchmarks
- Skills: Strong at summarization, conversational AI, multilingual tasks, and creating highly accurate subject matter experts for specific domains
- Languages Supported: English, French, German, Spanish, Italian, Chinese, Japanese, Korean, Portuguese, Dutch, Polish and more
- License: Apache 2.0
Deploy Mistral Small 3 on Koyeb →
Qwen 2.5 Coder 7B Instruct
Qwen2.5 is a new family of models from Qwen that includes Qwen2.5 LLMs, and specialized models Qwen2.5-Math for mathematics and Qwen2.5 Coder for coding.
The open-source Qwen2.5 models available with an Apache 2.0 license include:
- Qwen2.5: 0.5B, 1.5B, 7B, 14B, and 32B
- Qwen2.5-Coder: 1.5B, 7B, and 32B
- Qwen2.5-Math: 1.5B and 7B
There are also 3B and 72B variants, not available with an open-source license.
Among all the advancements in AI, code generation has been significant. Qwen 2.5 7B Coder Instruct stands out for its high performance in code tasks, including generation, reasoning, and code fixing.
- Model Provider: Alibaba Cloud
- Model Size: 7.61B
- Context Length: 131,072 tokens
- Comparison to Proprietary Models: Performs better than other open source code generation models. Competitive performance with GPT-4o. Explore available benchmarks
- Skills: Code generation, code reasoning and code fixing
- Languages Supported: Over 10, including Chinese, English, and Spanish
- License: Apache 2.0
Deploy Qwen 2.5 Coder 7B Instruct on Koyeb →
Best Open Source Models for Reasoning, Code Generation, and More
- ✅ Best for reasoning → DeepSeek-R1-Distill-Qwen-32B
- ✅ Best for conversational AI & summarization → Mistral Small 3
- ✅ Best for coding → Qwen 2.5 Coder 7B Instruct
Fine-Tuning and Deploying Open LLMs with Serverless GPUs
Open-source AI models like DeepSeek-R1, Mistral Small 3, and Qwen 2.5 Coder provide powerful alternatives to proprietary options, offering flexibility and cost-effectiveness.
With Koyeb’s serverless GPUs, you can fine-tune and deploy these models with a single click. Get a dedicated inference endpoint running on high performance GPUs without managing any infrastructure.
- Explore the one-click deploy catalog
- Deploy vLLM, Ollama, and other open-source models like Flux.1 [dev] and Phi-4
- Read our documentation
- Sign up for Koyeb to get started deploying serverless inference endpoints today
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