Which LLM should you choose in 2026? A timely question
Open-source vs. proprietary models: An overview
Open-source LLMs, such as those released byThinking Machines(e.g., Inkling 2 Large and Inkling 2 Small), offer transparency, flexibility, and the ability to adapt to specific hardware stacks like AMD MI355X GPUs. Proprietary models, offered by providers such asTogether AI,OpenAI, orAnthropic permitted, often provide optimized services, pre-trained base models, and dedicated commercial support.
- Open source: Inspectable code, customizable architecture, ideal for specialized use cases, hardware integration, and regulatory compliance.
- Proprietary: Turnkey services, scalability, integrated prompting pipelines, continuous support.
Key factors for decision-making
When evaluating which path to take, consider these practical factors:
1. Budget and operational costs
Open-source LLMs can reduce inference costs on existing hardware but still require resources for infrastructure and maintenance. Proprietary models typically follow a subscription or pay-per-use model.
2. Performance and optimizations in 2026
Recent optimizations, such as Zyphra’s FP8 FlashAttention kernel (ZYFA) for AMD MI355X, can boost open-source LLM performance by up to 1.76x compared to earlier kernels. Similarly, the Together AI Compiler, which uses agent-driven loops, can accelerate inference by up to 2.6x.
3. Complexity of the use case
For generic chat or content creation tasks, proprietary models often maintain an edge in coherence. For specialized tasks like scientific data analysis or code synthesis, open-source models allow for customized fine-tuning.
4. Regulatory and security requirements
Sensitive or regulated data (e.g., HIPAA, GDPR) may make an open-source model preferable, as inspectable code helps verify privacy and security.
5. Availability of talent
If your team already has experience with PyTorch, Hugging Face, or AMD toolchains, an open-source model can be integrated more quickly.
Practical example: Comparing an open-source LLM with a proprietary one
Suppose you need to create a chatbot that generates Python code with embedded comments. Below is a quick comparison with prompt engineering examples for both approaches.
Scenario: Prompt engineering for an open-source model (based on Inkling 2 Small)
# Prompt for an open-source model with additional context
prompt = f'''
You are an expert programming assistant who writes clean, well-documented Python code.
Use snake_case naming style.
Provide a concise explanation for each block.
Context: {context}
Question: {question}
'''Scenario: Prompt engineering for a proprietary model (e.g., GPT-4)
# Prompt for a proprietary model with built-in instructions
prompt = f'''
Generate a Python solution that follows these guidelines:
- Clear, commented code
- Use snake_case convention for variables
- Provide brief explanatory notes for each function
Context: {context}
Request: {question}
'''Both prompts use the same structure, but the open-source model can be trained specifically on code datasets, while the proprietary model relies on its vast pre-trained knowledge.
Performance in 2026: What the benchmarks say
Recent benchmarks reveal an interesting picture:
- Zyphra ZYFA (FP8 FlashAttention) on AMD MI355X
- Together AI Compiler Inkling 2 Small (594B MoE, 17B active)
These figures suggest that open-source LLMs can compete in terms of speed and cost, especially when leveraging the latest hardware and software optimizations.
How to create effective prompts for both approaches
Even though models differ, the principles of good prompt engineering remain constant:
- Clarity of purpose
- Context and constraints
- Structure
- Frames
When using an open-source model, consider adding an explicit instruction for the model about the underlying hardware (e.g., "optimize for AMD MI355X GPU"). Proprietary models often handle this automatically, but an explicit reminder can improve consistency.
Final thoughts and next steps
In 2026, the line between open-source and proprietary LLMs is less clear than ever. The best choice depends on budget, performance, compliance, talent, and the specific use case.
Concrete actionsyou can take today:
- Evaluate your current workloads against the benchmarks above.
- Design a proof-of-concept (PoC) with an open-source model (e.g., Inkling 2 Small) using a structured prompt as shown.
- Compare latencies and costs with a proprietary endpoint (e.g., Together AI).
- Define a prompt engineering playbook that works for both approaches, so you can quickly switch between models.
Next steps
Want a more detailed example of an end-to-end workflow that uses both an open-source and a proprietary LLM? Subscribe to our newsletter or leave a comment to receive a complete technical demo with real scripts and performance numbers.
IMPORTANT: We are in 2026. All temporal references must be current. Do not mention past years such as 2024, 2023, etc. The content must be fresh and relevant to the present. CURRENT CONTEXT (November 2026): Here are some recent trends and news to inspire you: - Zyphra ZYFA: FP8 FlashAttention for AMD MI355X, Up to 1.76x Faster: Zyphra released ZYFA, open-source FP8 and BF16 prefill attention kernels for AMD Instinct MI355X. On real model activations, its FP8 kernels run 1.16-... [2026-11-06] - Google Releases Gemini Omni 1.2 Flash: 15-Second AI Video at $0.07 per Second: Google released Gemini Omni 1.2 Flash, a video generation and editing model with native 15-second clips, multi-turn conversational editing and a 30% p... [2026-11-05] - Thinking Machines Releases Inkling 2 Small, a 594B MoE With 17B Active: Thinking Machines Lab released Inkling 2 Small, a 594B-parameter MoE with 17B active parameters. It scores 31 on the Artificial Analysis Intelligence ... [2026-11-06] Use this current information as inspiration to create an original and relevant prompt for 2026.