Build Smarter with the Right LLM

Choosing the right language model can slash costs, improve speed, and unlock bigger workflows. Learn how to pick and switch LLMs in Nocodo AI with just one click - no code, no hassle.

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Tanja Bayer

Tanja Bayer

6 min read
You've just dragged an LLM node into your Nocodo AI workflow, and suddenly you're staring at a dropdown with 23+ model options. OpenAI, Anthropic, Meta, Google, AWS - the list keeps growing. Should you pick GPT-4o? Claude 3.7? Maybe Llama 3.1 or Amazon's Nova series?
The choice might seem overwhelming, but it doesn't have to be. In the next 5 minutes, you'll learn exactly how to choose the right LLM for your specific use case - without the marketing hype or technical jargon. Let's cut through the complexity and get your workflow running with the optimal model.

Why Your LLM Choice Matters

Selecting the right language model isn't just about capability - it's about practical considerations that directly impact your project's success:
  • Cost efficiency : The difference between models can be dramatic. Switching from GPT-4 to Amazon Nova Micro for a high-volume chatbot could slash your costs by 90% with minimal quality loss.
  • Latency : Response times range from 200ms to several seconds. For interactive applications, this can make or break the user experience.
  • Context window : Need to process entire documents? The difference between an 8K and 200K token context window is enormous for applications like document analysis or knowledge retrieval.
  • Multimodality : Some models handle only text, while others can process images, audio, and soon video as inputs.
  • Compliance : Data residency requirements might limit you to specific providers or deployment options.

Before You Begin

To follow along with this guide, you'll need:
  1. A Nocodo AI workspace (sign up here if you don't have one)
  2. API keys for at least one LLM provider
  3. A test prompt that represents your actual use case
If you haven't set up your API keys yet, Nocodo AI provides detailed guides for configuring:
You can add these credentials at either the organization or project level in Nocodo AI's intuitive credentials manager.

Quick-Pick Decision Matrix

Here's your grab-and-go reference for selecting the optimal model based on common use cases:
Flowchart showing primary needs linked to AI solutions, including cost efficiency, text processing, latency, hosting, and reasoning capabilities.
LLM Selection Decision Tree

LLM Selection Decision Matrix

Use Case Recommended Model Why Choose This
Cheapest chat at scale Amazon Nova Micro (128K Context) Fraction-of-a-cent pricing with sub-second latency
Vision + text workflows GPT-4o (128K Context) Industry-leading reasoning with seamless image handling
Long documents & RAG Claude 3.7 Sonnet (200K Context) Excellent at synthesis with lower cost than GPT-4 tier
Ultra-fast FAQ bot Claude 3.5 Haiku (200K Context) Lowest latency in Anthropic's lineup
Open-source / on-prem Llama 3.1 70B Instruct (128K Context) Permissive license with state-of-the-art quality
Enterprise with 1M context Gemini 2.5 Pro Exp. (1M Context) Massive context for legal, research, and media workflows
Cutting-edge reasoning GPT o3 or Claude 4 Sonnet Preview access to next-gen capabilities
Feel free to screenshot this matrix for quick reference!

Provider Deep Dive

You just saw the quick-pick matrix: a fast, first-choice guide for common use cases. But maybe you're the kind of builder who wants to understand the trade-offs before locking in a model - or you're choosing for a team, not just a one-off workflow.
This section is for you.
We’ll walk through each major LLM provider supported in Nocodo AI: what models they offer, their context limits, where they shine, and where they fall short. If you’re comparing performance, licensing flexibility, or multimodal capabilities, this is your one-stop overview.
Let’s go deeper.

Anthropic (Claude Family)

Anthropic has positioned the Claude family as thoughtful assistants with strong reasoning capabilities and robust guardrails.
Available Models in Nocodo AI:
  • Claude 3 Haiku
  • Claude 3.5 Haiku
  • Claude 3.5 Sonnet
  • Claude 3.7 Sonnet
  • Claude 4 Opus & Claude 4 Sonnet
Context Window: All models support 200K tokens - a significant advantage for document processing.
Technical Deep Dive: Claude models excel at maintaining coherence across long contexts. Their training emphasizes helpful, harmless, and honest outputs, making them particularly good for tasks requiring nuanced understanding or careful explanation.
Strengths:
  • Exceptional narrative coherence with long documents
  • Well-calibrated responses (less prone to hallucinations)
  • The Haiku variants offer impressive speed
Watch-outs:
  • No local deployment license available
  • Pricing higher than open-source but lower than GPT-4o
  • Limited multimodal capabilities compared to GPT-4o

Amazon (Nova Family)

Amazon's Nova models, available through AWS Bedrock, deliver solid performance with AWS's enterprise-grade security and competitive pricing.
Available Models:
  • Nova Micro (128K context, text-only)
  • Nova Lite (300K context, multimodal)
  • Nova Pro (300K context, multimodal, enhanced reasoning)
  • Nova Premier (1M context, multimodal, complex reasoning)
Technical Deep Dive: Nova models integrate seamlessly with the AWS ecosystem, making them ideal for organizations already invested in AWS. They excel at structured data processing and offer exceptional token economics for high-volume applications.
Strengths:
  • Highly cost-efficient at scale
  • AWS-native for seamless integration
  • Fast response times with predictable performance
Watch-outs:
  • Limited community documentation
  • Text-only limitations at lower tiers (e.g., Micro)

Meta Llama

Meta’s Llama family offers open-source models that are developer-friendly and flexible across a range of deployment environments.
Available Variants:
1B, 3B, 8B, 70B, and 405B Instruct - all with 128K context windows.
Technical Deep Dive:
Llama models shine in open-source contexts. They are ideal for teams that need full model control, fine-tuning, or on-premises inference. Deployment is possible at the edge or on local GPUs, making them attractive for privacy-sensitive or low-latency use cases.
Strengths:
  • Fully open-source and fine-tune ready
  • Can be run locally or on custom infrastructure
  • No vendor lock-in
Watch-outs:
  • No vision or multimodal capabilities (yet)
  • Slower in zero-shot reasoning compared to GPT or Nova
  • Larger variants require careful inference tuning

OpenAI (GPT Family)

OpenAI’s models remain the industry standard for general-purpose reasoning, tool-calling, and multimodal workflows.
Available Models:
  • GPT-4 (8K legacy)
  • GPT-4 Turbo (128K)
  • GPT-4o and GPT-4o Mini
  • GPT o1 (advanced reasoning prototype)
  • GPT o3 and GPT o3-mini
  • GPT o4-mini
Context Range: 8K → 100K tokens.
Technical Deep Dive:
The GPT line is known for leading-edge performance, especially in logic, instruction following, and multi-step reasoning. GPT-4o introduces native multimodal support with fast response times and smooth handling of text-image input/output.
Strengths:
  • Best-in-class reasoning and coherence
  • Robust tool-calling and system prompt support
  • Seamless multimodal capabilities (especially GPT-4o)
Watch-outs:
  • Among the highest cost per token
  • Strict usage quotas for newer models
  • Limited deployment flexibility (SaaS only)
  • Data residency constraints (mainly US/EU)

Google (Gemini Family)

Google’s Gemini Pro models are geared toward large-scale document and media processing with strong GCP alignment.
Available Model:
  • Gemini 2.5 Pro Experimental (1M context tokens, multimodal)
Technical Deep Dive:
Gemini is currently experimental within Nocodo AI and ideal for workflows that require long-form comprehension (contracts, media logs, research papers) and vision input. It integrates deeply with Google Cloud services and offers strong grounding on web sources.
Strengths:
  • Largest available context window (up to 1 million tokens)
  • Multimodal processing built-in
  • Great for legal, research, and summarization workflows
Watch-outs:
  • Rate limits and pricing are still evolving
  • Early access model - may be less stable
  • Only available via GCP integration

Switching Models in Nocodo AI

Changing your model mid-project doesn't require rebuilding your workflow or rewriting prompts. In Nocodo AI, switching models is a one-click operation that preserves your settings and credentials - making experimentation easy and low-risk.

Here’s how:

  1. Open the LLM node in your workflow.
  2. Click the “Model Name” dropdown to view available options.
  3. Select a new model (e.g., swap Claude 3.5 Sonnet for Amazon Nova Lite ).
  4. Save & deploy. Your prompt, variables, and auth credentials remain untouched.
Once deployed, you can monitor and compare :
  • Latency (faster = smoother UX)
  • Token usage & cost
  • Response quality & consistency
💡 Pro tip: Duplicate the workflow and A/B test different models in separate channels. You’ll get side-by-side data with real prompts and traffic - perfect for picking a winner.

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