The AI Model Selection Guide is a 5-question wizard that recommends the best AI models for your specific use case, budget, and deployment requirements. Answer the questions to see ranked recommendations with match scores and cost estimates.
How to Use the AI Model Selection Guide
Choosing the wrong AI model for your application wastes engineering time and money. The AI Model Selection Guide asks 5 targeted questions about your use case, budget, performance priorities, deployment requirements, and scale — then scores 25+ models to surface the best fit.
How the Scoring Works
Each model has baseline scores across dimensions: quality, speed, cost-effectiveness, privacy, context length, multilingual support, and task-specific strengths (code, creative, reasoning). Your answers shift the weights on these dimensions. A "Free/open source" budget answer multiplies the privacy and cost weights, bringing Llama and Mistral to the top. "Code generation" as the primary task boosts models with strong SWE-bench scores.
Reading the Results
The top 3 cards show gold/silver/bronze recommendations. The match score (e.g., 87%) reflects how well each model aligns with your answers — it's relative, not absolute. A 70% match is still a strong recommendation; it means the model scores high on your key criteria but may trade off on some secondary factors. The "Why recommended" bullets explain which of your answers drove the score.
Using Results as a Starting Point
This guide is a starting point, not a final decision. After identifying your top candidate, test it with your actual prompts and data before committing. LLM performance varies significantly by task — benchmark scores don't always predict production behavior. Always evaluate on representative examples from your specific use case.
FAQ
GPT-4o vs Claude 3.5 Sonnet — which is better?
Both are top-tier models as of 2026. Claude 3.5 Sonnet edges ahead on coding tasks (SWE-bench) and reasoning, while GPT-4o has stronger multimodal capabilities and broader tool ecosystem. For most applications, the difference is negligible — choose based on API pricing, rate limits, and your team's existing integrations.
When should I use an open-source model vs a closed API?
Use open-source (Llama 3.1, Mistral) when you need data privacy (no data leaving your infrastructure), predictable costs at high volume, custom fine-tuning control, or air-gapped deployment. Use closed APIs when you need the highest quality output, fastest iteration, or lack GPU infrastructure. At 1M+ tokens/day, self-hosting can be 5–20x cheaper.
What is the best model for code generation?
Claude 3.5 Sonnet leads on SWE-bench (real-world coding tasks) as of early 2026. GPT-4o and DeepSeek Coder V2 are strong alternatives. For autonomous coding agents, Claude 3.5 Sonnet is currently preferred by most practitioners. For free/self-hosted code generation, Code Llama and DeepSeek Coder V2 are the top open-source options.
Which AI model has the longest context window?
Gemini 1.5 Pro supports up to 2M tokens (about 1,500 pages of text). Claude 3.5 Sonnet supports 200K tokens. GPT-4o supports 128K tokens. For most RAG applications, 128K tokens is more than sufficient — the effective retrieval limit is usually around 32K tokens before quality degrades.
Is there a free AI model I can use?
Yes — several. Llama 3.1 (Meta, open source) is free to self-host and can run on a consumer GPU. Gemini 1.5 Flash has a generous free tier (15 RPM, 1M tokens/day). Groq offers free API access to Llama and Mistral models with fast inference. Ollama lets you run Llama, Mistral, Phi-3, and others locally for free.
Is this model recommendation tool free?
Yes, completely free with no signup required. All logic runs in your browser with static data.