The AI Model Dilemma: Choosing for Cost, Freshness & Performance (Lessons from building a Steam Utility)

Hey Dev.to community!

When I set out to build the AI game suggestion feature for my side project, steamid.one (a suite of tools for Steam users), I thought the hardest part would be prompt engineering or integrating the API. Turns out, the real challenge (and one I rarely see discussed in depth) was which AI model to actually pick for a lean, production-ish service.

We're drowning in options: OpenAI's GPT-X, Google's Gemini, Anthropic's Claude, various open-source models... each with different pricing, rate limits, latency, and crucially, knowledge cutoffs that can quickly make your "smart" feature feel dated.

For steamid.one, I needed an AI that could:

• Understand nuanced gaming data: It needed to analyze player profiles (top games, playtimes, genres) and synthesize creative, distinct multiplayer game suggestions.
• Be cost-effective at scale: This is a free tool, so every penny counts. Sending thousands of requests for game suggestions had to be cheap.
• Provide fresh data (or not be bottlenecked by old data): Game trends change. A model with an outdated knowledge cutoff might suggest irrelevant titles.
• Be reliable and fast: Users don't want to wait ages for a suggestion.

After a lot of testing and staring at pricing tables, I ended up going with Google Gemini 1.5 Flash.

Here's why (and some findings on various models I considered):

I looked primarily at gpt-3.5-turbo, gpt-4o, Anthropic's Claude 3 Haiku, and Google's gemini-1.5-pro and gemini-1.5-flash.

Pricing (per 1 Million tokens, approximate public rates as of July 2025):

• OpenAI (GPT Series):
  • gpt-3.5-turbo (0.5M context): ~$0.50 input / $1.50 output.
  • gpt-4o (128K context): ~$5.00 input / $15.00 output.
• Anthropic (Claude Series):
  • Claude 3 Haiku (200K context): ~$0.25 input / $1.25 output.
• Google (Gemini Series):
  • gemini-1.5-pro (128K context, standard): ~$3.50 input / $10.50 output.
  • gemini-1.5-flash (128K context, standard): ~$0.35 input / $1.05 output.

For the kind of creative text generation and structured JSON output I needed for game suggestions, Flash offered a phenomenal balance of capability and extreme cost-effectiveness. This was the biggest driver for a free, public tool. Claude 3 Haiku was also very competitive on price.

Knowledge Cutoff vs. Practicality:

Most general models (GPT, Gemini, Claude) have cutoffs typically in early to mid-2024. They are constantly being updated, but rely on training data up to a certain point.

My discovery: For game suggestions based on user-provided data, the cutoff was less of a direct blocker than I initially thought. The AI's strength was its reasoning about the provided data (e.g., "User A loves survival games, User B loves crafting, User C likes indie titles -> suggest Don't Starve Together"), rather than needing to inherently "know" every new release since early 2024. This shifted my prompt engineering focus from "fill in recent data" to "reason expertly with provided data."

Context Window & Latency:

• While gpt-4o, Claude 3 Haiku, and gemini-1.5-pro offer substantial context windows, gemini-1.5-flash (with its 1M token capability if requested, otherwise 128K standard) still allowed for rich user profiles and lists of previously suggested/owned games to be included directly in the prompt. This was critical for generating genuinely new and relevant suggestions without hallucinating owned games.
• The ability to output structured JSON directly from the model was also a huge win for robust API integration, something all these models generally handle well, but Flash did it at a compelling price point.
• For an interactive feature, getting a response back quickly was key. Gemini Flash proved to be very snappy, often returning suggestions in under a second (after API calls to get profile data).

The learning curve was steep, navigating token usage, API quirks, and ensuring the AI stayed "on character" as a "gaming concierge" that outputs strict JSON. But the results have been great for users!

I'd love to hear from this community:

• For your side projects or production apps, how do you approach selecting the "right" AI model given the constant trade-offs between cost, capability, and knowledge cutoff?
• What are your go-to strategies for managing costs and ensuring relevant output for AI features that rely on real-time external data?
• Any surprising discoveries about model performance vs. price you've encountered?

You can see the AI suggestion feature in action (among other things) at: https://steamid.one

Thanks for any feedback!