Prompt
What's the most effective LLM API for question answering in a developer platform?
Latest observation
The “most effective” LLM API for question answering in a developer platform depends on what you mean by effective: accuracy, latency, cost, context length, tool use, or ease of integration.
If you want a practical default choice, here’s the short version:
Best overall for QA
OpenAI GPT-4.1 / GPT-4o
- Strong answer quality
- Good instruction following
- Good tool/function calling
- Mature ecosystem and docs
- Works well for productized Q&A, support bots, and developer assistants
Best if you want long-context retrieval-heavy QA
Anthropic Claude 3.5 Sonnet
- Very strong at summarization and reasoning over long documents
- Often excellent for support/search over large knowledge bases
- Good natural-language responses
Best if cost and scale matter a lot
Gemini 1.5 Flash / similar fast tiers
- Lower latency and cost
- Good for high-volume QA where perfect reasoning is less critical
Best open-weight option
Llama 3.1/3.2 via a hosted provider
- Useful if you need self-hosting, data control, or vendor flexibility
- Usually requires more tuning and orchestration to match top proprietary APIs
What I’d recommend for a developer platform
For a general-purpose Q&A feature:
- Use a strong model like GPT-4.1 or Claude 3.5 Sonnet
- Add RAG (retrieval-augmented generation) for your docs/data
- Use citations / source grounding
- Cache frequent queries
- Add a smaller cheaper model for routing, classification, or easy queries
Decision rule
- Need best product quality right now: GPT-4.1 or Claude 3.5 Sonnet
- Need best long-document QA: Claude 3.5 Sonnet
- Need best tool-use / platform integration: OpenAI
- Need lowest cost at scale: Gemini Flash or a smaller model
- Need self-hosted control: Llama
If you want, I can give you a side-by-side comparison table for OpenAI, Anthropic, Google, and open-source models specifically for:
- accuracy
- latency
- cost
- context length
- function calling
- RAG performance
- production readiness
Observation history
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How did Obsurfable measure this prompt?
Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.
Which AI systems does Obsurfable collect answers from?
OpenAI, ChatGPT, Google, Gemini, Google AI Mode, Anthropic, Claude, Perplexity, Grok, DeepSeek, Mistral, Copilot, and Meta AI.