OpenAI vs Anthropic vs Google: When to Use Which (And Why Routing Helps)

Teams often ask: should we use OpenAI, Anthropic, or Google for our LLM workload? The better question is: do we need to choose just one? A routing layer lets you use all three—and pick the right model per request.

Quick comparison

	OpenAI (GPT-4, 4o, mini)	Anthropic (Claude)	Google (Gemini Pro, Flash)
Strength	Broad capability, ecosystem	Long context, safety	Speed, multimodal, value
Cost	Mid to high (4o-mini is cheap)	Mid	Often lower (Flash)
Latency	Good	Good	Flash is very fast
Context	Large	Very large	Large

You don’t have to bet the company on one. Use GPT-4o for complex reasoning, Claude for long documents, Gemini Flash for fast cheap calls—and let a router choose by strategy and cost cap.

Why not just pick one provider?

• Lock-in. Switching later means rewriting integration, keys, and error handling.
• Single point of failure. If that provider has an outage, you’re stuck.
• Wrong model for the job. One model might be overkill (and overpriced) for simple tasks, or underpowered for others.

A multi-model router in front of your API keys gives one integration. You send a prompt and strategy; the router picks the best model (OpenAI, Anthropic, Google, Groq, DeepSeek, etc.) for that request. You keep your keys; the router never resells tokens. See what an LLM routing layer is for the full picture.

When each shines (and when to route)

• OpenAI: Strong all-rounder, great tool use and coding. Use when you want a safe default. Route to 4o-mini when cost matters.
• Anthropic: Long context, careful output. Use for documents, compliance, or when you want strong safety. Route to Haiku for cheaper long-context.
• Google: Gemini Flash is fast and cheap. Use for real-time or high-volume. Route to Pro when you need more capability.

With StepBlend you can set strategy (lowest cost, balanced, fastest, max reliability) and a per-request cost cap. One endpoint, multiple providers. Try the Optimizer → or read the routing API.