AI API Comparison 2025: OpenAI, Claude, Gemini

AI API Comparison: OpenAI vs Claude vs Gemini vs Ollama

In 2025, developers have access to more than 10 commercial LLM APIs. Choosing the right model directly affects the quality, speed and cost of your AI application.

Full Provider Comparison

Budget Options

How to Choose a Model

• Support chatbot — GPT-4o-mini or Claude 3 Haiku (cheap, good quality)
• Document analysis — Claude 3.5 Sonnet (200K context) or Gemini 1.5 Pro (1M context)
• Code generation — Claude 3.5 Sonnet or GPT-4o (best for code)
• Local use / privacy — Ollama + Llama 3.1
• Maximum speed — Groq (750 tokens/sec)
• Cheapest of all — Gemini 1.5 Flash

Multi-Provider Architecture

class AIRouter:
    def __init__(self):
        self.providers = {
            'openai':    OpenAI(api_key=os.environ["OPENAI_API_KEY"]),
            'anthropic': anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"]),
        }

    def complete(self, prompt: str, provider: str = 'openai', model: str = None) -> str:
        if provider == 'openai':
            r = self.providers['openai'].chat.completions.create(
                model=model or 'gpt-4o-mini',
                messages=[{'role': 'user', 'content': prompt}],
                max_tokens=500
            )
            return r.choices[0].message.content
        elif provider == 'anthropic':
            r = self.providers['anthropic'].messages.create(
                model=model or 'claude-3-5-haiku-20241022',
                max_tokens=500,
                messages=[{'role': 'user', 'content': prompt}]
            )
            return r.content[0].text

router = AIRouter()
print(router.complete("Write a greeting for the website", provider='anthropic'))

Common Errors When Switching Between Providers

The main challenge in a multi-provider architecture is not the price difference but the incompatible request and response formats between APIs.

Message Format Is Not Compatible Between APIs

OpenAI builds a request as a messages array with system/user/assistant roles, while Anthropic passes the system prompt as a separate system field rather than as a system-role message inside the array. Forwarding the same request body to a different API without converting the format results in a validation error on the provider's side, not a meaningful response.

Token Counting Differs Between Providers

The tokenizers used by OpenAI, Anthropic, and Google do not match: the same text produces a different token count in each system. If the max_tokens limit in your code was set based on one provider's tokenizer, switching to another can cut the response off earlier or later than expected. Calculate limits separately for each provider instead of reusing one number across all of them.

Latency and Reliability: What to Watch Besides Price

When Not to Build a Multi-Provider Architecture

• The project is small, and switching providers is a rare manual decision rather than an automatic fallback — the abstraction adds complexity without real benefit.
• The product depends on a unique feature of one provider (a specific tool or multimodal capability, for example) that cannot be properly replaced by another API.
• The team is small, and maintaining several SDKs and response formats costs more than the potential savings on token pricing.
• Data storage and processing requirements lock you into a single approved provider — a multi-provider setup is redundant in that case.

Verifying the Router Picks the Right Provider

python3 -c "
from router import AIRouter
r = AIRouter()
print(r.complete('ping', provider='openai')[:20])
print(r.complete('ping', provider='anthropic')[:20])
"

Run a short test request through each provider separately and confirm both return a non-empty response without exceptions. Only after that add automatic fallback logic — if the basic call to a provider was not tested on its own, an error in the fallback chain will be hard to tell apart from a regular API failure.