Use multiple AI providers (OpenAI, Anthropic, Google, Meta) through OpenRouter's unified API. Use when comparing providers, building cross-provider workflows, or maximizing availability. Triggers: 'openrouter providers', 'multi provider', 'openrouter openai anthropic', 'compare models openrouter'.
Use multiple AI providers (OpenAI, Anthropic, Google, Meta) through OpenRouter's unified API. Use when comparing providers, building cross-provider workflows, or maximizing availability. Triggers: 'openrouter providers', 'multi provider', 'openrouter openai anthropic', 'compare models openrouter'.
Designed for Claude Code, also compatible with Codex and OpenClaw
OpenRouter Multi-Provider
Overview
OpenRouter's unified API lets you access models from OpenAI, Anthropic, Google, Meta, Mistral, and others with a single API key and endpoint. Model IDs use provider/model-name format. The same OpenAI SDK code works for any provider by simply changing the model ID. This skill covers provider comparison, cross-provider routing, feature normalization, and BYOK (Bring Your Own Key).
Prerequisites
A single OpenRouter API key exported as OPENROUTER_API_KEY — it covers every provider (OpenAI, Anthropic, Google, Meta, Mistral); see the openrouter-install-auth skill for setup
curl and jq for the provider-landscape query
Python 3.8+ with the OpenAI SDK (pip install openai)
For BYOK only: your own provider API key (e.g. an OpenAI key) added in the OpenRouter dashboard under Settings > Integrations > Add Provider Key
Instructions
Survey what's on offer per Provider Landscape: curl -s https://openrouter.ai/api/v1/models | jq ... groups model IDs by their provider/ prefix and sorts by model count.
Benchmark candidates with compare_models() from Cross-Provider Comparison — the same prompt at temperature=0 across Anthropic, OpenAI, Google, and Meta, capturing latency, tokens, and the actual serving endpoint (response.model).
Shortlist by task using the Provider Strength Matrix — Anthropic for analysis/long context, OpenAI for code and tool calling, Google for multimodal and 1M context, Meta for budget work, Mistral for European data residency.
Pin or fail over per Provider-Specific Routing: provider.order with allow_fallbacks: False forces one provider (e.g. for regulated data); allow_fallbacks: True fails across providers such as Anthropic → AWS Bedrock.
For high-volume production, configure BYOK — requests route to your own provider key with the first 1M requests/month free, then 5% of normal provider cost.
Smooth capability gaps with normalized_completion() per Feature Normalization — JSON mode uses response_format natively on openai/ models and a system-prompt instruction elsewhere.
Provider Landscape
# List all providers and their model counts
curl -s https://openrouter.ai/api/v1/models | jq
import os, time, json
from openai import OpenAI
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key=os.environ["OPENROUTER_API_KEY"],
default_headers={"HTTP-Referer": "https://my-app.com", "X-Title": "my-app"},
)
defcompare_models(prompt: str, models: list[str], max_tokens: int = 500) -> list[dict]:
"""Run the same prompt across multiple models and compare results."""
results = []
for model in models:
start = time.monotonic()
try:
response = client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": prompt}],
max_tokens=max_tokens,
temperature=0,
)
latency = (time.monotonic() - start) * 1000
results.append({
"model": model,
"served_by": response.model,
"content": response.choices[0].message.content[:200] + "...",
"tokens": response.usage.prompt_tokens + response.usage.completion_tokens,
"latency_ms": round(latency, 1),
"status": "ok",
})
except Exception as e:
results.append({"model": model, "status": "error", "error": str(e)})
return results
# Compare top-tier models on the same task
results = compare_models(
"Explain the CAP theorem in distributed systems",
models=[
"anthropic/claude-3.5-sonnet", # Anthropic"openai/gpt-4o", # OpenAI"google/gemini-2.0-flash-001", # Google"meta-llama/llama-3.1-70b-instruct", # Meta (open-source)
],
)
for r in results:
print(f"{r['model']}: {r.get('latency_ms', 'N/A')}ms, {r.get('tokens', 'N/A')} tokens")
Provider Strength Matrix
Provider
Best For
Example Models
Price Range
Anthropic
Analysis, safety, long context
claude-3.5-sonnet, claude-3-haiku
$0.25-$15/1M
OpenAI
Code generation, tool calling
gpt-4o, gpt-4o-mini, o1
$0.15-$60/1M
Google
Multimodal, huge context (1M)
gemini-2.0-flash-001, gemini-pro
$0.075-$7/1M
Meta
Budget tasks, self-hosting
llama-3.1-8b-instruct, llama-3.1-70b-instruct
$0.06-$0.90/1M
Mistral
European data residency, code
mistral-large, mixtral-8x7b
$0.24-$8/1M
Provider-Specific Routing
# Force specific provider for a model
response = client.chat.completions.create(
model="anthropic/claude-3.5-sonnet",
messages=[{"role": "user", "content": "Hello"}],
max_tokens=200,
extra_body={
"provider": {
"order": ["Anthropic"], # Direct to Anthropic"allow_fallbacks": False, # Don't fall back to other providers
},
},
)
# Cross-provider fallback: if Anthropic is down, try via AWS Bedrock
response = client.chat.completions.create(
model="anthropic/claude-3.5-sonnet",
messages=[{"role": "user", "content": "Hello"}],
max_tokens=200,
extra_body={
"provider": {
"order": ["Anthropic", "AWS Bedrock"],
"allow_fallbacks": True,
},
},
)
BYOK (Bring Your Own Key)
# Use your own provider API key through OpenRouter# Configure BYOK in the OpenRouter dashboard:# Settings > Integrations > Add Provider Key# Benefits:# - First 1M requests/month free via OpenRouter# - After that, 5% of normal provider cost (vs full OpenRouter markup)# - Data flows directly to provider under your account# - Useful for high-volume production workloads# With BYOK configured, requests automatically use your provider key
response = client.chat.completions.create(
model="openai/gpt-4o", # Uses YOUR OpenAI key, routed through OpenRouter
messages=[{"role": "user", "content": "Hello"}],
max_tokens=200,
)
Feature Normalization
defnormalized_completion(messages, model, **kwargs):
"""Handle provider-specific feature differences."""# JSON mode: OpenAI native, others via system promptif kwargs.pop("json_mode", False):
if model.startswith("openai/"):
kwargs["response_format"] = {"type": "json_object"}
else:
# Add JSON instruction to system prompt for non-OpenAI models
messages = [{"role": "system", "content": "Respond in valid JSON only."}] + [
m for m in messages if m["role"] != "system"
] + [m for m in messages if m["role"] == "system"]
return client.chat.completions.create(model=model, messages=messages, **kwargs)
Output
Comparison result rows per model: served_by (the endpoint that actually answered), truncated content, token totals, latency_ms, and status (ok or the error)
A provider census from the jq query: {provider, models} objects sorted by model count, showing which namespaces dominate the catalog
Completions attributed to their exact serving provider via response.model — the raw material for cost/quality attribution across providers
Examples
One prompt — "Explain what an API gateway is in 2 sentences." — fanned across four providers through the same client produces a directly comparable scoreboard:
[OpenAI] 450ms, 65 tokens — ok
[Anthropic] 380ms, 58 tokens — ok
[Google] 620ms, 71 tokens — ok
[Meta] 510ms, 63 tokens — ok
Anthropic answered fastest with the fewest tokens on this run; the point is that switching providers cost zero code changes beyond the model ID. More worked examples: references/examples.md.
Error Handling
Error
Cause
Fix
Feature not supported
Provider lacks capability (e.g., tools on Llama)
Check model capabilities via /models; use fallback
Different response quality
Providers trained differently
Test critical prompts per model; adjust system prompts
Provider outage
Single provider down
Use provider.order with fallbacks across providers
BYOK auth failure
Provider key expired or invalid
Update provider key in OpenRouter dashboard
Enterprise Considerations
OpenRouter normalizes the API, but models differ in output quality, feature support, and data policies
Use provider.order + allow_fallbacks: true for cross-provider resilience
Test the same prompts across providers during evaluation; don't assume equal quality
BYOK eliminates OpenRouter margin for high-volume workloads (5% vs standard markup)
Route regulated data only to approved providers using allow_fallbacks: false
Monitor which provider actually serves each request (response.model) for attribution
