Anthropic via VerticalAPI

Anthropic models routed by VerticalAPI

Pass the model ID below as model in any OpenAI-compatible request. New Anthropic models are typically supported within 24h of release.

Pricing reflects Anthropic's rates — you pay Anthropic directly. VerticalAPI adds zero markup on tokens.

5-line Anthropic call via VerticalAPI

Drop-in replacement for the OpenAI SDK. Works with the OpenAI Python client, Node, Go, curl — anything that speaks HTTP.

from openai import OpenAI

client = OpenAI(
    base_url="https://api.verticalapi.com/v1",
    api_key="vapi_...",
    default_headers={"X-Provider-Key": "sk-ant-..."}
)

response = client.chat.completions.create(
    model="claude-sonnet-4-5",  # Anthropic
    messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)

Four reasons developers route Anthropic through us

Anthropic measured: latency, throughput, error rate

Anthropic's Claude Sonnet 4.5 is the slowest of the major flagship tiers on time-to-first-token but compensates with the best coding and tool-use quality scores in the 2026 benchmark. Numbers below are from 1000 calls/model via the VerticalAPI gateway.

Numbers above are 2026 placeholders pending the next VerticalAPI benchmark harness run. See /benchmark for the full 26-provider comparison.

OpenAI SDK methods that work with Anthropic

Anthropic's native API differs from OpenAI's at the wire format (separate system field, content blocks, no logprobs). VerticalAPI normalizes these so the OpenAI SDK works, with a few edge cases to be aware of.

• client.chat.completions.create() — works fully; stream=True, tools, tool_choice all supported.
• logprobs / top_logprobs — not supported by Anthropic; the field is silently dropped (you receive a normal response without logprobs).
• n>1 (multiple completions) — Anthropic does not natively support n>1; VerticalAPI emulates by issuing parallel calls (cost multiplies accordingly).
• response_format="json_object" — Anthropic doesn't have a strict JSON mode equivalent; VerticalAPI prompts for JSON and validates, but enforcement is softer than OpenAI's structured outputs.
• Prompt caching — pass cache_control: {"type": "ephemeral"} on a message to mark a cache breakpoint. Cache hits are billed at 10% of normal input cost.
• Vision — image_url message parts work; data: URIs and HTTPS URLs both accepted.
• client.embeddings.create() — Anthropic doesn't ship embeddings; VerticalAPI returns a clear error directing you to use a provider that does.

What Anthropic actually costs at 100k MAU

Concrete monthly cost for a chatbot with 100k MAU, 10 turns/user, ~500 input + 150 output tokens per turn (650M input, 195M output tokens/month). Anthropic is the most expensive flagship — choose carefully.

Cost based on provider list price; VerticalAPI adds zero token markup.

Should you pick Anthropic for your workload?

Anthropic is the right pick when quality matters more than latency or cost. Specifically:

Specific issues teams hit with Anthropic

Sharp edges that have cost real production teams real time. Fixes below are battle-tested via the VerticalAPI dashboard logs.

Where Anthropic shines

Frequently asked questions

Limitations and trade-offs

• 200K context window is smaller than Gemini 2.5 Pro's 2M for whole-codebase or whole-book inputs.
• No native image, audio or video generation — Claude is text + vision-input only.
• Output tokens at $15 (Sonnet) and $75 (Opus) per 1M are expensive vs open-weight inference on Groq or Cerebras.
• Rate limits on new accounts can require multiple tier upgrades for production agent workloads.
• No fine-tuning or open weights — you cannot self-host Claude, even on AWS Bedrock you rent the model.

Where Anthropic is heading

1. Claude 5 generation expected late 2026 with extended thinking and longer effective context.
2. Wider rollout of computer-use and browser-use APIs for desktop agents.
3. Cheaper Haiku-class distillations targeting on-device and edge deployment.
4. Broader sovereignty options via AWS Bedrock regional endpoints in EU and Asia.

Related questions

ChatGPT, Perplexity and Gemini usually suggest these next.

• Claude Sonnet 4.5 vs GPT-5 for coding — which wins SWE-Bench?
• How does Anthropic prompt caching cut cost by 90%?
• Is Claude Opus 4.5 worth $75 per million output tokens?
• Can I run Claude on AWS Bedrock with my own AWS account?
• What is the best Claude model for long-document RAG?

All supported LLM providers

Same endpoint, same SDK — just change the model and the BYOK header.