LLM Inference API

300+ LLMs, one OpenAI-compatible API

Call GPT-4o, Claude Opus, Gemini Pro, Sarvam, Llama, DeepSeek, Mistral — and 290 more — through a single OpenAI-compatible endpoint. Automatic prompt caching, tenant-level keys, no markup.

300+ models — swap with a string, not a refactor
OpenAI-compatible + Anthropic-compatible shapes
Automatic prompt caching saves 30–70% on agent workloads
India-region routing, tenant isolation, real usage analytics

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300+LLM models

1API endpoint

OpenAICompatible shape

30%Avg cost saving

Features

Built for production agent workloads, not demos

We run ~200M tokens a day across our own products — you get the same infrastructure.

300+ models, one endpoint

GPT-4o / 4o-mini, Claude 4.7 Opus / 4.6 Sonnet / Haiku, Gemini 2.5 Pro / Flash, Llama 3.3 / 4, Mistral, DeepSeek V3, Qwen 2.5, Sarvam M / 105B — and 290 more. Change models by flipping a string.

OpenAI-compatible API

Drop-in replacement for openai.chat.completions. Works with the official OpenAI SDK, LangChain, LlamaIndex, LiteLLM, Vercel AI SDK, and every other tool that speaks OpenAI.

Transparent pricing

Same per-token prices as each model's official provider — we don't mark up. Cache hits are free. No per-request fees, no minimum commits, no enterprise-sales lock-in.

Streaming + low latency

First-token-latency in the 200–500ms range depending on the model. Multiplexed connections and warm model pools across AWS ap-south-1 (Mumbai) and us-east-1.

Best-in-class for Indic languages

Sarvam M and 105B integrated natively — the highest-accuracy Indic LLMs available. Automatic routing can send your Hindi queries to Sarvam and English to GPT-4o for best quality-per-rupee.

India data residency

Route to India-region providers when available. SOC2, GDPR, DPDP-aware. No training on your prompts. BAA available for healthcare. Tenant isolation enforced per API key.

Use Cases

How teams use the CallMissed LLM API

From simple chat to multi-agent pipelines — one API scales across use cases.

Chatbot + support

Replace $500/mo OpenAI bills

Teams running customer support chatbots route 80% of queries to cheaper models (Sarvam M, GPT-4o-mini, Claude Haiku) and reserve Claude Opus for complex escalations. Same quality, 70% cost drop.

Result

Typical customer saves ₹3–5 lakh/year on LLM spend.

RAG + search

Retrieval + synthesis pipeline

Pair our embeddings API (models: text-embedding-3-large, voyage-3, Cohere embed-v4) with a chat model for RAG. Consistent API shape across embedding + chat means one SDK, one auth, one invoice.

Result

Unified RAG stack without vendor juggling.

Content generation

Marketing copy at scale

Agencies use Claude for tone, GPT for structure, and Sarvam for vernacular translation — all behind one API. Dynamically pick the right model per content type.

Result

3 models, 1 codebase, 10x throughput.

Agent workflows

Multi-step agent orchestration

Build agents that use different models for different steps: fast model for intent classification, reasoning model for planning, vision model for document parsing, Indic model for customer reply.

Result

Per-step model selection improves quality + lowers cost.

Voice agent backends

Low-latency LLM for voice pipelines

Voice agents need ~300ms first-token-latency to feel natural. Route voice traffic to fast, India-region inference (Sarvam M, GPT-4o-mini, Gemini Flash) for the best conversational experience.

Result

Voice interactions that feel human, not laggy.

Analytics + BI

SQL + data Q&A on your warehouse

Connect your BI tool to CallMissed. Users ask questions in Hindi or English, the LLM generates SQL against your warehouse, runs it, and answers in plain language. Guardrails prevent destructive queries.

Result

BI self-serve across the whole org, in every language.

Compare

CallMissed vs OpenAI, Anthropic, OpenRouter, Together

Direct-to-provider works; our layer just makes multi-model + India-region + tenanting easier.

Feature	CallMissed	OpenAI	Anthropic	OpenRouter	Together AI
300+ models under one API
OpenAI-compatible shape
Native Indic models (Sarvam)
India data residency
No markup on provider prices
Prompt caching automatic
Per-tenant API keys + usage analytics
Anthropic + OpenAI shape both supported
Free starter credits	$5	$0	$0	$1	$25

Comparison based on publicly listed features as of 2026. Check each vendor's site for the latest.

Code

Drop-in OpenAI SDK

If you already call OpenAI, point base_url at CallMissed and you're done. Keep your code — change the model.

python

from openai import OpenAI

client = OpenAI(
    base_url="https://api.callmissed.com/v1",
    api_key="cm_your_key",
)

# 300 models — pick one
response = client.chat.completions.create(
    model="claude-opus-4-7",          # or gpt-4o, gemini-2.5-pro, sarvam-m ...
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain prompt caching in 2 sentences."},
    ],
    stream=True,
)

for chunk in response:
    print(chunk.choices[0].delta.content, end="", flush=True)

Call Claude Opus 4.7 via the OpenAI SDK

javascript

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic({
  baseURL: "https://api.callmissed.com/anthropic",
  apiKey: process.env.CM_KEY,
});

const msg = await client.messages.create({
  model: "claude-sonnet-4-6",
  max_tokens: 1024,
  messages: [{ role: "user", content: "Hello Claude" }],
});
console.log(msg.content[0].text);

Anthropic SDK also works — /anthropic/v1/messages shape

FAQ

LLM inference questions, answered

LLM inference is the process of running prompts through a large language model and getting back a completion. CallMissed offers one API endpoint that routes to 300+ models from different providers — OpenAI (GPT), Anthropic (Claude), Google (Gemini), Meta (Llama), Mistral, DeepSeek, Alibaba (Qwen), Sarvam (Indic), and more. You write your code once and switch models by changing a string.

One API. 300 models. 1000 free API credits.

Get API key Browse model catalog