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Model Library

Built for agents that replay context. Agent traffic resends the whole conversation on every tool call. Here the repeated part hits cache and bills at the cached input rate.

5 models

Capabilities

FiltersAll models

Capabilities

GLM 5.3 Flash

glm-5-3-flashTrending

Model capabilities: Tool calling / JSON mode / Streaming / Image input

per 1M input tokens

per 1M cached input tokens

per 1M output tokens

cache hit, last 24 hours

output tok/s, model only, before the gateway

Effective input $0.031 per 1M at that hit rate Effective input rate $0.031 per 1M tokens, from the measured cache share on billed agent traffic in the last 24 hours. Cache hits are best effort; the method is published at runinfra.ai/methodology.

DeepSeek V4.1 Flash

deepseek-v4-1-flashTrending

Model capabilities: Tool calling / JSON mode / Streaming

per 1M input tokens

per 1M cached input tokens

per 1M output tokens

cache hit, last 24 hours

output tok/s, model only, before the gateway

Effective input $0.031 per 1M at that hit rate Effective input rate $0.031 per 1M tokens, from the measured cache share on billed agent traffic in the last 24 hours. Cache hits are best effort; the method is published at runinfra.ai/methodology.

Nemotron 3.5 Lightning 30B

nemotron-3-5-lightning-30b

Model capabilities: Tool calling / JSON mode / Streaming

per 1M input tokens

per 1M cached input tokens

per 1M output tokens

cache hit, last 24 hours

output tok/s, model only, before the gateway

Effective input $0.013 per 1M at that hit rate Effective input rate $0.013 per 1M tokens, from the measured cache share on billed agent traffic in the last 24 hours. Cache hits are best effort; the method is published at runinfra.ai/methodology.

Qwen3.8 27B

Model capabilities: Tool calling / JSON mode / Streaming / Image input

per 1M input tokens

per 1M cached input tokens

per 1M output tokens

cache hit, last 24 hours

output tok/s, end to end, on the buyer path

Effective input $0.016 per 1M at that hit rate Effective input rate $0.016 per 1M tokens, from the measured cache share on billed agent traffic in the last 24 hours. Cache hits are best effort; the method is published at runinfra.ai/methodology.

Ornith 1.5 35B

ornith-1-5-35b

Model capabilities: Tool calling / JSON mode / Streaming / Image input

per 1M input tokens

per 1M cached input tokens

per 1M output tokens

cache hit, last 24 hours

output tok/s, model only, before the gateway

Effective input $0.012 per 1M at that hit rate Effective input rate $0.012 per 1M tokens, from the measured cache share on billed agent traffic in the last 24 hours. Cache hits are best effort; the method is published at runinfra.ai/methodology.

Pick a model. Copy the call.

Works with the OpenAI and Anthropic SDKs.

ShellChat completion

# Set RUNINFRA_GATEWAY_KEY to your API key before running this example.
curl -X POST "https://api.runinfra.ai/v1/chat/completions" \
  -H "Authorization: Bearer $RUNINFRA_GATEWAY_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"nemotron-3-5-lightning-30b","messages":[{"role":"user","content":"Hello"}],"max_tokens":4096}'

# Set RUNINFRA_GATEWAY_KEY to your API key before running this example.
import os
import uuid
from openai import OpenAI

api_key = os.environ.get("RUNINFRA_GATEWAY_KEY")
if not api_key: raise RuntimeError("Set RUNINFRA_GATEWAY_KEY first.")

client = OpenAI(
    api_key=api_key,
    base_url="https://api.runinfra.ai/v1",
)

response = client.chat.completions.create(
    model="nemotron-3-5-lightning-30b",
    messages=[{"role": "user", "content": "Hello"}],
    max_tokens=4096,
    extra_headers={"X-Client-Request-Id": str(uuid.uuid4())},
)
print(response.choices[0].message.content)

// Set RUNINFRA_GATEWAY_KEY to your API key before running this example.
import { randomUUID } from "node:crypto";
import OpenAI from "openai";

const apiKey = process.env.RUNINFRA_GATEWAY_KEY;
if (!apiKey) throw new Error("Set RUNINFRA_GATEWAY_KEY first.");

const client = new OpenAI({
  apiKey,
  baseURL: "https://api.runinfra.ai/v1",
});

const response = await client.chat.completions.create(
  {
    model: "nemotron-3-5-lightning-30b",
    messages: [{ role: "user", content: "Hello" }],
    max_tokens: 4096,
  },
  { headers: { "X-Client-Request-Id": randomUUID() } },
);

console.log(response.choices[0]?.message.content ?? "");

// Set RUNINFRA_GATEWAY_KEY to your API key before running this example.
// Server-side only: never expose RUNINFRA_GATEWAY_KEY in browser code.
import { createOpenAICompatible } from "@ai-sdk/openai-compatible";
import { generateText } from "ai";

const apiKey = process.env.RUNINFRA_GATEWAY_KEY;
if (!apiKey) throw new Error("Set RUNINFRA_GATEWAY_KEY first.");

const runinfra = createOpenAICompatible({
  name: "runinfra",
  baseURL: "https://api.runinfra.ai/v1",
  apiKey,
});

const { text } = await generateText({
  model: runinfra.chatModel("nemotron-3-5-lightning-30b"),
  maxOutputTokens: 4096,
  messages: [
    { role: "user", content: "Write a TypeScript retry helper." },
  ],
});

console.log(text);

# Set RUNINFRA_GATEWAY_KEY to your API key before running this example.
import os
from anthropic import Anthropic

api_key = os.environ.get("RUNINFRA_GATEWAY_KEY")
if not api_key: raise RuntimeError("Set RUNINFRA_GATEWAY_KEY first.")

client = Anthropic(
    # The SDK appends /v1, so use the bare host.
    base_url="https://api.runinfra.ai",
    api_key=api_key,
)

message = client.messages.create(
    model="nemotron-3-5-lightning-30b",
    max_tokens=4096,
    messages=[{"role": "user", "content": "Hello"}],
)
print(message.content[0].text)