What does Token Forge Cloud do?

Token Forge Cloud provides managed APIs for Qwen, DeepSeek, GLM, and Seedance, with additional video and speech models for teams building production AI products. Proven workloads can also move into a Neo Cloud marketplace product path.

Who is Token Forge Cloud built for?

It is built for product and enterprise teams that need reliable language, reasoning, coding, agent, and video model access without operating every provider stack themselves.

Can teams start with APIs before buying through Neo Cloud?

Yes. Teams can begin with managed API access for Qwen, DeepSeek, GLM, Seedance, and additional media models, then move predictable production traffic into a Neo Cloud marketplace product path.

Which AI workflows does Token Forge Cloud support?

Token Forge Cloud supports assistants, reasoning, coding, agents, text-to-video, image-to-video, asynchronous jobs, model routing, and speech generation.

How does Token Forge Cloud handle security and data control?

Token Forge Cloud supports role policies for who can call which models and workflows, controlled access for prompts and application data, and audit-ready request, generation, and routing telemetry.

OpenAI Codex vs Claude: Coding Agents, APIs, and Which to Choose

Short answer: Choose between Codex and Claude Code based on the workflow you need—not on a claim that one is always faster or smarter. Codex’s app supports parallel agent work in separate threads and review of changes; Claude Code’s terminal and IDE surfaces let you work with an agent that reads files, edits code, and runs commands. If you’re building an application with a model API, that’s a separate decision: compare the APIs and models, not the coding agents’ subscriptions.

This comparison distinguishes coding agents from production model APIs. The API examples use OpenAI’s documented gpt-6-astra quickstart model and Anthropic’s claude-sonnet-5-5 model. They are not a claim that those two models are matched coding-agent systems.

First, separate the agent from the model API

“OpenAI Codex vs Claude” can mean two different things:

Coding agents: OpenAI Codex versus Anthropic Claude Code—the products that combine a model with tools, permissions, and a work interface.
Production model APIs: an OpenAI API model versus a Claude API model, called from your own application.

They are related, but not interchangeable. OpenAI describes Codex as a suite that includes Codex CLI, Codex Cloud, and a VS Code extension. Claude Code is available in terminal, IDE, desktop, and browser surfaces. Because products and execution modes vary by surface, the shorthand “Codex is cloud, Claude Code is local” is too broad as a current comparison. (OpenAI, “Unrolling the Codex Agent Loop”; Anthropic, “Claude Code Overview.”)

There is also a naming trap: Codex is not automatically the name of the API model you should call. OpenAI’s API quickstart demonstrates gpt-6-astra; its API page for gpt-5.3-codex marks that model deprecated. Don’t select an API model for a new integration based only on its appearance in an older Codex comparison. (OpenAI API quickstart and model documentation.)

Codex vs Claude Code for coding work

If your priority is…	Consider	Important trade-off
Delegating bounded tasks and coordinating parallel agent work	Codex app	The app supports separate agent threads, diff review, and opening changes in an editor. You still need to define the task, review changes, and run appropriate tests. (OpenAI, “Introducing the Codex App.”)
Working interactively with a project—reading files, editing code, and running commands	Claude Code terminal or IDE	Claude Code documents those capabilities, along with configurable permissions. Choose the permission mode and sandbox boundaries that fit your task; controls differ by mode. (Anthropic, “Claude Code Overview,” “Configure Permissions,” and “Configure the Sandboxed Bash Tool.”)
Debugging a failure that depends on your tools or environment	Try an interactive workflow first	This is a workflow-based suggestion, not a guarantee that either agent can reproduce the issue. Provide relevant logs and test the result in the actual environment.
Refactoring many files	Evaluate both on your repository	Competitor authors disagree about which tool handles refactors better; their accounts aren’t a controlled comparison. Don’t present them as a general performance result.

A practical decision rule is to trial both on a few representative tasks from your own codebase. Include one clearly specified change and one task with ambiguity or environment dependencies. Compare review time, test results, adherence to project conventions, and the amount of steering needed. Keep human review and tests in the workflow whichever agent you choose.

Security depends on configuration, not just product labels. Claude Code documents permissions for file and shell actions, as well as sandboxing that can restrict shell access to files and network hosts. A sandbox can narrow what a command reaches, but it does not by itself determine what context is sent to the model. Check the current data-handling terms and settings for the exact product surface, provider, and plan you use. (Anthropic, “Configure Permissions,” “Configure the Sandboxed Bash Tool,” and “Choose a Sandbox Environment.”)

Production APIs: compare API access directly

For basic text generation, these examples use different provider APIs:

OpenAI’s quickstart uses the Responses API, the OpenAI JavaScript SDK, and an API key.
Anthropic’s Messages API uses POST /v1/messages; its TypeScript SDK can read ANTHROPIC_API_KEY from the environment. (OpenAI API quickstart; Anthropic TypeScript SDK and Messages API reference.)

Keep API keys on your server or in a secrets manager—not in browser code or a public repository.

OpenAI Responses API

npm install openai
export OPENAI_API_KEY="your-key"

import OpenAI from "openai";

const client = new OpenAI();
const response = await client.responses.create({
  model: "gpt-6-astra",
  input: "Review this function for edge cases: function clamp(x, min, max) { return Math.min(max, Math.max(min, x)); }",
});

console.log(response.output_text);

The official OpenAI JavaScript SDK documents that the client reads OPENAI_API_KEY automatically and that response.output_text combines the response’s text output. The model identifier and Responses API pattern follow OpenAI’s published quickstart and SDK documentation.

Claude Messages API

npm install @anthropic-ai/sdk
export ANTHROPIC_API_KEY="your-key"

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

const client = new Anthropic();
const message = await client.messages.create({
  model: "claude-sonnet-5-5",
  max_tokens: 512,
  messages: [
    {
      role: "user",
      content: "Review this function for edge cases: function clamp(x, min, max) { return Math.min(max, Math.max(min, x)); }",
    },
  ],
});

const text = message.content
  .filter((block) => block.type === "text")
  .map((block) => block.text)
  .join("\n");

console.log(text);

Anthropic’s model documentation lists claude-sonnet-5-5; its Messages API reference documents the endpoint and request structure, and its TypeScript SDK documentation shows environment-variable authentication and extracting text blocks from the response.

These are starter requests, not complete production integrations. Before shipping:

Handle errors and retries. Check each provider’s current SDK guidance and account limits; rate limits vary by model, organization, or usage tier. OpenAI documents following Retry-After when present; Anthropic documents rate-limit responses and retry guidance.
Add usage monitoring and budget controls, and evaluate streaming or tool use if your application needs them.
Review data controls for the exact API features you use. OpenAI says API inputs and outputs are not used for training by default, while abuse-monitoring logs may retain content for up to 30 days unless approved controls apply. Anthropic documents that retention varies by API and feature. Check the applicable retention policy and terms for the features and arrangements you use; don’t treat a default policy as a substitute for reviewing your contract or compliance requirements.

Cost: subscriptions are not API billing

For API text tokens, the official price tables list these standard rates in USD per 1 million tokens:

API model	Input	Output
OpenAI `gpt-6-astra`	$10	$50
Anthropic `claude-sonnet-5-5`	$2	$10

These are standard token rates; caching, batch processing, tools, and other features can change the bill. The models are not established here as equivalent in capability, so price alone does not identify the better choice. (OpenAI API pricing; Anthropic API pricing.)

Worked example: At those standard rates, a request using 100,000 input tokens and producing 20,000 output tokens would cost about $2.00 with gpt-6-astra ($1.00 input + $1.00 output), versus about $0.40 with claude-sonnet-5-5 ($0.20 input + $0.20 output). This is arithmetic using listed per-token prices, not a measured workload; actual requests may use different token counts or incur other charges.

For coding-agent access, ChatGPT Plus is listed at $20/month with expanded Codex usage, and ChatGPT Pro starts at $100/month with longer Codex sessions. Claude Pro is $20/month billed monthly (or $17/month with an annual subscription paid upfront) and includes Claude Code; Claude Max starts at $100/month. These subscriptions are separate from API usage. Check each plan’s current limits and terms before buying; don’t assume an agent subscription gives your application API access. (ChatGPT plans and pricing; Claude plans and pricing.)

Bottom line

Choose Codex if the Codex surface’s delegation and parallel-work features fit how your team assigns and reviews tasks. Inspect changes and test them before merging.
Choose Claude Code if its interactive project workflow and configurable file and shell controls fit your development process. Select permissions and sandbox settings deliberately.
For a production application, compare APIs separately. Use current model documentation, price a representative workload, and run a task-specific evaluation before committing.
Use both only when the workflow justifies it. A hybrid setup may suit teams with distinct delegated and interactive tasks, but adds procurement, security, and review overhead.

The useful comparison is not a universal winner or an old benchmark table: it is a clear separation between the coding agent you use for engineering work and the model API your software calls in production.