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.

Codex AI vs Claude Code (2026): Which Coding Agent Fits Your Workflow?

The short answer

There is no supported basis here to declare one coding agent the universal winner. Choose by the specific workflow you need: Codex’s documented desktop Worktree option is relevant if you want a task to run in an isolated local Git worktree. Claude Code’s Manual permission mode is relevant if you want it to ask before most actions that edit files, run shell commands, or access the network. Codex also has approval and sandbox controls, but the available information here does not establish an exact like-for-like difference between their settings.

If you’re building an application that calls a model, compare the OpenAI and Anthropic APIs, not the Codex and Claude Code subscriptions. Coding agents provide ready-made development workflows; a direct API integration means your application must handle the surrounding workflow.

What exactly are Codex and Claude Code?

Codex is OpenAI’s coding-agent product, available through multiple experiences. In the ChatGPT desktop app, documented execution choices include Local (your current project directory), Worktree (an isolated Git worktree on your computer), and Cloud (a remote task in its own workspace). Local execution uses a constrained environment; approval and sandbox settings govern execution.

Claude Code is Anthropic’s coding agent, available in the terminal, IDE extensions, desktop app, and web. For local sessions, permission modes govern approvals; its Manual mode asks before most actions that edit files, run shell commands, or reach the network. Sandbox settings can also restrict shell-command access to files and network hosts. Hosted Claude Code sessions run in an isolated hosted environment; that does not mean the model runs locally.

Both products can work with repositories. Compare the particular product surface, execution mode, and settings you plan to use—not whether one product alone can edit files, run commands, or use cloud execution.

Which coding agent fits your workflow?

If you need…	Codex	Claude Code	Practical takeaway
A named local Git-worktree option	The ChatGPT desktop documentation lists a Worktree environment that isolates changes in a Git worktree.	Claude Code supports local and hosted workflows, but the available evidence here does not establish a directly equivalent worktree option.	Codex is a concrete fit if that specific desktop Worktree workflow is important. This is not a claim that Claude Code cannot be used with Git worktrees outside the documented feature.
Approval before consequential actions	Codex documents approvals and sandbox controls; the exact behavior depends on its settings and environment.	Manual mode asks before most file edits, shell commands, and network access.	Claude Code has a documented Manual-mode choice for this preference. Compare the actual Codex settings you would use before deciding that approval behavior is a differentiator.
A verdict on which agent writes better code	Product feature descriptions do not establish that Codex will produce better changes for your repository.	Product feature descriptions do not establish that Claude Code will produce better changes for your repository.	Run the same representative task in each and compare tests, diffs, review effort, and fit with your workflow.

A developer’s October 9, 2025 comparison reported a preference for Claude Code during investigation and refactor planning, and Codex for focused code drafts. That is an attributed, anecdotal CLI impression—not a controlled, current benchmark or a guarantee about either product today.

For a practical evaluation, give both agents the same small, reversible repository task and acceptance criteria. Compare test results, out-of-scope changes, review effort, and how their controls behaved. This is a suggested evaluation method, not a test performed for this article.

Access and cost: subscriptions are not API billing

The prices below are US monthly prices listed for the plans, not a promise of unlimited use. Reference date for the figures in this draft: November 9, 2026. Check the linked provider pages for current prices and account-specific terms before purchasing.

Access path	Listed US price	Usage conditions to consider
Codex with ChatGPT Plus	$20/month	Codex is included with Plus. Local messages and cloud chats share the plan allowance; usage depends on model and task, and weekly limits may apply. Eligible personal-plan users may see an option to add credits after reaching an included limit; availability depends on plan and account.
Claude Code with Claude Pro	$20/month	Claude Code is included, with usage shared across Claude surfaces. Pro has a five-hour session limit and a weekly limit. Eligible paid-plan users can enable usage credits to continue at additional, consumption-based cost.
Direct model API	Usage-based	API charges are separate from the subscription prices above. Model, input and output volume, caching, and other applicable features can affect the bill.

For a standard token-price illustration, the linked OpenAI pricing page lists GPT-6 Astra at $10 per million input tokens and $50 per million output tokens. Anthropic’s linked pricing page lists Claude Opus 5.5 at $4 per million input tokens and $20 per million output tokens, and Sonnet 5.5 at $2 and $10, respectively. These are provider-listed rates, not a comparison of model quality or a prediction of your bill; usage and other billing terms matter.

For example, 100,000 input tokens plus 20,000 output tokens would cost about $2.00 with GPT-6 Astra, $0.80 with Opus 5.5, or $0.40 with Sonnet 5.5 at those rates. This is arithmetic, not a measured coding workload. It excludes other applicable charges and does not account for caching or special pricing modes.

Do not assume a subscription includes unlimited API calls, or interpret an API rate as the price of using the ready-made coding agent. Codex plan pricing and usage, ChatGPT personal-plan credits, Claude Pro, OpenAI API pricing, and Anthropic API pricing describe these distinct access and billing paths.

Using a model API in production

A direct API is relevant when your own application needs to generate or analyze code. The API call alone does not provide a complete coding-agent workflow: your application is responsible for context, tool execution, permissions, validation, error handling, monitoring, and review.

These minimal Python examples show a text request to each provider’s API. They are smoke tests, not production integrations. See the provider documentation for the OpenAI Responses API and Anthropic Messages API for request details.

OpenAI Responses API:

from openai import OpenAI

client = OpenAI()
response = client.responses.create(
    model="gpt-6-astra",
    input="Explain this Python error: AttributeError: 'NoneType' object has no attribute 'profile'.",
)
print(response.output_text)

Anthropic Messages API:

import anthropic

client = anthropic.Anthropic()
message = client.messages.create(
    model="claude-opus-5-5",
    max_tokens=256,
    messages=[
        {"role": "user", "content": "Explain this Python error: AttributeError: 'NoneType' object has no attribute 'profile'."}
    ],
)
for block in message.content:
    if block.type == "text":
        print(block.text)

Tool use and structured outputs

API model	Tool use	Structured outputs	Important condition
GPT-6 Astra	OpenAI’s documentation says GPT-6 Astra tool calling uses the Responses API.	OpenAI’s structured-output guide demonstrates schema-based output with GPT-6 Astra.	Follow the API-mode and request-format requirements in the function-calling and structured-output guides; the basic text example above does not demonstrate either feature.
Claude Opus 5.5	The Messages API supports tool use; the model-specific notes report that forced tool use returns an error.	Anthropic documents JSON output and strict schema validation for tool names and inputs.	The available documentation excerpts do not establish the applicability of every structured-output mechanism to Opus 5.5 specifically. Check the Opus 5.5 model notes, tool-use guide, and structured-output guide for model and request conditions.

OpenAI’s code-generation guide recommends GPT-6 Astra as a starting point for most API-based code generation. Anthropic’s Messages API uses a direct model request. For either provider, validate important outputs in your application; structured-output features constrain format, but should not be treated as proof that generated code is correct.

Bottom line

Choose Codex if the documented ChatGPT desktop Worktree option fits your local Git workflow. Its approval and sandbox controls also matter, but are not established here as unique to Codex.
Choose Claude Code if you specifically want its documented Manual mode to ask before most file edits, shell commands, and network access. Codex also has approvals and sandbox settings, so compare the settings for your intended environment.
Choose a direct API if you are integrating model behavior into your own software. Compare API model features and usage costs separately from agent subscriptions; GPT-6 Astra’s documented tool-calling surface is Responses, while the available Claude Opus 5.5 material flags a limitation for forced tool use and does not settle every structured-output applicability condition.
If coding quality decides it, test both on the same representative repository task. Product documentation describes workflows and controls, not which agent will deliver the better result on your codebase.