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.

Gemini Deep Think API: Argon Integration and Availability

Direct answer

Google documents Gemini 4 Argon (gemini-4-argon) for API-based reasoning, with thinkingLevel settings of low, medium, and high. Google’s Gemini Apps help says Deep Think is being replaced by Gemini 4 Argon. These sources establish Argon as a documented API option and successor, but do not establish that a separate API call exactly reproduces the former app’s Deep Think mode. The Interactions API documentation also mentions background processing for Deep Think-like tasks; that mention alone does not identify a separate Deep Think model or prove equivalence. (Argon model documentation, Gemini Apps help, Interactions API)

Make a Gemini 4 Argon API request

The following is a documented REST example, not a tested request. The model page identifies gemini-4-argon as stable; the example uses the v1beta API path. To get a key, open Google AI Studio’s API-key page and create or select a key associated with a project. New users may first need to accept the terms, after which AI Studio can create a default project and key. For Argon, which is not on the free tier, Google’s billing setup documentation describes linking a billing account and adding at least US$5 (or the equivalent in another currency) in prepaid credits to move to the paid tier. Store the key as GEMINI_API_KEY on your server and send it in the x-goog-api-key header; do not commit it to source control. (API-key setup, billing setup, Argon model documentation, Argon generateContent example, text-generation documentation)

export GEMINI_API_KEY="your_api_key"

curl --fail-with-body --silent --show-error \
  "https://generativelanguage.googleapis.com/v1beta/models/gemini-4-argon:generateContent" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -X POST \
  -d '{
    "contents": [{
      "parts": [{
        "text": "Review this design for failure modes. List the three highest-risk assumptions and a test for each."
      }]
    }],
    "generationConfig": {
      "maxOutputTokens": 4096,
      "thinkingConfig": { "thinkingLevel": "high" }
    }
  }' \
  -o response.json

The response text is in candidates[].content.parts[].text. For example, use jq to extract text while reporting an error if the response has no candidate text:

jq -er '[.candidates[]?.content.parts[]? | .text? // empty] |
  if length == 0 then error("No candidate text in response") else join("\n") end' \
  response.json

curl --fail-with-body returns an error for an unsuccessful HTTP response; the jq check handles a successful response that contains no candidate text. (Text-generation REST example and response handling)

Choose a thinking level for the task: Argon supports low, medium, and high; medium is the documented default. These are API settings for Argon. They do not establish parity with the Gemini app’s former Deep Think mode. (Argon model documentation, thinking documentation)

When a request may take longer

Google documents background execution for standard Gemini models, including Gemini 4 Argon. For long-running tasks, the Interactions API supports background processing; that API is currently in beta. This is a documented execution option, not a separate Deep Think model interface. (Background execution, Interactions API)

Is `thinkingLevel: "high"` the same as Deep Think?

That equivalence is not established by the documentation cited here. Google describes Argon as replacing Deep Think in Gemini Apps and separately documents Argon’s API thinking levels. Neither statement says that setting high recreates the former app mode exactly. A Google AI Developers Forum thread asked whether an earlier model’s high-thinking setting matched app Deep Think, but the captured thread did not provide a substantive answer. A user’s impression based on response speed is not proof either way. (Gemini Apps help, thinking documentation, forum thread)

API pricing and a cost example

Google lists Gemini 4 Argon at US$2 per 1 million input tokens and US$10 per 1 million output tokens through November 30, 2026. The listed rates from December 1, 2026 are US$4 per 1 million input tokens and US$20 per 1 million output tokens. Google’s pricing documentation says Argon is not available on the free tier and that processed tokens, including thinking tokens, are counted and billed. The estimate below assumes 8,000 billable output tokens; check Google’s current pricing details for the applicable token billing and rates. These are API charges, not a Gemini app subscription. (Argon model pricing, Gemini API pricing)

For a hypothetical request with 100,000 input tokens and 8,000 billable output tokens, the introductory-rate estimate is US$0.28: 100,000 × $2/1,000,000 = $0.20 for input, plus 8,000 × $10/1,000,000 = $0.08 for output. This is an arithmetic example, not a measured request; the estimate assumes the stated 8,000 tokens are the billable output total.

Bottom line

For a documented Gemini API integration using advanced reasoning, use gemini-4-argon and choose a supported thinkingLevel. Google identifies Argon as the successor to Deep Think in Gemini Apps, but the documentation cited here does not establish that the API reproduces the former app mode one-to-one. Use the documented generateContent request for synchronous calls; consider Interactions API background execution for longer-running work, noting its beta status. Recheck rates before launch, especially for use after November 30, 2026.