GPT-5.6 is now on Token Station: try it in your coding agent routes

GPT-5.6 is now available on Token Station.

For coding-agent teams, what matters is that GPT-5.6 can be tested through the routes developers already use: a direct OpenAI-compatible API and OpenAI Codex-style workflows.

That turns GPT-5.6 from a launch announcement into something you can actually route, compare, and adopt without rebuilding your agent stack.

What Token Station supports

Token Station exposes the GPT-5.6 family through direct OpenAI-compatible routes:

• openai/gpt-5.6
• openai/gpt-5.6-sol
• openai/gpt-5.6-terra
• openai/gpt-5.6-luna

OpenAI Codex-style workflows use these same openai/ routes, so there is no separate Codex route to set up.

The result is simple: you can test the same model family from the surface that matches your workflow instead of treating each provider interface as a separate integration project.

Try GPT-5.6 from one endpoint

The endpoint is the standard Token Station OpenAI-compatible API:

curl https://models.bytefuture.ai/v1/chat/completions \
  -H "Authorization: Bearer TOKEN_STATION_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-5.6-sol",
    "messages": [
      {"role": "user", "content": "Plan a safe refactor for a pricing module and list the tests to run."}
    ]
  }'

To switch variants, change only the model field:

{
  "model": "openai/gpt-5.6-terra"
}

Your application still talks to the same endpoint. The route changes; the integration stays stable.

GPT-5.6 Sol, Terra, and Luna

A coding agent is not one API call. A real session may include planning, repository search, patch generation, test repair, code review, and delegated subtasks. Those steps do not all need the same model tier.

The three named variants give teams a useful testing ladder:

• GPT-5.6 Sol: the flagship route for the hardest coding-agent steps.
• GPT-5.6 Terra: a middle route for repeated implementation and debugging loops.
• GPT-5.6 Luna: a lower-cost route for exploration, triage, and subtask fan-out.

A practical routing pattern looks like this:

• Use a cheaper route for exploration, triage, and repeated iteration.
• Move to a stronger route for hard reasoning, risky patches, and final review.
• Keep the endpoint stable so your harness, agent, and evaluation scripts do not need to change every time you compare a model.

Agent workloads are uneven. A repository-wide plan, a subtle failing test, and a boilerplate file edit should not automatically use the same cost tier. Model choice becomes a route name, not a new integration project.

Pricing and cache accounting

Token Station exposes GPT-5.6 pricing in the categories agent operators actually need to track: input, output, cached input, cache writes, and long-context tiers above 272K input tokens.

For coding agents, cache accounting matters because repository context repeats. Agents send file summaries, diffs, test output, task state, and previous plans again and again. Prompt caching can lower repeated-context cost, but only if cache writes and cached reads are handled separately.

Token Station normalizes GPT-5.6 cache_write_tokens usage so cache writes are charged in the cache-creation bucket without double-counting them as ordinary input tokens.

A practical price frame inside Token Station (GPT-5.6 rates are for up to 272K input tokens):

Above 272K input tokens, GPT-5.6 uses a long-context tier: Sol doubles input and cached-input prices and moves output to $45/M; Terra moves to $5/M input and $22.50/M output; Luna moves to $2/M input and $9/M output.

How to compare GPT-5.6 with Claude Fable 5

Claude Fable 5 is still a natural comparison point for long-running coding agents. It is configured in Token Station with a 1M context window and a higher $10/$50 price profile.

GPT-5.6 brings a different operating shape: OpenAI-native routes, Codex-style surfaces, and multiple price tiers in the same family.

A simple starting point:

• Reach for the GPT-5.6 family when you want OpenAI-native routes, Codex-style surfaces, or a cheaper tier for high-volume steps. Pick Sol, Terra, or Luna by task, as above.
• Reach for Claude Fable 5 when you specifically want Anthropic’s long-running-agent behavior and its 1M context, and are willing to pay the higher rate.
• Compare them inside Token Station when workflow fit matters more than model-brand loyalty.

Try GPT-5.6 through Token Station

If you already use Token Station, GPT-5.6 is now another route family you can test in your coding-agent workflow.

Start with the direct OpenAI route for a simple API check. Use the same openai/ route in your Codex setup for terminal coding tasks.

Token Station gives you one place to compare those routes without rewriting your agent stack for every model launch.

Try Token Station