Between Luna at $0.1 and Opus at $0.4, the GPT lineup has two middle tiers: gpt-5.6-terra at $0.2 and gpt-5.6-sol at $0.3. Both cover ground where Luna isn't enough and the flagship is overkill.
Terra: a step up from Luna
Terra costs twice what Luna does, and reasons noticeably more confidently. Reach for it when the task is a notch past pure classification: explain a decision in a couple of sentences, reconcile data from a few sources, handle mixed input that doesn't fit a rigid template. It's a mid-pipeline model — accuracy matters, but you don't need multi-step logic.
Sol: the top GPT tier
Sol costs $0.3, the same as Sonnet. It's the strongest model in Claudexia's GPT lineup, and price-wise it's a direct competitor to the Claude mid-tier. Reach for Sol when you specifically want a GPT-family model: a different response style, different behavior on certain prompts, or compatibility with an existing pipeline already built around GPT.
Terra versus Sol: how to choose
Simple rule: if Terra solves the task, don't pay for Sol. Run a test set through both and compare errors, not general impressions of the output. If quality is indistinguishable, save the cents. If Terra keeps missing on edge cases, the $0.1 gap per million tokens pays for itself quickly in fewer retries.
Sol versus Sonnet: price isn't the argument
Sol and claude-sonnet-5 are priced the same: $0.3 per million tokens on each side. Choosing between them, like choosing between Opus revisions, comes down to behavior, not the bill. If your pipeline is already tuned around Claude, Sonnet's output style will feel more familiar. If you're keeping both families for resilience against one provider's outages, Sol is a solid backup at the same price.
A working example
from anthropic import Anthropic
client = Anthropic(
api_key="sk_cdx_your_key_here",
base_url="https://api.claudexia.tech",
)
message = client.messages.create(
model="gpt-5.6-sol",
max_tokens=1024,
messages=[{"role": "user", "content": "Merge these three reports into one table and flag the discrepancies."}],
)
print(message.content[0].text)
The same call over OpenAI Chat Completions differs only in the request body shape — the key and the base URL stay the same.
Mixing models in one pipeline
Nothing stops you from running Terra on a preprocessing step and Sol on the step that needs a sharper answer. One key works across the whole lineup, both API formats live on one endpoint, so switching a model is a one-line config change, not a new integration.
For example: Terra filters the incoming stream first and drops obvious noise, then Sol works through what's left and writes the full answer. High volume and price sensitivity on the first step, lower volume and accuracy on the second.
Comparing on your own task
The calculator on the site works out the bill difference between Terra, Sol, and Sonnet at your volumes. Per-key stats show which model is actually spending your budget in production, not in a test run.
In short
Terra for mid-pipeline steps that need accuracy without deep reasoning. Sol for a step up, at the same price as Sonnet. Between Sol and Sonnet, decide by model behavior, not by cost — the price is identical.