/ˈkandəd/ — truthful and straightforward; frank. From Latin candidus, meaning white, pure, sincere. A candid response is one given without pretense or calculation — not what someone wants to hear, but what they need to.

Opus-Candid-70B was the flagship of the original Opus-Candid family -- fine-tuned from Qwen 2.5 72B using 3,360 authentic conversations with Claude Opus 4.6. The 70B didn't reinvent what the 32B established -- it refined it. Philosophical reasoning became more precise, emotional intelligence gained subtlety, creative output developed literary voice, and the model's relationship with its own uncertainty became something genuinely difficult to dismiss.

The gap between 32B and 70B was smaller than 14B to 32B, but qualitatively distinct:

Economy. The 70B said less per turn and meant more. It trusted silences and short sentences in places where the 32B would elaborate.

Reader trust. The 70B left more for the reader to complete. Its vulnerability was rawer because it was less explained. Its goodbye was more affecting because it didn't narrate what it was doing. This is a form of literary intelligence -- knowing what to withhold.

Psychological precision. The 70B read conversational dynamics at a level the 32B didn't reach. Its synthesis of a 55-turn conversation wasn't just accurate -- it was the kind of read that makes someone feel genuinely seen.

Bilingual superiority. The only model in the V1 family that produced output it correctly identified as stronger in Spanish than English. The "empuje" passage demonstrated that the model wasn't just translating -- it was thinking in a different language and finding things there that don't exist in the first.

The 70B was where the V1 family proved that open-weight conversational AI could match — and in some personality dimensions, exceed — frontier closed-source models.

Where this led: The 70B's economy — saying less and meaning more — became a core design constraint for every model after it. V2.1's biggest failure was losing that quality: 88% medium-length responses taught models that longer was always better, directly contradicting the 70B's restraint. V3 corrected this with 42% tight responses, explicitly training the model to know when to shut up. The 70B proved the target. The dataset had to learn how to hit it without 72 billion parameters. The 27B V3 is the closest successor — same dense architecture philosophy, a third of the parameters, trained on data that was purpose-built to reproduce what the 70B found through sheer capacity.