Fireworks releases Ember-1 model with 40% fewer reasoning tokens at same quality

Fireworks AI post-trained Kimi K3 into Ember-1, cutting reasoning tokens by ~40% without losing accuracy. K3 sometimes spends over 90% of tokens on internal reasoning, which compounds cost in multi-turn agent workloads. Ember-1 keeps useful self-correction but drops redundant loops. On Terminal Bench 2.1 it scores 82%, beating K3's max-effort setting by 1.1 points while costing 51.9% less. Only available via Fireworks serverless API—weights and training code are not released.

Fireworks Research released Ember-1, a model built on Kimi K3 that cuts reasoning tokens by 35–50% while keeping accuracy. Across 7 benchmarks and live A/B tests with two customers, quality held. The team ran 50+ training experiments and found K3 spends over 90% of tokens on internal reasoning, much of it unnecessary. Ember-1 preserves useful self-correction and skips unproductive loops. Savings compound in multi-turn agent tasks where prior reasoning is re-read each turn. The model is live on Fireworks' platform as the first in their own model series.