01
Cheap models aren’t cheap
Benchmarks report price per million tokens and leave out cost per task. Smaller models spend more tokens to make up for weaker abilities.
Compare · Routers
Route Every Turn.
Not Every Task.
Routers patch your call through to one model for the whole task, like a 1920s switchboard. Parsec picks the right model at every turn, including models trained on your traffic.
- savings on real workloads
- 60%
- savings on real workloads
- typical router savings
- 15%
- typical router savings
- quality
- +5–15%
- quality
The nuance
Why router savings disappoint.
Anyone can save 90% when they stop using Fable, Astra or Kimi to write birthday cards. Real workloads land nearer 15%, for two reasons.
02
Cache economics
Switching models mid-task breaks the cache and you pay twice. A task that is 90% easy rides the expensive model the whole way.
The actual solution
Universal K/V cache. Post-trained teams.
01
Per-turn routing
Our universal K/V cache extends NVIDIA’s cross-model cache transfer research to any conversation length. The next model picks the same next token 89–95% of the time, and a switch costs 15–19% of a full re-read.
02
Models you own
Parsec deploys models post-trained on your traffic, not just public ones.
03
A team, not one model
Model A handles 90% of the conversation. Model B steps in for the hard moment. Efficiency and quality at once.
The simple truth
Parsec vs traditional routing.
| Parsec | Traditional routing | |
|---|---|---|
| Routing | Per turn | Per task |
| Models | Post-trained on your traffic | One size fits all |
| Model ownership | You | The labs |
| Responses | Differentiated | Commoditized |
| Savings on real workloads | 60% | 15% |
| Typical quality increase | 5–15% | Flat |
One Line. Then We Do the Rest.
First post-trained model in about 4 weeks. Full savings and quality gains within 3 months of moving production traffic.