Build ROI positive agents for your product
Power your agents with open models, enhanced agentic workloads. Far cheaper, faster, and able to keep going long past the context window.
- 50%+
- Lower cost
- on long tasks
- 2x
- Faster
- task completion
- 5m+
- Extended context
- tokens in one session
$1,000 of inference buys
Assume one session is 750 steps. Subconscious allows more workloads with faster speed and higher accuracy due to context compression.
Long-context reasoning at a fraction of the cost.
Every turn of an agent resends the whole thread, and a standard provider bills all of it. Our runtime compresses the spans that stopped mattering before they are billed, and caches both sides of each cut. You pay for what it processes, not what you send.
1B tokens of agent work
Buy the same billion tokens three ways.
= $10$100 a row, every column filling at one rate
$03x ours
Claude Opus 5
Frontier list price, and every resent token is metered
1.4B tokens metered
$01.4x ours
GLM-5.3, hosted elsewhere
The same rate card as ours, without context compression
1.4B tokens metered
$0what you pay
GLM-5.3 Marathon on Subconscious
Spans that stopped mattering are compressed before they bill
1B tokens metered
Put it the other way round and $1,000 buys 1.2B on Opus 5, 2.5B on GLM-5.3 elsewhere, and 3.5B on us.
Priced on the token mix we measure across our own agentic traffic: 98.2% cached input, 1.6% uncached input, 0.2% output. Rows without context compression meter 40% more tokens for the same work, because the harness re-sends a thread that grows every step. The GLM-5.3 row runs on our own published rate card, so the gap to $287 is compression, not a cheaper model. Opus 5 is quoted at its base input rate rather than the higher cache-write rate, which understates the 3x gap.
Speed and accuracy that hold deep into a run.
General-purpose runtimes slow down as context grows, and models get noisier as the thread fills with stale tool output. Compressing that noise at runtime keeps generation fast and gives the model a cleaner view of the task.
- 2x
- Faster task completion
- Measured at 200k+ tokens of context, same model and hardware.
- 46%
- DeepSWE accuracy for GLM-5.2
- Against 44% for the same model on a standard provider, at a lower cost per task.
DeepSWE (Long-Horizon Agentic Coding Benchmark)
Better accuracy, at a lower cost per task.
Sessions that keep going, and the agents that unlocks.
Runs keep their history past the model’s window instead of hitting a wall or being compacted into a summary that drops what mattered. Once cost and context stop being the ceiling, a different class of agent becomes shippable.
Data-heavy agents
Research, coding, and document understanding, where the thread grows all run.
Multi-modal agents
Browser automation, computer use, and image generation orchestration.
Workflow automation
Long, multi-step runs fanning out across MCP servers and internal tools.
Latency-sensitive agents
Translation, voice agents, and control flows a user is waiting on.
Trace tokens
0.00
vLLM processed
0.00
Subconscious processed
0.00
Tokens avoided
0.00
0.0% less
A base URL, a key, and a model string.
Every model is served in both the OpenAI and the Anthropic format. Pick the one your agent already speaks, and the request below is the whole change.
API format
Language
Endpoint
https://api.subconscious.dev/v1/chat/completions
Model
subconscious/glm-5.3-marathon
Works with
OpenAI SDK
Intact
Tool calls, streaming, structured output
from openai import OpenAI
client = OpenAI(
base_url="https://api.subconscious.dev/v1",
api_key="YOUR_API_KEY",
)
response = client.chat.completions.create(
model="subconscious/glm-5.3-marathon",
messages=[
{
"role": "user",
"content": "Refactor the billing module and update every affected test.",
}
],
)
print(response.choices[0].message.content)Enhance your agents today.
Sign up, mint a key, change the base URL. Feel the difference in minutes.
What people say after their first run.
“It was faster than a lot of LLMs I've used, and it was easy to set up.”
Cedric Prentice
Software Engineer @ Wayfair
“Very cool, easy to use!”
Bill Simmons
Co-Founder @ Orbit.me
“Very interesting alternative to OpenAI”
Dave Gogi
Founder @ Signal X
“Pretty fast and fun to use”
Nihir Kothari
Founder @ Sidekick Software
“It worked very well and provided accurate descriptions of the images we passed in. The API costs were very cheap.”
Sam Mayle
Researcher @ Mitsubishi Electric Research Lab
“It was faster than a lot of LLMs I've used, and it was easy to set up.”
Cedric Prentice
Software Engineer @ Wayfair
“Very cool, easy to use!”
Bill Simmons
Co-Founder @ Orbit.me
“Very interesting alternative to OpenAI”
Dave Gogi
Founder @ Signal X
“Pretty fast and fun to use”
Nihir Kothari
Founder @ Sidekick Software
“It worked very well and provided accurate descriptions of the images we passed in. The API costs were very cheap.”
Sam Mayle
Researcher @ Mitsubishi Electric Research Lab
“It was faster than a lot of LLMs I've used, and it was easy to set up.”
Cedric Prentice
Software Engineer @ Wayfair
“Very cool, easy to use!”
Bill Simmons
Co-Founder @ Orbit.me
“Very interesting alternative to OpenAI”
Dave Gogi
Founder @ Signal X
“Pretty fast and fun to use”
Nihir Kothari
Founder @ Sidekick Software
“It worked very well and provided accurate descriptions of the images we passed in. The API costs were very cheap.”
Sam Mayle
Researcher @ Mitsubishi Electric Research Lab
“It was faster than a lot of LLMs I've used, and it was easy to set up.”
Cedric Prentice
Software Engineer @ Wayfair
“Very cool, easy to use!”
Bill Simmons
Co-Founder @ Orbit.me
“Very interesting alternative to OpenAI”
Dave Gogi
Founder @ Signal X
“Pretty fast and fun to use”
Nihir Kothari
Founder @ Sidekick Software
“It worked very well and provided accurate descriptions of the images we passed in. The API costs were very cheap.”
Sam Mayle
Researcher @ Mitsubishi Electric Research Lab
“Made the whole process very easy!”
Smruthi Ramesh
Lead Data Scientist @ Schneider Electric
“It was really fast!!”
Inder Singh
UDE
“Easy to use, great UI”
Yassine Fatimi
Founder @ ClauseGuard
“Pretty easy to use. No brainer. Easy drop in for OpenAI.”
Hansen Liang
Founder @ stealth
“Made the whole process very easy!”
Smruthi Ramesh
Lead Data Scientist @ Schneider Electric
“It was really fast!!”
Inder Singh
UDE
“Easy to use, great UI”
Yassine Fatimi
Founder @ ClauseGuard
“Pretty easy to use. No brainer. Easy drop in for OpenAI.”
Hansen Liang
Founder @ stealth
“Made the whole process very easy!”
Smruthi Ramesh
Lead Data Scientist @ Schneider Electric
“It was really fast!!”
Inder Singh
UDE
“Easy to use, great UI”
Yassine Fatimi
Founder @ ClauseGuard
“Pretty easy to use. No brainer. Easy drop in for OpenAI.”
Hansen Liang
Founder @ stealth
“Made the whole process very easy!”
Smruthi Ramesh
Lead Data Scientist @ Schneider Electric
“It was really fast!!”
Inder Singh
UDE
“Easy to use, great UI”
Yassine Fatimi
Founder @ ClauseGuard
“Pretty easy to use. No brainer. Easy drop in for OpenAI.”
Hansen Liang
Founder @ stealth
Outgrow the API?
Take control and get maximum cost savings. We set up a dedicated endpoint tuned to your workload, in our cloud or inside yours.
Talk to us about dedicated- Models
- Any open model, or bring your own
- Capacity
- Dedicated GPUs, no rate limits
- Deployment
- Our cloud, or inside yours
- Billing
- Per-GPU under management