Pay 6.9× less than Claude Opus, and 3.0× less than standard GLM-5.2.

On standard inference vs. Subconscious, with runtime context compression.

Less Tokens

More Tokens

Short

Med

Long

XL

Max

75K

375K

750K

1.5M

5M

Claude Opus

$136.76

GLM-5.2

$59.65

GLM-5.2 Marathon

$19.88

Standard inferenceSubconscious
Retained context per step0500KContext window limitContext compression ceiling1500 stepsRetained context tokens

Effective context

750K tokens

Compaction stalls

0

Tokens billed

187.9M → 62.4M

Context ceiling

150K

1,500 tokens per step, assuming 95% cache hits on input. Baselines compact at a 800K context window; Subconscious holds a 150K ceiling with runtime context compression.

Chatbots respond with one answer.An agent can work across millions of tokens and hundreds of steps.

We built an opinionated inference system that makes long-horizon agent work possible at scale with the same models and frameworks you already use.

You need to cut your AI spend, but you really want more. Now you can have it all.

We serve models better for agents

Open models in, powerful agents out.

The best open models are compatible with our our inference system. Use our API or host on your own infrastructure, then plug straight into your coding agents and agentic products.

Open models

GLM logoGLMQwen logoQwenNemotron logoNemotronGemma logoGemmaDeepSeek logoDeepSeekGPT-OSS logoGPT-OSSKimi logoKimiMuse logoMuse

Inference system

Subconscious

Coding agents

Claude CodeClaude CodeCursorCursorCodexCodexOpenCodeOpenCodePiPi

Agentic products

LangChainLangChainMastraMastran8nn8nStack AIStack AICrewAICrewAI

Try the API

Integrate in three lines of code.

To get started quickly, we serve our own TIM models + OrangeLine runtime behind OpenAI and Anthropic compatible APIs.

from openai import OpenAI

client = OpenAI(
    base_url="https://api.subconscious.dev/v1", # Step 1: point to our API
    api_key="YOUR_API_KEY", # Step 2: add your API key
)

response = client.chat.completions.create(
    model="glm-5.2-marathon", # Step 3: use one of our hosted models
    messages=[
      {
        "role": "user",
        "content": "Write a landing page geared towards developers in Boston."
      }
    ],
)

print(response.choices[0].message.content)
from anthropic import Anthropic

client = Anthropic(
    auth_token="YOUR_API_KEY",
    base_url="https://api.subconscious.dev",
)

message = client.messages.create(
    model="glm-5.2-marathon",
    max_tokens=1024,
    messages=[
    {
      "role": "user",
      "content": "Write a landing page geared towards developers in Boston."
    },
  ],
)

print(message.content[0].text)
import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: 'https://api.subconscious.dev/v1',
  apiKey: 'YOUR_API_KEY',
});

const response = await client.chat.completions.create({
  model: 'glm-5.2-marathon',
  messages: [
    {
      role: 'user',
      content: 'Write a landing page geared towards developers in Boston.'
    },
  ],
});

console.log(response.choices[0].message.content);
import Anthropic from '@anthropic-ai/sdk';

const client = new Anthropic({
  authToken: 'YOUR_API_KEY',
  baseURL: 'https://api.subconscious.dev',
});

const message = await client.messages.create({
  model: 'glm-5.2-marathon',
  max_tokens: 1024,
  messages: [
    {
      role: 'user',
      content: 'Write a landing page geared towards developers in Boston.'
    },
  ],
});

console.log(message.content[0].text);
curl https://api.subconscious.dev/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "glm-5.2-marathon",
    "messages": [
      {
        "role": "user",
        "content": "Write a landing page geared towards developers in Boston."
      }
    ]
  }'

Works anywhere you use the OpenAI completions or Anthropic messages APIs including OpenCode, Claude Code, Cursor, Codex, Pi, OpenHands, LangChain, Mastra, Vercel AI SDK, n8n, and more.

Try the API for free →

Performance

Measurable improvements with Subconscious

Standard inference systemsSubconscious
  • 5M+

    Token context window

    Enables agents to understand more data. It automatically compresses the KV cache at runtime, so it can explore more information.

  • 2.3×

    Concurrent workloads on the same hardware

    Enables more agents to run on the same GPUs. More concurrency means better economics.

  • 3.5×

    Faster token sustained throughput with long context

    Generates far more tokens with the same GPU, especially with 100k+ tokens of context.

Subconscious vs. SGLang, measured on identical hardware and models

Accuracy

Compression doesn't lose accuracy. Amazingly, it improves it.

In our testing, runtime compression raised accuracy for GLM-5.2 on the DeepSWE benchmark for long-context agentic coding.

DeepSWE (Long-Horizon Agentic Coding Benchmark)

36%40%44%$3.00$3.50$4.00Cost per task →Accuracy →GLM 5.2 (high)$2.85 · 36%GLM 5.2 (max)$3.92 · 44%GLM 5.2 (max) on Subconscious$2.79 · 46%

Get better accuracy, at a lower cost.

How it works

We built an inference system for agent workloads.

We view agent workloads as the primary workload for better performance and reliability.

  • Runtime context compression

    As an agent's context fills, we compress less relevant steps at runtime. GPU memory frees up and accuracy holds, so long runs never slow down or degrade mid-task.

  • Hyper-efficient caching

    When the agent prunes mid-context, we reuse prefix and the suffix. Repeated context is never recomputed, so cost and latency stay flat deep into a run.

  • Additional proprietary enhancements

    A deeper stack of runtime optimizations we refine release over release, so the same GPUs keep doing more for your agents over time.

Intelligent, on-GPU context compression tuned for agents

vLLM (periodic compaction)Subconscious (compression at runtime)0128K256K384K512K0250500Agent turns →Retained context tokens →Context compression ceilingvLLM context window

Trace tokens

0.00

vLLM processed

0.00

Subconscious processed

0.00

Tokens avoided

0.00

0.0% less

Deployed wherever you run agents

Power your agents on fewer GPUs and get better performance.

  • For

    Coding Agents

    Power coding agents with our API.

    Your engineering team needs coding agents, now get frontier performance with on-premise hosted models. Swap the base URL and the same agents run faster with our TIM and OrangeLine runtime at a fraction of frontier pricing.

  • For

    Agentic Products

    Power agentic products with our API.

    You want to ship agents to customers. Our efficient runtime keeps AI costs nearly linear instead of quadratic across multi step processes, so you can launch to actual users beyond an internal demo and turn a profit.

  • For

    Edge Devices

    Run capable agents on edge devices, for the first time.

    You already have a GPU. Take our runtime alone or paired with our post-trained models, and run capable agents completely on-device. These workloads are not possible in memory and compute constrained environments without us.

  • For

    Inference clusters

    A drop-in replacement for vLLM and SGLang.

    You run inference on GPUs you control. Swap Subconscious in where your serving engine sits today and the same fleet can run more concurrent agents, finish long jobs that used to degrade, and push tokens out faster. That means more revenue per GPU for clouds and more capacity for enterprises.

Subconscious visualized

Longer runs and more concurrency on the same GPUs.

The runtime manages memory and context during long agent runs. The gains land exactly where agentic workloads hurt the most.

Handle millions of tokens with context management at runtime.

With the highly efficient Subconscious Cache, save 10x on cost at scale.

Run 2.3x as many workloads on the same compute footprint.

3.5x faster token throughput down deep reasoning chains.

SGLang

Context windowTokens processedmax

Limited to the model context window with compaction. Long tasks run with unnecessary context or compact necessary information.

Subconscious

Context windowTokens processedmax

Processes millions of tokens per run. The system compresses its context at runtime, so accuracy stays high at any scale.

Deploy in your cloud

Run agents in your own cloud, and keep every advantage.

Point your coding agents at open models on GPUs you control. Here is what that unlocks.

Lower cost

50 to 75% cheaper inference

Run frontier-class open models for 50 to 75% less than closed APIs, and 50% less than self-hosting without our runtime.

More throughput

3.5× faster on long traces

Generate tokens 3.5× faster down long reasoning traces, exactly where other systems slow to a crawl.

Longer horizon

5m+ context window size

Extend a model's usable context, so your agents keep going without losing the thread.

Protect your Data

Nothing leaves your cloud

Self-host and no prompt or token ever leaves your walls. Stop handing your IP to the frontier labs building to compete with you.

Nightshift tokens

Billions of extra tokens overnight

A dedicated endpoint already beats closed models prices by supporting day time usage.. Overnight, the same GPUs give you billions more tokens for PRs, code review, red-teaming, research, R&D, and more.

Your own models

Post-train with Subconscious

Context compression builds stronger models with less compute, tuned for long-context reasoning.

Questions

Frequently asked questions

  • OrangeLine is our specialized inference runtime designed for agent workloads. It caches and compresses tokens aggressively during processing directly on the GPU. As a result, our system extends the context window of the models it serves by 10x, enables 2.3x concurrent workloads running on the same hardware, and sustains 3.5x faster token throughput where general-purpose runtimes slow down. OrangeLine can serve LLMs, SLMs, and multimodal modals.
  • Most likely. OrangeLine is compatible with LLMs and Multimodal models. Our team has experimented extensively with the Qwen, GLM, Nemotron, and Kimi models, and many more open and closed models are compatible.
  • Subconscious GLM-5.2 is the open-source GLM-5.2 model served on our OrangeLine runtime, built for agentic coding. It is available via an OpenAI and Anthropic compatible API.
  • TIM-Qwen3.6-27B is our post-trained small language model running on our OrangeLine inference system. We took the already powerful Qwen3.6 27B model and significantly improved its capabilities with OrangeLine and our post-training process. We offer this system via an OpenAI completions and Anthropic messages compatible API.
  • Yes. The API supports OpenAI chat completions and Anthropic messages formats. If you have code that already uses the OpenAI or Claude SDK, you can point it at our endpoint and try our system with 3 lines of code.
  • Yes. Our API is compatible with any tool that uses the OpenAI chat completions or Anthropic messages format. Our documentation has pointers to get you started.
  • Yes. Our API is compatible with any tool that uses the OpenAI chat completions or Anthropic messages format. Our documentation has pointers to get you started.
  • Yes. Any framework that uses the OpenAI completions or Anthropic API works with Subconscious. Swap in our base URL and API key and you are up and running.
  • Yes. OrangeLine compresses context aggressively without losing reasoning quality, which changes the math on edge AI. With OrangeLine, the same device can run a larger model, complete longer context tasks, or do work that simply was not possible before. We are currently running on workstations like the Nvidia DGX Spark, laptops, and even mobile devices like iPhones and Samsung Galaxy phones. Sign up for our platform and head to the local devices tab to learn more.
  • Yes. We open source our post-trained models on Hugging Face. We do not, however, open source our proprietary inference runtime OrangeLine.
  • Yes. We offer dedicated GPU infrastructure with no rate limits, optional post-training on your tools and data, and custom SLAs. Sign up for our platform and head to the dedicated endpoint tab to get started.
  • No. Subconscious is a runtime optimization and post-training company. We develop our OrangeLine runtime and TIM family of post-trained models. We take open models and post-train them to improve their reasoning ability on policy with our OrangeLine runtime. For specific customers, we help them post-train models for their unique data and tooling.

Get started

Make your agents go further.

Same models, same hardware, more agents that run reliable, fast, and affordable for longer. Try our API or deploy Subconscious in your own cloud.

  • 5M+

    Token context window

  • 2.3×

    Concurrent workloads on the same hardware

  • 3.5×

    Faster token sustained throughput with long context

© 2026 Subconscious Systems Technologies, Inc.

Subconscious