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Inception vs OpenScale
Inception sells closed diffusion LLMs that generate 1,000+ tok/s on standard GPUs. OpenScale sells open DeepSeek V4.1 Flash at low per-token prices.
By The Subconscious Team · Updated
Inception vs OpenScale: key differences
Inception's Mercury models generate many tokens in parallel by refining a draft over a few passes. Mercury 2.5 is a reasoning model with tool use and 260K context at $0.20 in and $0.75 out, with about 640 tokens per second measured by Artificial Analysis. It is also on AWS Bedrock and Azure Foundry. OpenScale, a Y Combinator-backed API out of Singapore, serves DeepSeek V4.1 Flash at $0.06 in, $0.003 cached and $0.24 out per million tokens under a 50% promotion, at 200+ tokens per second, with a fast tier at about twice the speed.
Inception is the pick when latency drives the product, as in autocomplete, voice agents or real-time chat. OpenScale is the pick for cheaper long-context calls on an open model, with a 1M token window and very low cached-input prices.
What Inception and OpenScale do
Inception
Inception builds diffusion large language models (dLLMs). Every other LLM on this list generates text one token at a time; Inception's Mercury models start from a rough draft of a whole block and refine it over a few passes, producing many tokens in parallel. That ties speed less to output length and lets the models run fast on ordinary NVIDIA GPUs rather than custom chips. The company was founded by professors from Stanford, UCLA and Cornell behind core work on diffusion, Flash Attention and DPO, and is led by Stanford's Stefano Ermon. It raised a $50M seed in November 2025 led by Menlo Ventures, with Mayfield, M12, NVentures, Snowflake and Databricks, plus Andrew Ng and Andrej Karpathy as angels.
Example models: Mercury 2.5, Mercury 2
Full Inception profileOpenScale
OpenScale is a Y Combinator-backed inference API built by LatentSpace and run out of Singapore. It sells open models behind one endpoint that speaks Chat Completions, the OpenAI Responses API and Anthropic Messages, so Claude Code, Codex, OpenCode, Cline, GitHub Copilot, Zed and other coding agents can point at it with a base URL and key. Its public catalog today is DeepSeek V4.1 Flash in two versions, standard and fast, both with a 1M token context window and up to 384K output tokens.
Example models: DeepSeek V4.1 Flash
Full OpenScale profileShould you choose Inception or OpenScale?
Inception vs OpenScale at a glance
| Attribute |  Inception | OpenScale |
|---|---|---|
| Model access | Closed weights (Mercury dLLMs) | Open weights |
| Flagship models | Mercury 2.5, Mercury 2 | DeepSeek V4.1 Flash |
| Speed | 1,100+ tok/s (vendor); ~640 measured | 200+ tok/s; fast tier ~2x |
| Price | $0.20 in, $0.75 out (Mercury 2.5) | $0.06 in, $0.24 out (promo, DeepSeek V4.1 Flash) |
| Customization | Fine-tuning on request | N/A |
| Deployment | Serverless API, Bedrock, Azure, private | Serverless API |
| Long context | 260K | 1M context |
Frequently asked questions
What is the difference between Inception and OpenScale?
Inception sells closed diffusion LLMs that generate 1,000+ tok/s on standard GPUs. OpenScale sells open DeepSeek V4.1 Flash at low per-token prices.
When should I choose Inception over OpenScale?
Latency-critical loops like autocomplete and voice; Very fast output on standard GPUs; Buying through Bedrock or Azure.
When should I choose OpenScale over Inception?
Lower per-token prices; 1M token context; Open weights instead of a single lab's closed model.
Is Inception or OpenScale cheaper?
Inception: $0.20 in, $0.75 out (Mercury 2.5). OpenScale: $0.06 in, $0.24 out (promo, DeepSeek V4.1 Flash). The cheaper choice depends on the model and workload.
Which has more context, Inception or OpenScale?
Inception: 260K. OpenScale: 1M context.
Related comparisons
Running long-horizon agents?
If your agents run past 200K tokens, compare both against Subconscious. Our inference stack treats a long-horizon trace as the primary workload, so speed, cost, and accuracy hold up deep into the trace.