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Meta vs Inception
Meta sells its Muse models on a new closed API with 1M context. Inception sells closed diffusion LLMs built for speed.
By The Subconscious Team · Updated
Meta vs Inception: key differences
Meta's Muse Spark 1.3 is a multimodal reasoning model for coding, tool use and computer use, at $1.25 in and $4.25 out with 1M context, running around 145 to 233 tokens per second. A Contributor tier cuts that to $0.10 in and $0.20 out if Meta can train on your traffic. Inception's Mercury 2.5 costs $0.20 in and $0.75 out with 260K context, and its quality sits with cost-optimized models, not the frontier. Inception reports 1,100+ tokens per second; Artificial Analysis measures about 640.
Muse Spark fits multimodal agents and long context, and the API speaks both OpenAI and Anthropic formats, though it is still in preview. Inception is faster and cheaper at standard pricing for narrow, latency-critical tasks, and it is already sold through Bedrock and Azure.
What Meta and Inception do
Meta
Meta has moved from open Llama releases toward its own closed API. Meta Superintelligence Labs builds the Muse family, and in July 2026 Meta opened a public preview of the Meta Model API with Muse Spark 1.1, a multimodal reasoning model aimed at agentic coding, tool use and computer use. The current lineup runs through Muse Spark 1.3 with a 1M token context. Standard pricing is $1.25 in and $4.25 out per million tokens, with cached input at $0.15, and the endpoint speaks OpenAI Chat Completions, Anthropic Messages and a stateful agentic format.
Example models: Muse Spark 1.3, Muse Glimmer
Full Meta profile
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 profileShould you choose Meta or Inception?
Meta vs Inception at a glance
| Attribute | Meta |  Inception |
|---|---|---|
| Model access | Closed API; open Muse Glimmer | Closed weights (Mercury dLLMs) |
| Flagship models | Muse Spark 1.3, Muse Glimmer | Mercury 2.5, Mercury 2 |
| Speed | ~145–233 tok/s on Muse Spark 1.3 | 1,100+ tok/s (vendor); ~640 measured |
| Price | $1.25 in, $4.25 out; Contributor tier cheaper | $0.20 in, $0.75 out (Mercury 2.5) |
| Customization | Open Muse Glimmer weights to fine-tune | Fine-tuning on request |
| Deployment | Meta Model API (preview) | Serverless API, Bedrock, Azure, private |
| Long context | 1M | 260K |
Frequently asked questions
What is the difference between Meta and Inception?
Meta sells its Muse models on a new closed API with 1M context. Inception sells closed diffusion LLMs built for speed.
When should I choose Meta over Inception?
Multimodal agents with 1M context; Cheap prototyping on the Contributor tier; Image and transcription on the same key.
When should I choose Inception over Meta?
Very fast text output; Code autocomplete and voice agents; Buying through Bedrock or Azure.
Is Meta or Inception cheaper?
Meta: $1.25 in, $4.25 out; Contributor tier cheaper. Inception: $0.20 in, $0.75 out (Mercury 2.5). The cheaper choice depends on the model and workload.
Which has more context, Meta or Inception?
Meta: 1M. Inception: 260K.
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.