vs
Meta vs Morph
Not substitutes. Muse Spark can plan and write code for an agent; Morph's small models apply those edits to files at 10,500+ tokens per second.
By The Subconscious Team · Updated
Meta vs Morph: key differences
Meta pitches Muse Spark at agentic coding, tool use and computer use, which makes it a candidate for the main model in a coding agent. Morph sits next to that model. Its Fast Apply takes the changed lines a large model writes, marked with existing-code comments, and merges them into the full file at 10,500+ tokens per second with up to 98% accuracy. Around it, Morph offers WarpGrep for repository search, Compact for context compression and Reflex for classification, plus fine-tuning. Muse Spark writes the plan and the diff. Morph applies it.
Together they cut cost on the expensive part of the bill. Muse Spark 1.3 output costs $4.25 per million, and Morph reports about 40% fewer tokens when the big model writes diffs instead of full-file rewrites. Morph's approach also avoids brittle search-and-replace tool calls. Its 2 to 4% merge error rate means tests or linting should check edits before they ship. Meta's API is new, so teams should keep a fallback model in mind, which Morph's model-agnostic apply step makes easy.
What Meta and Morph 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 launched the Meta Model API with Muse Spark 1.1, a multimodal reasoning model aimed at agentic coding, tool use and computer use. At Meta Connect in September, Meta made the API generally available globally. Muse Spark 1.3 has a 1M token context and costs $1.25 in and $4.25 out per million tokens, with cached input at $0.15. Muse Wonder, released October 12, 2026, sits above it for longer, harder agent work at $4 in and $20 out, also with 1M context. The endpoint speaks OpenAI Chat Completions, Anthropic Messages and a stateful agentic format.
Example models: Muse Wonder, Muse Spark 1.3, Muse Spark Open
Full Meta profileMorph
Morph builds small, very fast specialist models that sit beside a big coding model inside an agent. Its flagship is Fast Apply. The frontier model writes only the changed lines with // ... existing code ... markers, and Morph merges them into the full file at 10,500+ tokens per second with up to 98% accuracy. It is the same idea behind Cursor's instant apply, offered as an OpenAI compatible API.
Example models: morph-v3-fast, morph-v3-large
Full Morph profileShould you choose Meta or Morph?
Meta vs Morph at a glance
| Attribute | Meta | Morph |
|---|---|---|
| Model access | Closed API; open Muse Spark Open and Glimmer | Specialist models |
| Flagship models | Muse Wonder, Muse Spark 1.3, Muse Spark Open | morph-v3-fast, morph-v3-large |
| Speed | ~145–233 tok/s on Muse Spark 1.3 | 10,500+ tok/s Fast Apply |
| Price | $1.25–$4 in, $4.25–$20 out; Contributor tier cheaper | $0.80 in, $1.20 out (morph-v3-fast) |
| Customization | Open Muse weights to fine-tune | Fine-tuning offered |
| Deployment | Meta Model API, Oracle Cloud | OpenAI compatible API; self-hosted on Enterprise |
| Long context | 1M | Unknown |
Frequently asked questions
What is the difference between Meta and Morph?
Not substitutes. Muse Spark can plan and write code for an agent; Morph's small models apply those edits to files at 10,500+ tokens per second.
When should I choose Meta over Morph?
The planning and editing model in a coding agent; Tool use and computer use in the same model; Multimodal input with 1M context.
When should I choose Morph over Meta?
Applying edits to large files at high speed; Cutting output tokens versus full rewrites; Repository search and context compression.
Is Meta or Morph cheaper?
Meta: $1.25–$4 in, $4.25–$20 out; Contributor tier cheaper. Morph: $0.80 in, $1.20 out (morph-v3-fast). The cheaper choice depends on the model and workload.
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.