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Cerebras vs Morph
Morph is a specialist that merges coding edits at 10,500+ tokens per second. Cerebras is a general host that generates tokens fast. They stack.
By The Subconscious Team · Updated
Cerebras vs Morph: key differences
Each sells speed, at a different step of a coding agent. Cerebras generates text fast in general, with GPT-OSS 120B near 3,000 tokens per second. Morph does one job extremely fast: a 7B model trained only on code merging takes a big model's edit snippet and applies it to the full file at 10,500+ tokens per second with up to 98% accuracy. Morph says this cuts tokens about 40% against full-file rewrites. Its lineup also includes WarpGrep for repo search, Compact for context compression and Reflex for classification.
They are not substitutes. Morph's own profile calls it a narrow tool that complements a main model. Cerebras could serve that main model when its shared catalog fits, though GPT-OSS 120B and Gemma 4 31B are the only shared options as of August 2026. A fast coding stack could run planning on Cerebras and merges on Morph, with tests to catch the 2 to 4% of merges that still fail.
What Cerebras and Morph do
Cerebras
Cerebras builds a single chip the size of a silicon wafer, and its inference cloud is the fastest public host on the models it serves. Its developer table lists GPT-OSS 120B near 3,000 tokens per second, about six times Groq on the same weights, at $0.35 in and $0.75 out per million. The public shared catalog is thin, just GPT-OSS 120B and Gemma 4 31B as of August 2026. More model families live on dedicated endpoints and through partners like OpenRouter, Hugging Face, Vercel and AWS Marketplace.
Example models: GPT-OSS 120B, Gemma 4 31B
Full Cerebras 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 Cerebras or Morph?
Cerebras
Choose Cerebras for
- Fast general generation for the main coding model
- Live code autocomplete
- Long outputs on GPT-OSS 120B
Morph
Choose Morph for
- Applying edits to large files at 10,500+ tokens per second
- Cutting output tokens from full-file rewrites
- Repo search and context compression beside a main model
Cerebras vs Morph at a glance
| Attribute | Cerebras | Morph |
|---|---|---|
| Model access | Open weights | Specialist models |
| Flagship models | GPT-OSS 120B, Gemma 4 31B | morph-v3-fast, morph-v3-large |
| Speed | ~3,000 tok/s on GPT-OSS 120B | 10,500+ tok/s Fast Apply |
| Price | $0.35 in, $0.75 out (GPT-OSS 120B) | ~40% fewer tokens than full rewrites |
| Customization | Unknown | Fine-tuning offered |
| Deployment | Shared API, dedicated, partners | OpenAI-compatible API |
| Long context | Unknown | Unknown |
Frequently asked questions
What is the difference between Cerebras and Morph?
Morph is a specialist that merges coding edits at 10,500+ tokens per second. Cerebras is a general host that generates tokens fast. They stack.
When should I choose Cerebras over Morph?
Fast general generation for the main coding model; Live code autocomplete; Long outputs on GPT-OSS 120B.
When should I choose Morph over Cerebras?
Applying edits to large files at 10,500+ tokens per second; Cutting output tokens from full-file rewrites; Repo search and context compression beside a main model.
Is Cerebras or Morph cheaper?
Cerebras: $0.35 in, $0.75 out (GPT-OSS 120B). Morph: ~40% fewer tokens than full rewrites. 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.