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Groq vs Morph
Morph is a code-edit merge model running at 10,500+ tokens per second. Groq is a general fast host. Both chase speed, for different steps of a coding agent.
By The Subconscious Team · Updated
Groq vs Morph: key differences
Morph and Groq both sell speed, but only Groq sells general inference. Morph's Fast Apply takes an edit snippet from a larger model and merges it into the full file at 10,500+ tokens per second with up to 98% accuracy, using a 7B model trained only on code merging. Groq runs general open models like GPT-OSS 120B at 500 tokens per second. A coding agent could plan on a model served by Groq and apply the resulting edits with Morph, getting a fast loop end to end.
The limits are clear on each side. Morph is a narrow tool that complements a main model rather than replacing an inference provider, and its 2 to 4% merge error rate still calls for tests or linting. Groq's catalog is small, its context caps around 131K, and it hosts no fine-tunes, while Morph offers fine-tuning plus WarpGrep search and Compact context compression. Morph also cuts the frontier model's output tokens by about 40% against full rewrites, a saving Groq cannot deliver on its own.
What Groq and Morph do
Groq
Groq serves open models on its own chip, the LPU, which keeps model weights in on-chip SRAM and runs a deterministic schedule instead of waiting on GPU memory. Groq publishes 1,000 tokens per second on GPT-OSS 20B and 500 on GPT-OSS 120B. Latency stays tight between median and tail, which matters for strict SLAs. The API is OpenAI compatible and also hosts Whisper for speech to text. Groq retired its Compound agent system in September 2026, and GPT-OSS 120B now carries built-in browser search and code execution instead.
Example models: GPT-OSS 120B, Qwen 3.8 27B
Full Groq 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 Groq or Morph?
Groq vs Morph at a glance
| Attribute | Groq | Morph |
|---|---|---|
| Model access | Open weights | Specialist models |
| Flagship models | GPT-OSS 120B, Qwen 3.8 27B | morph-v3-fast, morph-v3-large |
| Speed | 500–1,000 tok/s | 10,500+ tok/s Fast Apply |
| Price | Near the floor on small models | $0.80 in, $1.20 out (morph-v3-fast) |
| Customization | No fine-tuned model hosting | Fine-tuning offered |
| Deployment | GroqCloud API | OpenAI compatible API; self-hosted on Enterprise |
| Long context | Around 131K max | Unknown |
Frequently asked questions
What is the difference between Groq and Morph?
Morph is a code-edit merge model running at 10,500+ tokens per second. Groq is a general fast host. Both chase speed, for different steps of a coding agent.
When should I choose Groq over Morph?
Fast planning and chat steps in coding agents; General open-model inference at high speed; Voice front ends for developer tools.
When should I choose Morph over Groq?
Merging edits into large files quickly; Reducing output tokens from the main model; Agentic repo search with WarpGrep.
Is Groq or Morph cheaper?
Groq: Near the floor on small models. 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.