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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 plus an agentic system called Groq Compound with built-in search and code execution.
Example models: GPT-OSS 120B, Qwen 3.6 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 3.6 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 | ~40% fewer tokens than full rewrites |
| Customization | No fine-tuned model hosting | Fine-tuning offered |
| Deployment | GroqCloud API | OpenAI-compatible API |
| 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: ~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.