vs
Groq vs Relace
Relace builds small, fast tool models for coding agents. Groq serves general open models fast. Relace handles the chores; Groq can handle the conversation.
By The Subconscious Team · Updated
Groq vs Relace: key differences
Relace's models are utilities. relace-apply-3 merges edits at about 10,000 tokens per second with 128K tokens of input and output, its compaction model runs at 50,000 tokens per second, and its agentic search explores big codebases in parallel. Groq serves general models, GPT-OSS and Qwen 3.8, on its LPU with steady latency. There is almost no overlap in function. A coding product might send user chat and reasoning to a Groq model and route apply and search calls to Relace.
Deployment and limits point in different directions. Relace can be self-hosted with guided onboarding for enterprises that keep code in-house, which Groq does not offer. Relace returns an error past 128K tokens, and Groq's own context caps around 131K, so neither handles very large files alone. Relace also offers source control with retrieval built in. Groq's extras are Whisper and built-in search and code execution on GPT-OSS 120B, run server-side.
What Groq and Relace 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 profileRelace
Relace started out training small, fast models that act as tools for coding agents, and by October 2026 its homepage led with open-model inference instead. It says it served 30 trillion tokens in the 30 days to late October and ranks among the top providers on OpenRouter. The hosted catalog covers DeepSeek V4 Pro, V4 Flash and V4.1 Flash, Kimi K3, GLM 5.3, GLM 5.3 Flash and GLM 5.2 over OpenAI and Anthropic compatible APIs, billed per token with no subscription. Prices run from $0.07 in and $0.14 out per million tokens on DeepSeek V4 Flash to $2.00 in and $14.00 out on Kimi K3, and Relace says it reprices often.
Example models: DeepSeek V4.1 Flash, Kimi K3, GLM 5.3, relace-apply-3
Full Relace profileShould you choose Groq or Relace?
Groq vs Relace at a glance
| Attribute | Groq | Relace |
|---|---|---|
| Model access | Open weights | Open weights, plus specialist coding models |
| Flagship models | GPT-OSS 120B, Qwen 3.8 27B | DeepSeek V4.1 Flash, Kimi K3, GLM 5.3, relace-apply-3 |
| Speed | 500–1,000 tok/s | ~10,000 tok/s apply |
| Price | Near the floor on small models | $0.07 in, $0.50 out (DeepSeek V4.1 Flash) |
| Customization | No fine-tuned model hosting | Unknown |
| Deployment | GroqCloud API | Hosted API, OpenRouter or self-hosted |
| Long context | Around 131K max | 128K on apply; varies by open model |
Frequently asked questions
What is the difference between Groq and Relace?
Relace builds small, fast tool models for coding agents. Groq serves general open models fast. Relace handles the chores; Groq can handle the conversation.
When should I choose Groq over Relace?
General chat and reasoning steps at high speed; Voice input with hosted Whisper; Agent loops on GPT-OSS or Qwen 3.8.
When should I choose Relace over Groq?
Applying AI edits in app builders; Fast codebase search for PR review; Self-hosted coding tools for in-house code.
Is Groq or Relace cheaper?
Groq: Near the floor on small models. Relace: $0.07 in, $0.50 out (DeepSeek V4.1 Flash). The cheaper choice depends on the model and workload.
Which has more context, Groq or Relace?
Groq: Around 131K max. Relace: 128K on apply; varies by open model.
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.