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Cerebras vs RunInfra
RunInfra offers cheap coding plans and auto-built endpoints for mid-size models. Cerebras offers top speed on a small open catalog.
By The Subconscious Team · Updated
Cerebras vs RunInfra: key differences
RunInfra is a young company with two products. Its hosted Model APIs serve a small library of mid-size models such as Nemotron 3.5 Lightning 30B and Qwen 3.8 27B, with coding plans from $10 a month for Claude Code, Codex and other agent CLIs. Its deployment agent takes a plain-English spec, benchmarks GPUs from L4 to B200, searches quantized variants and ships an endpoint that scales to zero with cold starts under two seconds. Cerebras skips configuration entirely and sells speed on its own chip, with GPT-OSS 120B near 3,000 tokens per second.
Both catalogs are small, so the decision turns on the job. RunInfra suits developers who want a flat-rate open model inside a coding tool, and small teams that need a tuned endpoint or a Whisper, LLM and TTS voice pipeline without ML ops staff. Cerebras suits teams whose users feel every token, such as voice agents and live autocomplete. RunInfra has little independent benchmarking or enterprise track record. Cerebras, founded in 2015, has OpenAI renting about 750 MW of its capacity.
What Cerebras and RunInfra 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 profileRunInfra
RunInfra pitches open models built for agents, with two ways in. Its hosted Model APIs serve a small curated library, including Nemotron 3.5 Lightning 30B, Qwen 3.8 27B and Ornith 1.5 35B, behind one key that works with both the OpenAI and Anthropic SDKs. Cached context bills at a discount. Coding plans start at $10 a month with limits that reset every five hours and every week, and they plug into Claude Code, Codex, OpenCode, Cline, Aider and dozens of other agent CLIs.
Example models: Nemotron 3.5 Lightning 30B, Qwen 3.8 27B
Full RunInfra profileShould you choose Cerebras or RunInfra?
Cerebras
Choose Cerebras for
- Voice agents that need the fastest output
- GPT-OSS 120B at about 3,000 tokens per second
- Buyers who want an established vendor
RunInfra
Choose RunInfra for
- Flat-rate open models inside Claude Code or Codex
- Auto-benchmarked, quantized endpoints without ML ops
- Voice pipelines chaining Whisper, an LLM and TTS
Cerebras vs RunInfra at a glance
| Attribute | Cerebras | RunInfra |
|---|---|---|
| Model access | Open weights | Open weights |
| Flagship models | GPT-OSS 120B, Gemma 4 31B | Nemotron 3.5 Lightning 30B, Qwen 3.8 27B |
| Speed | ~3,000 tok/s on GPT-OSS 120B | Cold starts under 2s |
| Price | $0.35 in, $0.75 out (GPT-OSS 120B) | Coding plans from $10 a month |
| Customization | Unknown | Uploads up to 50 GB; auto-quantization |
| Deployment | Shared API, dedicated, partners | Model APIs, agent-built endpoints |
| Long context | Unknown | Varies by model |
Frequently asked questions
What is the difference between Cerebras and RunInfra?
RunInfra offers cheap coding plans and auto-built endpoints for mid-size models. Cerebras offers top speed on a small open catalog.
When should I choose Cerebras over RunInfra?
Voice agents that need the fastest output; GPT-OSS 120B at about 3,000 tokens per second; Buyers who want an established vendor.
When should I choose RunInfra over Cerebras?
Flat-rate open models inside Claude Code or Codex; Auto-benchmarked, quantized endpoints without ML ops; Voice pipelines chaining Whisper, an LLM and TTS.
Is Cerebras or RunInfra cheaper?
Cerebras: $0.35 in, $0.75 out (GPT-OSS 120B). RunInfra: Coding plans from $10 a month. The cheaper choice depends on the model and workload.
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