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Cerebras vs Moonshot AI
Kimi K3 is near the frontier but runs around 33 tokens per second. Cerebras runs smaller open models near 3,000. Capability against speed.
By The Subconscious Team · Updated
Cerebras vs Moonshot AI: key differences
This pair captures the core trade in open models. Moonshot's Kimi K3 is the most capable open-weight model, scoring 93.4% on SWE-bench Verified in Vals AI's neutral harness, with native vision and 1M context. It also always thinks and runs around 33 tokens per second on Moonshot's API, at $3 in and $15 out. Cerebras serves GPT-OSS 120B near 3,000 tokens per second at $0.35 in and $0.75 out, but its shared catalog as of August 2026 holds only that model and Gemma 4 31B. Kimi K3 is not on it.
For long-horizon coding on huge repositories, where answer quality matters more than wait time, Kimi K3 is the stronger model, and cached input at $0.30 softens repeated context. For voice, live autocomplete and streaming UIs, K3's speed and verbosity are a poor fit, and Cerebras is built for exactly that job. Moonshot has had capacity trouble, pausing new API subscriptions days after K3's launch. Cerebras has its own gap, a tiny self-serve catalog that pushes most models into a sales conversation.
What Cerebras and Moonshot AI 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 profileMoonshot AI
Moonshot AI is the Beijing lab behind the Kimi models. Its flagship Kimi K3 launched July 16, 2026 as a 2.8 trillion parameter mixture-of-experts model that activates 16 of 896 experts per token, with native vision and a 1M token context. It is the first open model in the 3T class, and full weights landed on Hugging Face on July 27. The hosted API costs $3 in and $15 out per million tokens, with cached input at $0.30, and it runs through an OpenAI-compatible endpoint, Kimi Code in the terminal, OpenRouter and Cloudflare Workers AI.
Example models: Kimi K3, Kimi K2.6
Full Moonshot AI profileShould you choose Cerebras or Moonshot AI?
Cerebras
Choose Cerebras for
- Real-time voice and autocomplete that cannot wait
- Fast long outputs on GPT-OSS 120B at low cost
- Streaming UIs where token speed is visible
Moonshot AI
Choose Moonshot AI for
- Long-horizon coding on huge repositories
- Document-heavy and visual work needing 1M context
- Near-frontier coding scores on open weights
Cerebras vs Moonshot AI at a glance
| Attribute | Cerebras | Moonshot AI |
|---|---|---|
| Model access | Open weights | Open weights, custom license |
| Flagship models | GPT-OSS 120B, Gemma 4 31B | Kimi K3, Kimi K2.6 |
| Speed | ~3,000 tok/s on GPT-OSS 120B | ~33 tok/s on Kimi K3 |
| Price | $0.35 in, $0.75 out (GPT-OSS 120B) | $3 in, $15 out (Kimi K3) |
| Customization | Unknown | Open weights to fine-tune |
| Deployment | Shared API, dedicated, partners | API, Kimi Code, OpenRouter |
| Long context | Unknown | 1M |
Frequently asked questions
What is the difference between Cerebras and Moonshot AI?
Kimi K3 is near the frontier but runs around 33 tokens per second. Cerebras runs smaller open models near 3,000. Capability against speed.
When should I choose Cerebras over Moonshot AI?
Real-time voice and autocomplete that cannot wait; Fast long outputs on GPT-OSS 120B at low cost; Streaming UIs where token speed is visible.
When should I choose Moonshot AI over Cerebras?
Long-horizon coding on huge repositories; Document-heavy and visual work needing 1M context; Near-frontier coding scores on open weights.
Is Cerebras or Moonshot AI cheaper?
Cerebras: $0.35 in, $0.75 out (GPT-OSS 120B). Moonshot AI: $3 in, $15 out (Kimi K3). 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.