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Groq vs Moonshot AI
Moonshot's Kimi K3 is the strongest open model but runs around 33 tokens per second. Groq serves smaller open models at 500 or more. Quality against speed.
By The Subconscious Team · Updated
Groq vs Moonshot AI: key differences
Few pairs show the quality and speed trade as clearly. Moonshot's Kimi K3 is a 2.8 trillion parameter mixture-of-experts model with 1M context, and Vals AI scored it 93.4% on SWE-bench Verified, fourth overall. It always thinks and runs around 33 tokens per second, at $3 in and $15 out per million. Groq's catalog is smaller models, GPT-OSS 120B and Qwen 3.6 27B, which it pushes at 500 to 1,000 tokens per second on its LPU with predictable tail latency. Groq does not serve Kimi.
Context and capacity also split them. Kimi K3 holds 1M tokens for repo-scale work, while Groq stops around 131K. But Moonshot's GPUs were overrun within days of K3's launch, pausing new subscriptions, and Groq's constraint is catalog rather than capacity. The sensible pattern for many agents is to route hard planning or long-context coding to Kimi and fast, frequent sub-steps to Groq. Moonshot's cheaper Kimi K2.6 at $0.95 in and $4 out is another middle option.
What Groq and Moonshot AI 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 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 Groq or Moonshot AI?
Groq
Choose Groq for
- Fast sub-steps in an agent loop
- Voice interfaces that cannot tolerate slow output
- Cheap high-frequency calls on small models
Moonshot AI
Choose Moonshot AI for
- Hard coding tasks on huge repositories
- Document-heavy research at 1M context
- Near-frontier quality from open weights
Groq vs Moonshot AI at a glance
| Attribute | Groq | Moonshot AI |
|---|---|---|
| Model access | Open weights | Open weights, custom license |
| Flagship models | GPT-OSS 120B, Qwen 3.6 27B | Kimi K3, Kimi K2.6 |
| Speed | 500–1,000 tok/s | ~33 tok/s on Kimi K3 |
| Price | Near the floor on small models | $3 in, $15 out (Kimi K3) |
| Customization | No fine-tuned model hosting | Open weights to fine-tune |
| Deployment | GroqCloud API | API, Kimi Code, OpenRouter |
| Long context | Around 131K max | 1M |
Frequently asked questions
What is the difference between Groq and Moonshot AI?
Moonshot's Kimi K3 is the strongest open model but runs around 33 tokens per second. Groq serves smaller open models at 500 or more. Quality against speed.
When should I choose Groq over Moonshot AI?
Fast sub-steps in an agent loop; Voice interfaces that cannot tolerate slow output; Cheap high-frequency calls on small models.
When should I choose Moonshot AI over Groq?
Hard coding tasks on huge repositories; Document-heavy research at 1M context; Near-frontier quality from open weights.
Is Groq or Moonshot AI cheaper?
Groq: Near the floor on small models. Moonshot AI: $3 in, $15 out (Kimi K3). The cheaper choice depends on the model and workload.
Which has more context, Groq or Moonshot AI?
Groq: Around 131K max. Moonshot AI: 1M.
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.