GPT-6 Astra vs Kimi K3
Detailed pricing comparison and cost analysis.
Updated September 2026
Cost Simulator
| Feature | GPT-6 Astra | Kimi K3 |
|---|---|---|
| Provider | OpenAI | Moonshot AI |
| Input Price (1M) | $10.00 | $3.00 |
| Output Price (1M) | $50.00 | $15.00 |
| Context Window | 1,050,000 | 1,000,000 |
Verdict
GPT-6 Astra costs $10.00 per 1M input tokens and $50.00 per 1M output tokens. Kimi K3 costs $3.00 per 1M input tokens and $15.00 per 1M output tokens. Kimi K3 is 70% cheaper on input tokens than GPT-6 Astra. For output tokens, Kimi K3 is the more affordable option at $15.00/1M vs $50.00.
On context window, GPT-6 Astra supports 1,050,000 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose GPT-6 Astra
- ✓ You need a larger context window (1,050,000 tokens)
- ✓ You are already integrated with OpenAI
When to choose Kimi K3
- ✓ You need the lowest input token cost ($ 3.00/1M)
- ✓ Your workload is output-heavy — Kimi K3 generates text cheaper
- ✓ You are already integrated with Moonshot AI
Use the calculator above to simulate your specific workload and find the exact break-even point. For most applications, the cheapest model is the one that minimises your total monthly bill given your input-to-output token ratio.
Frequently Asked Questions
Is GPT-6 Astra cheaper than Kimi K3? ▼
Kimi K3 is cheaper on input tokens at $3.00/1M vs $10.00/1M for GPT-6 Astra — a 70% saving.
What is the context window of GPT-6 Astra vs Kimi K3? ▼
GPT-6 Astra has a 1,050,000-token context window. Kimi K3 has a 1,000,000-token context window. GPT-6 Astra supports the larger context, suitable for longer documents and agentic workflows.
Which model is better: GPT-6 Astra or Kimi K3? ▼
The best choice depends on your use case. For cost efficiency on input tokens, Kimi K3 is the cheaper option. For maximum context length, GPT-6 Astra supports 1,050,000 tokens. Use the comparison table above to find the right fit for your workload.