babbage-002 vs Kimi K2.7 Code
Detailed pricing comparison and cost analysis.
Updated September 2026
Cost Simulator
| Feature | babbage-002 | Kimi K2.7 Code |
|---|---|---|
| Provider | OpenAI | Moonshot AI |
| Input Price (1M) | $0.40 | $0.95 |
| Output Price (1M) | $0.40 | $4.00 |
| Context Window | 16,384 | 262,144 |
Verdict
babbage-002 costs $0.40 per 1M input tokens and $0.40 per 1M output tokens. Kimi K2.7 Code costs $0.95 per 1M input tokens and $4.00 per 1M output tokens. babbage-002 is 58% cheaper on input tokens than Kimi K2.7 Code. For output tokens, babbage-002 is the more affordable option at $0.40/1M vs $4.00.
On context window, Kimi K2.7 Code supports 262,144 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose babbage-002
- ✓ You need the lowest input token cost ($ 0.40/1M)
- ✓ Your workload is output-heavy — babbage-002 generates text cheaper
- ✓ You are already integrated with OpenAI
When to choose Kimi K2.7 Code
- ✓ You need a larger context window (262,144 tokens)
- ✓ 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 babbage-002 cheaper than Kimi K2.7 Code? ▼
babbage-002 is cheaper on input tokens at $0.40/1M vs $0.95/1M for Kimi K2.7 Code — a 58% saving.
What is the context window of babbage-002 vs Kimi K2.7 Code? ▼
babbage-002 has a 16,384-token context window. Kimi K2.7 Code has a 262,144-token context window. Kimi K2.7 Code supports the larger context, suitable for longer documents and agentic workflows.
Which model is better: babbage-002 or Kimi K2.7 Code? ▼
The best choice depends on your use case. For cost efficiency on input tokens, babbage-002 is the cheaper option. For maximum context length, Kimi K2.7 Code supports 262,144 tokens. Use the comparison table above to find the right fit for your workload.