Kimi K2.5 vs babbage-002
Detailed pricing comparison and cost analysis.
Updated September 2026
Cost Simulator
| Feature | Kimi K2.5 | babbage-002 |
|---|---|---|
| Provider | Moonshot AI | OpenAI |
| Input Price (1M) | $0.60 | $0.40 |
| Output Price (1M) | $3.00 | $0.40 |
| Context Window | 262,144 | 16,384 |
Verdict
Kimi K2.5 costs $0.60 per 1M input tokens and $3.00 per 1M output tokens. babbage-002 costs $0.40 per 1M input tokens and $0.40 per 1M output tokens. babbage-002 is 33% cheaper on input tokens than Kimi K2.5. For output tokens, babbage-002 is the more affordable option at $0.40/1M vs $3.00.
On context window, Kimi K2.5 supports 262,144 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose Kimi K2.5
- ✓ You need a larger context window (262,144 tokens)
- ✓ You are already integrated with Moonshot AI
When to choose babbage-002
- ✓ You need the lowest input token cost ($ 0.40/1M)
- ✓ Your workload is output-heavy — babbage-002 generates text cheaper
- ✓ You are already integrated with OpenAI
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 Kimi K2.5 cheaper than babbage-002? ▼
babbage-002 is cheaper on input tokens at $0.40/1M vs $0.60/1M for Kimi K2.5 — a 33% saving.
What is the context window of Kimi K2.5 vs babbage-002? ▼
Kimi K2.5 has a 262,144-token context window. babbage-002 has a 16,384-token context window. Kimi K2.5 supports the larger context, suitable for longer documents and agentic workflows.
Which model is better: Kimi K2.5 or babbage-002? ▼
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 K2.5 supports 262,144 tokens. Use the comparison table above to find the right fit for your workload.