Kimi K2 Thinking is $4.4 cheaper per 1M input tokens (88% lower; 8.33x difference).
API cost decision in 10 seconds
OpenAI GPT Latest vs Kimi K2 Thinking
Pick Kimi K2 Thinking for lower cost; pick OpenAI GPT Latest only if the larger context window matters more.
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Data confirmed:
Prices normalized to USD per 1M tokens
Sample workload: 1M input + 500K output
Budget verdict
Pick Kimi K2 Thinking for lower cost; pick OpenAI GPT Latest only if the larger context window matters more.
On the standard 1M input plus 500K output workload, Kimi K2 Thinking is estimated at $1.85 vs $20 for OpenAI GPT Latest, saving $18.15 (90.8% lower).
Cost-first pickKimi K2 Thinking
Context-first pickOpenAI GPT Latest
Sample savings$18.1590.8%
10x traffic gap$181.5
OpenAI GPT Latest has more context, but Kimi K2 Thinking saves $18.15 on the standard workload. At 10x that traffic, the same price gap is about $181.5. Use the calculator below to replace the sample workload with your own token volume.
Cost sensitivity
Workload Sensitivity
Kimi K2 Thinking stays cheaper across input-heavy, balanced, and output-heavy sample workloads.
| Workload shape | Token mix | Better pick | OpenAI GPT Latest | Kimi K2 Thinking |
|---|---|---|---|---|
| Input-heavy / RAG | 5M input + 500K output | Kimi K2 Thinking | $40 | $4.25 |
| Balanced workload | 1M input + 1M output | Kimi K2 Thinking | $35 | $3.1 |
| Output-heavy chatbot | 1M input + 5M output | Kimi K2 Thinking | $155 | $13.1 |
Kimi K2 Thinking is $27.5 cheaper per 1M output tokens (91.7% lower; 12x difference).
OpenAI GPT Latest has 787.86K more context (4.01x larger).
Kimi K2 Thinking is $18.15 cheaper on the standard workload (90.8% lower).
Estimate your workload cost
Your Workload Cost
This estimate uses normalized public API pricing per 1M tokens. It is a planning aid, not a billing quote. Verify provider pricing, limits, and terms before production use.
Quick Decision
Kimi K2 Thinking has the lower input price; Kimi K2 Thinking has the lower output price; OpenAI GPT Latest offers the larger context window. For the 1M input plus 500K output sample, Kimi K2 Thinking is cheaper for the standard workload.
For a 1M input token plus 500K output token workload, the estimated API cost is $20 for OpenAI GPT Latest and $1.85 for Kimi K2 Thinking.
Choose OpenAI GPT Latest when you care most about larger context window.
Choose Kimi K2 Thinking when you care most about lower input-token price, and lower output-token price.
- On the standard 1M input plus 500K output workload, Kimi K2 Thinking is estimated at $1.85 vs $20 for OpenAI GPT Latest, saving $18.15 (90.8% lower).
- Kimi K2 Thinking is $18.15 cheaper on the standard workload (90.8% lower).
- Kimi K2 Thinking is $4.4 cheaper per 1M input tokens (88% lower; 8.33x difference).
- Kimi K2 Thinking is $27.5 cheaper per 1M output tokens (91.7% lower; 12x difference).
- OpenAI GPT Latest has 787.86K more context (4.01x larger).
| Feature | OpenAI GPT Latest (OpenAI) | Kimi K2 Thinking (MoonshotAI) |
|---|---|---|
| Input Price prompt tokens per 1M | $5 | $0.6 |
| Completion Price per 1M tokens | $30 | $2.5 |
| Sample Workload Cost 1M input + 500K output | $20 | $1.85 |
| Context Window | 1.05M | 262.14K |
| Release Date |
Use-Case Decision Matrix
| Use case | Better pick | Why |
|---|---|---|
| Budget-constrained production | Kimi K2 Thinking | On the standard 1M input plus 500K output workload, Kimi K2 Thinking is estimated at $1.85 vs $20 for OpenAI GPT Latest, saving $18.15 (90.8% lower). |
| High-volume input processing | Kimi K2 Thinking | Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill. |
| Long responses and chatbots | Kimi K2 Thinking | Lower output-token price matters most when assistants generate many completion tokens. |
| RAG or long-document work | OpenAI GPT Latest | A larger context window leaves more room for retrieved passages, conversation history, or source files. |
Related Alternatives
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Kimi K2 Thinking is Moonshot AI’s most advanced open reasoning model to date, extending the K2 series into agentic, long-horizon reasoning. Built on the trillion-parameter Mixture-of-Experts (MoE) architecture introduced in...