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GPT-3.5 Turbo vs Kimi K2.6

Detailed pricing comparison and cost analysis.

Updated September 2026

Cost Simulator

GPT-3.5 Turbo Cost
$0.80
Kimi K2.6 Cost
$1.75
GPT-3.5 Turbo is 54% cheaper
FeatureGPT-3.5 TurboKimi K2.6
ProviderOpenAIMoonshot AI
Input Price (1M)$0.50$0.95
Output Price (1M)$1.50$4.00
Context Window16,385262,144

Verdict

GPT-3.5 Turbo costs $0.50 per 1M input tokens and $1.50 per 1M output tokens. Kimi K2.6 costs $0.95 per 1M input tokens and $4.00 per 1M output tokens. GPT-3.5 Turbo is 47% cheaper on input tokens than Kimi K2.6. For output tokens, GPT-3.5 Turbo is the more affordable option at $1.50/1M vs $4.00.

On context window, Kimi K2.6 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 GPT-3.5 Turbo

  • ✓ You need the lowest input token cost ($ 0.50/1M)
  • ✓ Your workload is output-heavy — GPT-3.5 Turbo generates text cheaper
  • ✓ You are already integrated with OpenAI

When to choose Kimi K2.6

  • ✓ 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 GPT-3.5 Turbo cheaper than Kimi K2.6?

GPT-3.5 Turbo is cheaper on input tokens at $0.50/1M vs $0.95/1M for Kimi K2.6 — a 47% saving.

What is the context window of GPT-3.5 Turbo vs Kimi K2.6?

GPT-3.5 Turbo has a 16,385-token context window. Kimi K2.6 has a 262,144-token context window. Kimi K2.6 supports the larger context, suitable for longer documents and agentic workflows.

Which model is better: GPT-3.5 Turbo or Kimi K2.6?

The best choice depends on your use case. For cost efficiency on input tokens, GPT-3.5 Turbo is the cheaper option. For maximum context length, Kimi K2.6 supports 262,144 tokens. Use the comparison table above to find the right fit for your workload.