OPENAI VS MOONSHOT AI

GPT-5.6 Terra vs Kimi K2 Thinking

Kimi K2 Thinking is about 4.2x cheaper than GPT-5.6 Terra on a blended 3:1 input:output rate — the same 10K-in/2K-out request costs $0.011 vs $0.044. GPT-5.6 Terra has the larger context window (1,047,576 vs 262,144 tokens).

Pricing verified

API pricing per 1M tokens

ModelInput / 1M tokensOutput / 1M tokensBlended (3:1 mix)
GPT-5.6 Terra$2.00$12.00$4.50
Kimi K2 Thinking$0.60$2.50$1.075

Rates from official OpenAI pricing and official Moonshot AI pricing, checked 2026-08-11.

What a real workload costs

Example: one request with 10,000 input tokens and 2,000 output tokens, then that request at 100,000 runs per month.

ModelPer request (10K in / 2K out)Per month (100K requests)Monthly delta
GPT-5.6 Terra$0.044$4,400+$3,300
Kimi K2 Thinking$0.011$1,100—

Model your own prompt sizes and volumes in the token cost analyzer, or check caching savings with the prompt caching calculator.

Specs side by side

SpecGPT-5.6 TerraKimi K2 Thinking
ProviderOpenAIMoonshot AI
Context window1,047,576 tokens262,144 tokens
Model classGeneral textReasoning
Tokenizero200k_base (exact, public)Proprietary (counts estimated)

Count tokens for each model on its own page: GPT-5.6 Terra token counter and Kimi K2 Thinking token counter. All OpenAI models are on the OpenAI Token Counter. All Moonshot AI models are on the Kimi Token Counter.

FAQ

GPT-5.6 Terra vs Kimi K2 Thinking FAQ

Which is cheaper, GPT-5.6 Terra or Kimi K2 Thinking?

Kimi K2 Thinking is cheaper. At rates checked 2026-08-11, GPT-5.6 Terra costs $2.00 input / $12.00 output per 1M tokens, while Kimi K2 Thinking costs $0.60 input / $2.50 output — about a 4.2x difference on a typical 3:1 input-heavy workload.

How much does a real request cost on GPT-5.6 Terra vs Kimi K2 Thinking?

A request with 10,000 input tokens and 2,000 output tokens costs about $0.044 on GPT-5.6 Terra and $0.011 on Kimi K2 Thinking. At 100,000 such requests per month that is $4,400 vs $1,100 — a difference of $3,300 per month.

Which has the bigger context window, GPT-5.6 Terra or Kimi K2 Thinking?

GPT-5.6 Terra. It supports 1,047,576 tokens of context versus 262,144 tokens — roughly 786,000 English words in one request.

Do GPT-5.6 Terra and Kimi K2 Thinking count tokens the same way?

Not exactly. GPT-5.6 Terra uses the published o200k_base tokenizer and Kimi K2 Thinking uses a proprietary tokenizer (counts are estimates). The same prompt will tokenize differently, which shifts effective cost per word.