As of August 20, 2026, Grok-3 and Kimi K3 have the same blended API cost at $18.00 per 1 million input plus 1 million output tokens. Grok-3 is $3.00 / $15.00 per million tokens versus Kimi K3 at $3.00 / $15.00. Sources: https://docs.x.ai/docs/models and https://platform.kimi.com/docs/pricing/chat
Sources: xAI API pricing (https://docs.x.ai/docs/models); Moonshot AI pricing (https://platform.kimi.com/docs/pricing/chat)
Grok-3
xAI
Input
$3.00
Output
$15.00
Kimi K3
Moonshot AI
Input
$3.00
Output
$15.00
Token pricing, specs, and benchmarks side by side
Grok-3Kimi K3Verdict
Grok-3 offers significantly lower pricing, while Kimi K3 leads on benchmark performance. Your choice depends on whether cost efficiency or raw capability matters more for your use case.
As of August 20, 2026, Grok-3 is 0% cheaper on blended LLM API pricing. Grok-3 costs $3.00 per million input tokens and $15.00 per million output tokens ($18.00 blended 1M-in + 1M-out). Kimi K3 costs $3.00 / $15.00 per million tokens ($18.00 blended). Sources: https://docs.x.ai/docs/models and https://platform.kimi.com/docs/pricing/chat. The cheapest LLM for your app still depends on how many output tokens you generate.
Grok-3 xAI API pricing is $3.00 input and $15.00 output per million tokens via xAI. Kimi K3 Moonshot AI API pricing is $3.00 input and $15.00 output per million tokens via Moonshot AI. Use those four numbers, not ChatGPT Plus or Claude Pro subscription prices, when you are comparing APIs.
Kimi K3 has a published SWE-bench Verified score of 93.4%. Grok-3 does not have that eval on this page yet.
Grok-3 supports a 128K token context window, while Kimi K3 supports 1M tokens. Kimi K3 offers a larger context window. Filling a larger window bills more input tokens, so the cheaper-per-million model can still cost more on long documents.
Kimi K3 leads on 9 of 11 shared benchmarks versus Grok-3's 2 wins. Check the comparison table for GPQA Diamond, SWE-bench, MMLU, HLE, and the other evals we track. Token price and benchmark score together are the usual LLM comparison, not either number alone.
Both Grok-3 and Kimi K3 are production API models. For cost-sensitive production traffic, Grok-3 has better economics on the blended 1M-in + 1M-out scale. For maximum capability, weight the benchmark table for your domain. Many production stacks route cheap models for drafts and a frontier model for the hard turn, which is usually cheaper than sending everything to the expensive API.
Yes. Call Grok-3 through xAI and Kimi K3 through Moonshot AI with separate API keys, or through a gateway that already wraps both. Keep prompts in tokens, not characters, when you estimate the invoice. AnotherWrapper templates can swap providers without rewriting auth and billing.
List rates are the latest published xAI API pricing and Moonshot AI API pricing, quoted per million tokens. Official rate sheets: https://docs.x.ai/docs/models and https://platform.kimi.com/docs/pricing/chat. Prompt caching, batch APIs, and committed-use discounts can change the invoice. Benchmark scores are from official publications and independent evals, updated as new numbers land.
Grok-3 supports up to 8K output tokens per request, while Kimi K3 supports up to 1M output tokens. Output tokens are usually the expensive half of API pricing, so a higher max output cap is a capability, not a discount.
Grok-3 runs at approximately 100 tokens/second while Kimi K3 runs at 8.253358168863906 tokens/second. Grok-3 is faster in raw throughput. Throughput (tokens per second) and time-to-first-token are why two models with the same per-million-token price can still feel different in chat and agents.
Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open Grok-3 or Kimi K3 from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.
This comparison covers API pricing, context window, throughput, and benchmark scores. Grok-3 costs $3.00/M input and $15.00/M output. Kimi K3 costs $3.00/M input and $15.00/M output.
Rank both on the LLM leaderboard, SWE-bench, and Humanity's Last Exam.
The index
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