Which is cheaper, Kimi K2 Instruct or Qwen3.8 Max?
As of August 20, 2026, Kimi K2 Instruct is 88% cheaper on blended LLM API pricing. Kimi K2 Instruct costs $0.50 per million input tokens and $0.50 per million output tokens ($1.00 blended 1M-in + 1M-out). Qwen3.8 Max costs $2.00 / $6.00 per million tokens ($8.00 blended). Sources: https://platform.kimi.com/docs/pricing/chat and https://www.alibabacloud.com/help/en/model-studio/pricing. The cheapest LLM for your app still depends on how many output tokens you generate.
How much does Kimi K2 Instruct vs Qwen3.8 Max cost per million tokens?
Kimi K2 Instruct Moonshot AI API pricing is $0.50 input and $0.50 output per million tokens via Moonshot AI. Qwen3.8 Max Qwen API pricing is $2.00 input and $6.00 output per million tokens via Qwen. Use those four numbers, not ChatGPT Plus or Claude Pro subscription prices, when you are comparing APIs.
Which is better for coding, Kimi K2 Instruct or Qwen3.8 Max?
Qwen3.8 Max leads on SWE-bench Verified: Kimi K2 Instruct at 43.8% vs Qwen3.8 Max at 85.6%. SWE-bench measures real GitHub fixes; LiveCodeBench measures contest programming.
What is the context window for Kimi K2 Instruct vs Qwen3.8 Max?
Kimi K2 Instruct supports a 200K token context window, while Qwen3.8 Max supports 1M tokens. Qwen3.8 Max 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.
Which model performs better on benchmarks, Kimi K2 Instruct or Qwen3.8 Max?
Qwen3.8 Max leads on 12 of 12 shared benchmarks versus Kimi K2 Instruct's 0 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.
Is Kimi K2 Instruct or Qwen3.8 Max better for production use?
Both Kimi K2 Instruct and Qwen3.8 Max are production API models. For cost-sensitive production traffic, Kimi K2 Instruct 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.
Can I switch between Kimi K2 Instruct and Qwen3.8 Max in my app?
Yes. Call Kimi K2 Instruct through Moonshot AI and Qwen3.8 Max through Qwen 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.
How accurate is this Kimi K2 Instruct vs Qwen3.8 Max pricing data?
List rates are the latest published Moonshot AI API pricing and Qwen API pricing, quoted per million tokens. Official rate sheets: https://platform.kimi.com/docs/pricing/chat and https://www.alibabacloud.com/help/en/model-studio/pricing. 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.
What is the output token limit for Kimi K2 Instruct and Qwen3.8 Max?
Kimi K2 Instruct supports up to 200K output tokens per request, while Qwen3.8 Max supports up to an unspecified number of output tokens. Output tokens are usually the expensive half of API pricing, so a higher max output cap is a capability, not a discount.
Which model has better throughput, Kimi K2 Instruct or Qwen3.8 Max?
Neither Kimi K2 Instruct nor Qwen3.8 Max has published throughput data on this page yet. 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.
How do I switch the models in this Kimi K2 Instruct vs Qwen3.8 Max comparison?
Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open Kimi K2 Instruct or Qwen3.8 Max from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.
