Kimi K3.1 Pro vs Qwen3.8 Max

As of November 10, 2026, Kimi K3.1 Pro and Qwen3.8 Max cost the same per input token ($2.00 per 1M), but Kimi K3.1 Pro costs 67% more per output token ($10.00 vs $6.00) and has a 1M-token context window versus 992K.

Prices from LiteLLM's price fileChecked November 10, 2026

Kimi K3.1 Pro vs Qwen3.8 Max side by side

Headline prices in US dollars per 1M tokens: each model's own lab where it sells it, otherwise its cheapest provider. Source: LiteLLM model price file, as of November 10, 2026.
	Kimi K3.1 Pro	Qwen3.8 Max
Input, per 1M tokens	$2.00	$2.00
Output, per 1M tokens	$10.00	$6.00
Cache read, per 1M tokens	$0.20	$0.25
Cache write, per 1M tokens	$2.00	–
Context window	1,048,576 tokens	991,808 tokens
Max output	1,048,576 tokens	131,072 tokens
Image input	Yes	Yes
Tool calling	Yes	Yes
Structured output (JSON)	Yes	Yes
Prompt caching	Yes	Yes
Reasoning	Yes	Yes
Providers on LiteLLM	10	12
Price shown is from	Moonshot AI	Alibaba Cloud Model Studio
Added to LiteLLM	October 24, 2026	August 7, 2026

Cheapest provider for each

Kimi K3.1 Pro: Databricks at $2.00 input and $10.00 output per 1M tokens (databricks/databricks-kimi-k3-1-pro)
Qwen3.8 Max: DeepInfra at $1.65 input and $4.951 output per 1M tokens (deepinfra/Qwen/Qwen3.8-Max)

Benchmark scores

From the Epoch AI Benchmarking Hub (CC BY 4.0), data as of October 23, 2026. Some results were run by Epoch and others collected by Epoch from each benchmark's own leaderboard, linked in the first column. Each model's best reported reasoning setting is shown in brackets. Higher is better. Only benchmarks with a score for both models are shown.
Benchmark	Kimi K3.1 Pro	Qwen3.8 Max
Coding and agents
APEX-Agents	65.5%	63.3%

When to pick which

Pick Kimi K3.1 Pro for

Returns up to 1,048,576 tokens per response, vs 131,072.

Pick Qwen3.8 Max for

Costs 40% less per output token: $6.00 vs $10.00 per 1M.

From differences in the data above only; where the data is equal, no reason is given.

Call Kimi K3.1 Pro or Qwen3.8 Max with LiteLLM

Both use the OpenAI format through LiteLLM, so switching is a model name change. Set each provider's API key in the environment first.

Python SDK

from litellm import completion

messages = [{"role": "user", "content": "Hello!"}]

# Kimi K3.1 Pro
response = completion(model="moonshot/kimi-k3.1-pro", messages=messages)

# Qwen3.8 Max
response = completion(model="dashscope/qwen3.8-max", messages=messages)

# Kimi K3.1 Pro, falling back to Qwen3.8 Max if the call fails
response = completion(model="moonshot/kimi-k3.1-pro", messages=messages, fallbacks=["dashscope/qwen3.8-max"])

FAQ

Which is cheaper, Kimi K3.1 Pro or Qwen3.8 Max?

Qwen3.8 Max is cheaper at 3 input tokens per output token: $3.00 per 1M tokens blended ($2.00 input, $6.00 output), versus $4.00 ($2.00 and $10.00) for Kimi K3.1 Pro, as of November 10, 2026.

Which has the larger context window, Kimi K3.1 Pro or Qwen3.8 Max?

Kimi K3.1 Pro has the larger context window: 1,048,576 tokens, versus 991,808 for Qwen3.8 Max.

Can I switch from Kimi K3.1 Pro to Qwen3.8 Max without changing my code?

Yes. Through LiteLLM both use the OpenAI format, so switching means changing the model name from "moonshot/kimi-k3.1-pro" to "dashscope/qwen3.8-max", or pointing a LiteLLM gateway model alias at the other model. You can also use one as a fallback for the other.