DeepSeek V4 Pro vs Qwen3.8 Max

As of October 21, 2026, Qwen3.8 Max costs 52% more per input token than DeepSeek V4 Pro ($2.00 vs $1.32 per 1M) and has a 992K-token context window versus 1M.

Prices from LiteLLM's price fileChecked October 21, 2026

DeepSeek V4 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 October 21, 2026.
	DeepSeek V4 Pro	Qwen3.8 Max
Input, per 1M tokens	$1.32	$2.00
Output, per 1M tokens	$3.96	$6.00
Cache read, per 1M tokens	$0.044	$0.25
Cache write, per 1M tokens	$0	–
Context window	1,000,000 tokens	991,808 tokens
Max output	393,216 tokens	131,072 tokens
Image input	No	Yes
Tool calling	Yes	Yes
Structured output (JSON)	Yes	Yes
Prompt caching	Yes	Yes
Reasoning	Yes	Yes
Providers on LiteLLM	18	8
Price shown is from	DeepSeek	Alibaba Cloud Model Studio
Added to LiteLLM	June 2026	August 7, 2026

Cheapest provider for each

DeepSeek V4 Pro: Sail at $0.92 input and $2.77 output per 1M tokens (sail/deepseek-ai/DeepSeek-V4-Pro-0813)
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 August 27, 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	DeepSeek V4 Pro	Qwen3.8 Max
Coding and agents
APEX-Agents	47.3%	63.3%
Reasoning and knowledge
GPQA Diamond	90.9% (high)	92.7% (xhigh)
SimpleQA Verified	47.0% (max)	45.8% (xhigh)
Math
FrontierMath (tiers 1-3)	45.3% (max)	74.7% (xhigh)
FrontierMath (tier 4)	2.4% (max)	46.3% (xhigh)
OTIS Mock AIME 2024-2025	96.7% (max)	99.4% (xhigh)

When to pick which

Pick DeepSeek V4 Pro for

Costs 34% less per input token: $1.32 vs $2.00 per 1M.
Returns up to 393,216 tokens per response, vs 131,072.

Pick Qwen3.8 Max for

Supports image input, which DeepSeek V4 Pro doesn't list.

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

Call DeepSeek V4 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!"}]

# DeepSeek V4 Pro
response = completion(model="deepseek-v4-pro", messages=messages)

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

# DeepSeek V4 Pro, falling back to Qwen3.8 Max if the call fails
response = completion(model="deepseek-v4-pro", messages=messages, fallbacks=["dashscope/qwen3.8-max"])

FAQ

Which is cheaper, DeepSeek V4 Pro or Qwen3.8 Max?

DeepSeek V4 Pro is cheaper at 3 input tokens per output token: $1.98 per 1M tokens blended ($1.32 input, $3.96 output), versus $3.00 ($2.00 and $6.00) for Qwen3.8 Max, as of October 21, 2026.

Which has the larger context window, DeepSeek V4 Pro or Qwen3.8 Max?

DeepSeek V4 Pro has the larger context window: 1,000,000 tokens, versus 991,808 for Qwen3.8 Max.

Can I switch from DeepSeek V4 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 "deepseek-v4-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.