DeepSeek V4 Flash vs DeepSeek V4 Pro
As of October 21, 2026, DeepSeek V4 Pro costs 340% more per input token than DeepSeek V4 Flash ($1.32 vs $0.30 per 1M), and both have a 1M-token context window.
DeepSeek V4 Flash vs DeepSeek V4 Pro side by side
| DeepSeek V4 Flash | DeepSeek V4 Pro | |
|---|---|---|
| Input, per 1M tokens | $0.30 | $1.32 |
| Output, per 1M tokens | $1.20 | $3.96 |
| Cache read, per 1M tokens | $0.006 | $0.044 |
| Cache write, per 1M tokens | $0 | $0 |
| Context window | 1,000,000 tokens | 1,000,000 tokens |
| Max output | 393,216 tokens | 393,216 tokens |
| Image input | Yes | No |
| Tool calling | Yes | Yes |
| Structured output (JSON) | Yes | Yes |
| Prompt caching | Yes | Yes |
| Reasoning | Yes | Yes |
| Providers on LiteLLM | 23 | 18 |
| Price shown is from | DeepSeek | DeepSeek |
| Added to LiteLLM | June 2026 | June 2026 |
Cheapest provider for each
- DeepSeek V4 Flash: DeepInfra at $0.08 input and $0.18 output per 1M tokens (
deepinfra/deepseek-ai/DeepSeek-V4-Flash-0731) - DeepSeek V4 Pro: Sail at $0.92 input and $2.77 output per 1M tokens (
sail/deepseek-ai/DeepSeek-V4-Pro-0813)
Benchmark scores
| Benchmark | DeepSeek V4 Flash | DeepSeek V4 Pro |
|---|---|---|
| Coding and agents | ||
| FrontierCode | 18.8% | 17.6% (none) |
| Reasoning and knowledge | ||
| GPQA Diamond | 91.0% (max) | 90.9% (high) |
| ARC-AGI-2 | 61.4% (max) | 61.3% (max) |
| SimpleQA Verified | 33.6% (max) | 47.0% (max) |
| Math | ||
| FrontierMath (tiers 1-3) | 57.5% (max) | 45.3% (max) |
| FrontierMath (tier 4) | 24.4% (max) | 2.4% (max) |
| OTIS Mock AIME 2024-2025 | 94.4% (max) | 96.7% (max) |
When to pick which
Pick DeepSeek V4 Flash for
- Costs 77% less per input token: $0.30 vs $1.32 per 1M.
- 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 Flash or DeepSeek V4 Pro 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.
from litellm import completion
messages = [{"role": "user", "content": "Hello!"}]
# DeepSeek V4 Flash
response = completion(model="deepseek-v4-flash", messages=messages)
# DeepSeek V4 Pro
response = completion(model="deepseek-v4-pro", messages=messages)
# DeepSeek V4 Flash, falling back to DeepSeek V4 Pro if the call fails
response = completion(model="deepseek-v4-flash", messages=messages, fallbacks=["deepseek-v4-pro"])FAQ
Which is cheaper, DeepSeek V4 Flash or DeepSeek V4 Pro?
DeepSeek V4 Flash is cheaper at 3 input tokens per output token: $0.525 per 1M tokens blended ($0.30 input, $1.20 output), versus $1.98 ($1.32 and $3.96) for DeepSeek V4 Pro, as of October 21, 2026.
Which has the larger context window, DeepSeek V4 Flash or DeepSeek V4 Pro?
Both have a 1,000,000-token context window.
Can I switch from DeepSeek V4 Flash to DeepSeek V4 Pro without changing my code?
Yes. Through LiteLLM both use the OpenAI format, so switching means changing the model name from "deepseek-v4-flash" to "deepseek-v4-pro", or pointing a LiteLLM gateway model alias at the other model. You can also use one as a fallback for the other.
