DeepSeek V4.1 Flash vs DeepSeek V4 Pro

Specs side by side

Price per token

Prices exclude taxes and are charged from your prepaid USD balance. Both models work through the same key, in the OpenAI-compatible /v1/chat/completions endpoint. Failed generations cost nothing.

For new projects, use DeepSeek V4.1 Flash: it is newer, about 10 times cheaper than V4 Pro on a typical chat workload, and DeepSeek itself says it beats the larger model on quality, cost and speed in several third-party tests. Keep DeepSeek V4 Pro where your prompts, parsers and tests were built around it, or where your own data shows it gives better results. Both have a 1M-token context, replies up to 384K tokens and open weights.

Key differences

• Price. DeepSeek V4.1 Flash costs $0.54 / $1.75 per 1M tokens, DeepSeek V4 Pro costs $2.35 / $4.70 per 1M tokens. Pro is about 7 times more on input and about 12 times more on output. On the 1,000-reply row above the gap is about 10.5 times.
• Size. V4 Pro is a MoE model with 1.6T parameters, 49B of them active per token. V4.1 Flash is a MoE with an encoder-decoder design: about 8B active parameters while reading the prompt and 16B while writing.
• Generation. Flash came out in September 2026 as the first model of DeepSeek's new architecture family. Pro was in preview from April 2026 and became generally available in August 2026.
• Thinking. V4 Pro has three reasoning effort levels: low, high and max. Thinking tokens are billed as output, which is already the most expensive part of Pro.
• Pro's future. DeepSeek first announced it would route V4 Pro traffic to V4.1 Flash, then kept Pro available after user pushback. The announced V4.1 Pro has no public date.
• Shared. 1,000,000 tokens of context, replies up to 384,000 tokens, weights on Hugging Face, the same OpenAI-compatible endpoint.

When to pick DeepSeek V4.1 Flash

• Coding agents that read a large chunk of the repository on every step.
• Bulk classification, data extraction and summaries of long documents.
• New projects without prompts already tuned to a specific model.
• Tests and prototypes with thousands of requests.

When to pick DeepSeek V4 Pro

• Production pipelines that run on V4 Pro and have regression tests built on its outputs.
• Tasks where you want to set reasoning effort to max yourself.
• An A/B run of both models on your data before migrating.
• Long-context analysis, if Pro does better on your prompts despite the price.

Cost in practice

Output drives the bill in both models, since it costs many times more than input. With V4 Pro on high reasoning effort that share grows, because reasoning is billed as output too. In my tokenizer test (published in Polish on promptowy.com), V4 Pro used only about 5% more tokens than OpenAI models on the same Polish text; V4.1 Flash was not part of that measurement, so I do not quote a number for it. A wider ranking of budget models is in Cheapest LLM API in 2026, and other currently available pairs are listed in model comparisons.

I start with V4.1 Flash, and only when a specific task type comes out clearly worse do I run it in parallel on V4 Pro. Both models sit behind the same key and balance, so switching is a one-line change to model.

FAQ

DeepSeek V4.1 Flash or V4 Pro: which is better?

According to DeepSeek, V4.1 Flash, despite fewer active parameters. I have no independent head-to-head test of this pair, so a trial on your own prompts decides.

How much more expensive is V4 Pro than V4.1 Flash?

About 7 times on input and about 12 times on output. Current rates: $0.54 / $1.75 per 1M tokens and $2.35 / $4.70 per 1M tokens.

Will DeepSeek V4 Pro be shut down?

DeepSeek walked back its plan to redirect traffic and kept V4 Pro, but with a newer Flash and an announced V4.1 Pro, expect a migration at some point. Keep the model ID in your config.

Does moving from V4 Pro to V4.1 Flash need code changes?

Change model from deepseek-v4-pro to deepseek-v4.1-flash. The endpoint, key and response format stay the same. Just check that your parsers handle the new model's replies.

Do I need a DeepSeek account?

No. You call both models with your Jantaris key and pay from one prepaid balance, and Jantaris does not use your prompts or outputs to train models.