Everything about DeepSeek V4.1 Flash 1017

What you need to know

DeepSeek released DeepSeek V4.1 Flash 1017 on October 17, 2026. It has a reasoning mode: it can "think" before answering, which helps with multi-step problems but uses more tokens, and therefore more money. It is an open-weight model: you can host it yourself on infrastructure you control, and 34 API providers listed on models.dev also offer it. It is a very recent release.

We file it under open-weight models (68 models in this category on the site).

Benchmark results

According to results published by Epoch AI (checked on October 22, 2026), DeepSeek V4.1 Flash 1017 has a capability index of 90.6 out of 100, computed on 3 benchmarks from our basket.

With only 3 benchmarks reported, it does not enter the public ranking, which requires at least 4 results, so its index should be read as partial.

• GPQA Diamond (PhD-level science questions): 93.2% — the best tracked model, Gemini 4 Argon, reaches 96.4%
• OTIS Mock AIME (competition mathematics problems): 99.4% — the best tracked model, GPT-5.5 Pro, reaches 100%
• FrontierMath (very hard research-level mathematics): 71.2% — the best tracked model, GPT-6 Astra, reaches 93.7%

Pricing and budget

DeepSeek V4.1 Flash 1017 costs $0.15 per million input tokens and $0.60 per million output tokens (developer's price, checked on October 22, 2026).

In practice, an agent task that reads 20,000 tokens and writes 2,000 costs about $0.004, or $4.2 for 1,000 such tasks.

That is 1.7 times cheaper than the median output price of open-weight models ($1).

Tokens read back from cache cost $0.003 per million, which helps an agent that keeps resending the same context.

To estimate your own budget, use the agent cost calculator.

What it can do

Its context window reaches 1,048,576 tokens, roughly 1,573 pages of text at 500 words per page: enough to load a whole book or a mid-sized codebase. A single answer can run to 393,216 tokens.

It accepts as input: text and image. Its knowledge stops at May 2025: beyond that, it needs web search or your documents.

It can call tools (functions, APIs) and return structured JSON output, which makes it a possible foundation for an AI agent.

The weights are published on Hugging Face (deepseek-ai/DeepSeek-V4.1-Flash-1017). See also our list of open-source models.

When to choose it

Given its category, DeepSeek V4.1 Flash 1017 is mainly aimed at projects where you want control over your data or hosting, or want to adapt the model to your field.

Its very large context window makes it worth considering for analysing large sets of documents in a single request.

Its very low cost (under half a cent for our sample task) means it can be called thousands of times a day.

Before adopting it, test it on your own examples: benchmarks measure standard tasks, not your job.

Comparable models

In the same category, the models closest to DeepSeek V4.1 Flash 1017 are:

• Kimi K2.6 (Moonshot AI, index 84.4) — see the comparison
• Kimi K3 (Moonshot AI, index 80.7) — see the comparison
• GLM-5.3 (Zhipu AI (Z.ai), index 78.8) — see the comparison
• GLM-5 (Zhipu AI (Z.ai), index 77.2) — see the comparison