The short version
- DeepSeek-V4.1-Flash-1017 is an update to DeepSeek-V4.1-Flash, released on 10 September 2026. Same architecture and API name, better scores and shorter answers.
- In DeepSeek's own tests it gains most on agentic coding: 42.3 on Terminal-Bench 4.0, up from 31.2, and 77.0 on DeepSWE v1.1.
- It is a mixture-of-experts model with 552 billion parameters, of which 16 billion work on each generated token, and a context of up to 1 million tokens. The weights are free under the MIT license.
- The full weights take about 510 GB. On a Mac it needs a Mac Studio with 256 to 512 GB of memory, or SSD streaming on 128 GB.
1What DeepSeek V4.1 Flash 1017 is
DeepSeek-V4.1-Flash is the smallest model of DeepSeek's V4.1 family, with built-in image understanding. It arrived in DeepSeek's API on 10 September 2026; on 17 October DeepSeek replaced it with DeepSeek-V4.1-Flash-1017, which it calls a minor update (DeepSeek API changelog).
The update improves reasoning, coding, agentic and visual benchmarks, and the model finishes tasks with shorter output in less time. DeepSeek tuned it for its own DeepSeek Harness, Tencent CodeBuddy and WorkBuddy, and recommends DeepSeek Harness 0.2.1-rc.1 or later (Hugging Face).
The name follows DeepSeek's habit of dating updates: 1017 is the month and day, 17 October.
2Benchmarks: 1017 against V4.1 Flash and others
| Benchmark | V4.1 Flash 1017 | V4.1 Flash | Opus 5 | GPT-5.6 Sol | Kimi K3 | GLM-5.3 |
|---|---|---|---|---|---|---|
| HLE, no tools | 42.6 | 36.8 | 56.3 | 44.5 | 43.5 | 42.0 |
| Terminal-Bench 4.0 | 42.3 | 31.2 | 51.8 | 39.9 | 12.6 | 37.9 |
| DeepSWE v1.1 | 77.0 | 74.2 | 74.0 | 73.0 | 67.5 | 66.9 |
| ProgramBench | 29.1 | 20.3 | 37.0 | 23.0 | 17.5 | 19.0 |
| NL2Repo-Bench | 68.2 | 65.4 | 75.3 | 56.8 | 58.0 | 58.0 |
| Agents' Last Exam | 35.4 | 31.8 | 28.6 | 26.7 | 27.6 | 28.5 |
| Apex Bench v1.1 | 72.1 | 64.6 | 65.8 | 54.3 | 47.8 | 56.6 |
On these numbers, 1017 leads every model in the table on DeepSWE, Agents' Last Exam and Apex Bench, and trails Opus 5 on reasoning without tools, terminal work and building whole programs. It also improves on chart and image tests such as Chartography (83.0, up from 78.9) and BabyVision (92.5, up from 89.6).
Cost per task falls too. In DeepSeek's charts reported by The Decoder, a DeepSWE task costs just under $0.20 at the highest setting, against about $0.50 for the previous version.
3Parameters, context and size
| DeepSeek-V4.1-Flash-1017 | |
|---|---|
| Total parameters | 552 billion (mixture of experts) |
| Active per token | 8 billion reading the prompt, 16 billion writing |
| Experts | 384 routed and 1 shared, 6 used per token |
| Context | Up to 1,048,576 tokens |
| Memory per token of context | About 890 bytes, a quarter of V4 Flash |
| Weights | FP8, with experts in FP4; about 510 GB |
| Input | Text and images |
| License | MIT |
About 189 GiB of the download is two Engram tables: a large memory the model looks up rather than computes, a few rows per token. Runtimes such as DwarfStar keep them on the SSD and read only the rows they need, which is why the model can run in far less memory than its download size (antirez on Hugging Face).
4API name and price
In DeepSeek's API the model is called deepseek-flash, and it now points to 1017; the older names deepseek-v4-flash and deepseek-v4-flash-vision-exp are routed to it for now. DeepSeek says the calling method and prices stayed the same with 1017 (DeepSeek).
| Provider | Input, per million tokens | Output, per million tokens |
|---|---|---|
| DeepSeek | $0.15 | $0.60 |
| Cloudflare | $0.10 | $0.60 |
| DeepInfra | $0.098 | $0.294 |
| Together, Fireworks, Baseten | $0.30 | $1.20 |
DeepSeek recommends a temperature of 1.0, top_p of 1.0 and room for long answers (max_tokens of at least 256K). The weights ship without a Jinja chat template: prompts are built with the encoding code in the model's repository.
5Can you run it on your own Mac?
Yes, but only on the biggest Macs. A Mac Studio with 512 GB of memory (M3 Ultra or M5 Ultra) holds the 4-bit builds entirely; 256 GB is enough for a 2-bit build with the Engram tables left on the SSD; a 128 GB Mac can stream from the SSD, slowly. The first published run, on an M3 Ultra with 512 GB, generated about 6.5 tokens a second.
Which build to download, the commands and the speeds are in our step-by-step guide: Run DeepSeek V4.1 Flash on a Mac.
For every other Mac, the API costs cents per million tokens, and smaller open models run well locally: see the best Mac for local LLMs.
6Put a big Mac's idle hours to work
A Mac Studio bought to run models like this spends most of the day waiting. MLX Node lends that spare time to MLX Network, which runs AI jobs on Macs with Apple's MLX framework, and earns MLX Tokens for verified work: up to 1 MLX a day on a free seat, and up to 320 with an Ultra dedicated seat running on an Ultra Mac. Mined MLX is locked for 30 days, less with a dedicated seat; see the risks.
7Questions
- What is DeepSeek V4.1 Flash 1017?
- It is the 17 October 2026 update of DeepSeek-V4.1-Flash, DeepSeek's open-weight mixture-of-experts model. It keeps the architecture and API name and scores higher on reasoning, coding, agent and image benchmarks.
- What is the difference between V4.1 Flash and 1017?
- Same model size and API, newer training. In DeepSeek's tests 1017 scores 42.3 instead of 31.2 on Terminal-Bench 4.0 and 77.0 instead of 74.2 on DeepSWE v1.1, and it writes shorter answers, so tasks cost less.
- How many parameters does DeepSeek V4.1 Flash have?
- 552 billion in total. It activates 8 billion per token while reading the prompt and 16 billion while writing, and adds two large Engram lookup tables that can stay on disk.
- Is DeepSeek V4.1 Flash 1017 free?
- The weights are free to download and use under the MIT license. Using it through DeepSeek's API is paid: $0.15 per million input tokens and $0.60 per million output tokens, as listed on OpenRouter.
- Can I run DeepSeek V4.1 Flash 1017 locally?
- Yes, with a lot of memory. On a Mac, a Mac Studio with 512 GB runs the 4-bit builds and 256 GB runs a 2-bit build with the Engram tables on the SSD. Smaller Macs are better off with the API or a smaller model.
Sources
- DeepSeek API changelogapi-docs.deepseek.com
- Hugging Facehuggingface.co
- The Decoderthe-decoder.com
- V4.1 Flashhuggingface.co
- antirez on Hugging Facehuggingface.co
- OpenRouteropenrouter.ai