DeepSeek V4.1 Flash 1017
Intelligence benchmarks
Artificial Analysis indexes - compared with the best open and proprietary models
Intelligence
45.1
AA Index
Intelligence Index - DeepSeek V4.1 Flash 1017 vs. the field
Best open-weight models (you can run locally) and leading proprietary models for context.
Benchmark data from Artificial Analysis · updated 2026-10-22.
Hosted API cost & speed
What it costs to rent this model instead of running it locally
| Metric | Value |
|---|---|
| Input (per 1M tokens) | $0.30 |
| Output (per 1M tokens) | $1.20 |
| Median speed (hosted API) | 203 tok/s |
Median across hosted providers, measured by Artificial Analysis - updated 2026-10-22. Local llama.cpp speed depends on your hardware — see the hardware pages for estimates.
Standard benchmarks
Performance across standard evaluations
| Benchmark | Score |
|---|---|
| HLE | 43.3 |
| SciCode | 55.9 |
Source: Artificial Analysis component evals - updated 2026-10-22.
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
vllm serve deepseek-ai/DeepSeek-V4.1-Flash-1017New to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
DeepSeek V4.1 Flash 1017 is a 763.2B-parameter mit model from DeepSeek. It scores 45.1 on the Artificial Analysis Intelligence Index. At Q4_K_M it needs roughly 443 GB of VRAM, placing it in the 48 GB+ / multi-GPU hardware tier.
Benchmarks
Artificial Analysis Intelligence Index - DeepSeek V4.1 Flash 1017 vs. leading closed models:
| Model | Intelligence | Coding | GPQA |
|---|---|---|---|
| DeepSeek V4.1 Flash 1017 | 45.1 | - | - |
| Claude Opus 5.5 | 57.6 | - | - |
| Claude Sonnet 5.5 | 56 | - | - |
| Muse Wonder Full | 55.8 | - | - |
| Claude Fable 5.1 | 53.4 | 81.6 | 93.7 |
| GPT-6 Astra | 52.7 | 76.9 | 96.1 |
Source: Artificial Analysis (2026-10-22).
Popularity
DeepSeek V4.1 Flash 1017 has 42,122 downloads in the last month on HuggingFace and 2,179 likes.
Frequently asked
Quick answers to common questions
How much VRAM does DeepSeek V4.1 Flash 1017 need?
DeepSeek V4.1 Flash 1017 with 763.2B parameters needs approximately 443 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is DeepSeek V4.1 Flash 1017 better than other deepseek-ai models?
DeepSeek V4.1 Flash 1017 has 763.2B parameters with 1,048,576 context - a strong choice for general use.
What license is DeepSeek V4.1 Flash 1017 under?
DeepSeek V4.1 Flash 1017 is released under the mit license, making it suitable for most commercial and personal projects.
What hardware runs DeepSeek V4.1 Flash 1017 well?
With 763.2B parameters, DeepSeek V4.1 Flash 1017 requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for DeepSeek V4.1 Flash 1017?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~542 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.
How long can DeepSeek V4.1 Flash 1017's context window handle?
DeepSeek V4.1 Flash 1017 supports a 1,048,576-token context window - enough for very long documents, codebases, or multi-turn conversations. Real-world usable context may vary by implementation.
What models compete with DeepSeek V4.1 Flash 1017?
DeepSeek V4.1 Flash 1017 competes with other 382B–1145B. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Nearby options
Similar models and compatible hardware by spec