DeepSeek · Open weights
DeepSeek V4.1 Flash 1017
The October 17, 2026 snapshot of DeepSeek V4.1 Flash supports text and image input, tool calling, and optional reasoning with low, high, and max effort. It has a shared 1,048,576-token context window and supports up to 393,216 output tokens within the remaining context. Released under the MIT license.
- Reasoning
- Tools
- Vision
- Structured output
- Input
- Text, Images
- Output
- Text
- Reasoning effort
- none · low · high · max
- Discovery tier
- Open weights
Pricing
USD per 1M tokens- Input
- $0.10
- Output
- $0.60
- Cache read
- $0.003
Reference prices as published by the model provider. API example
Specifications
- Context window
- 1M
- Max output
- 393.2K
- Average output (7 days)
- 465 tokens
- Longer than 46% of 465 models
Side by side
DeepSeek V4.1 Flash 1017 compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| DeepSeek V4.1 Flash 1017DeepSeekThis model | DeepSeek V4.1 Flash TEEDeepSeekRemove | |
|---|---|---|
| Provider | DeepSeek | DeepSeek |
| Discovery tier | Open weights | Open weights |
| Added | Oct 17, 2026 | Sep 12, 2026 |
| Limits | ||
| Context window | 1.05M | 1.05M |
| Max output | 393.22KBest | 384K |
| Average output | 465 tokens | 571 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.10Best | $0.65 |
| Output | $0.60Best | $1.45 |
| Cache read | $0.003Best | $0.13 |
| Performance | ||
| Throughput | 154.0 tok/s | 239.9 tok/sBest |
| Time to first token | 2.2 sBest | 3.0 s |
| Request success (24h) | 99.7% | 99.9%Best |
| Benchmark indexes | ||
| Intelligence Index | 24.7 | 24.7 |
| Capabilities | ||
| Reasoning | Yes | Yes |
| Tools | Yes | Yes |
| Vision | Yes | Yes |
| Structured output | Yes | Yes |
| Parallel tools | No | Yes |
| Reasoning effort | none, low, high, max | none, low, medium, high, max |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- DeepSeek V4.1 Flash 1017
- DeepSeek V4.1 Flash TEE
Intelligence Index
- DeepSeek V4.1 Flash 101724.7
- DeepSeek V4.1 Flash TEE24.7
DeepSeek V4.1 Flash 1017: Better than 60% of 326 scored models
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- DeepSeek V4.1 Flash 1017: 47.8%Top
- DeepSeek V4.1 Flash TEE: 47.8%Top
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- DeepSeek V4.1 Flash 1017: 14.2%Top
- DeepSeek V4.1 Flash TEE: 14.2%Top
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- DeepSeek V4.1 Flash 1017: 1,118 EloTop
- DeepSeek V4.1 Flash TEE: 1,118 EloTop
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- DeepSeek V4.1 Flash 1017: 1,340 EloTop
- DeepSeek V4.1 Flash TEE: 1,340 EloTop
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- DeepSeek V4.1 Flash 1017: 3.8%Top
- DeepSeek V4.1 Flash TEE: 3.8%Top
AA-LCRv1.1
Reasoning across long documents
- DeepSeek V4.1 Flash 1017: 55.3%Top
- DeepSeek V4.1 Flash TEE: 55.3%Top
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- DeepSeek V4.1 Flash 1017: 10.8%Top
- DeepSeek V4.1 Flash TEE: 10.8%Top
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- DeepSeek V4.1 Flash 1017: 5.6%Top
- DeepSeek V4.1 Flash TEE: 5.6%Top
SciCode
Scientific computing in Python
- DeepSeek V4.1 Flash 1017: 35.5%Top
- DeepSeek V4.1 Flash TEE: 35.5%Top
Legacy evaluations
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- DeepSeek V4.1 Flash 1017: 55.3%Top
- DeepSeek V4.1 Flash TEE: 55.3%Top
Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis
- DeepSeek V4.1 Flash 1017
- DeepSeek V4.1 Flash TEE
| Leaderboard | DeepSeek V4.1 Flash 1017 | DeepSeek V4.1 Flash TEE |
|---|---|---|
| ARC-AGI-1 | #17 · 94.5% | Not in top 20 |
| ARC-AGI-2 | #16 · 78.1% | Not in top 20 |
Ranks among Noumen catalog models on ARC Prize leaderboards. Only top-20 placements are listed.ARC Prize
- DeepSeek V4.1 Flash 1017
- DeepSeek V4.1 Flash TEE
| Leaderboard | DeepSeek V4.1 Flash 1017 | DeepSeek V4.1 Flash TEE |
|---|---|---|
| Website | #17 · 1300 | Not in top 20 |
| UI Component | #16 · 1301 | Not in top 20 |
| SVG | #18 · 1242 | Not in top 20 |
| 3D | #12 · 1361 | Not in top 20 |
| ASCII Art | #18 · 1221 | Not in top 20 |
Ranks among Noumen catalog models on Design Arena leaderboards. Only top-20 placements are listed.Design Arena
- DeepSeek V4.1 Flash 1017
- DeepSeek V4.1 Flash TEE
| Leaderboard | DeepSeek V4.1 Flash 1017 | DeepSeek V4.1 Flash TEE |
|---|---|---|
| Usage Share | #10 · 2.2% | Not in top 20 |
Ranks among Noumen catalog models on Usage leaderboards. Only top-20 placements are listed.
Live routing data
Performance and reliability
Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.
- Throughput
- 154.0 tok/s
- Faster than 76% of 250 models
- Time to first token
- 2.2 s
- Quicker than 74% of 250 models
- Prompt cache hits
- 87.0%
- About $0.0156 per 1M input tokens with caching
- Request success (24h)
- 99.7%
- Reliable
Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.
- Healthy (98% or more)
- Degraded (90–98%)
- Down (under 90%)
- No requests
Developers
Use it through the API
Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.
curl https://www.noumen.xyz/api/v1/chat/completions \
-H "Authorization: Bearer $NOUMEN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek/deepseek-v4.1-flash-1017",
"reasoning_effort": "none",
"messages": [{ "role": "user", "content": "Explain this stack trace." }]
}'import os
from openai import OpenAI
client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
model="deepseek/deepseek-v4.1-flash-1017",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)DeepSeek
Related models
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