DeepSeek V4.1 Flash 1017
DeepSeekKey deepseek-v4.1-flash-1017Also reported as deepseek/deepseek-v4.1-flash-1017, deepseek-v4.1-flash:1017, ali/deepseek-v4.1-flash-1017, deepseek-ai/deepseek-v4.1-flash-1017, DeepSeek-V4.1-Flash-1017, alicn/deepseek-v4.1-flash-1017, sk/deepseek-v4.1-flash-1017, group/deepseek-v4.1-flash-1017, modelscope/deepseek-ai/deepseek-v4.1-flash-1017, @cf/deepseek-ai/deepseek-v4.1-flash-1017, codearts/deepseek-v4.1-flash-1017, cline-pass/deepseek-v4.1-flash-1017, cline-2/deepseek/deepseek-v4.1-flash-1017, cline/cline-pass/deepseek-v4.1-flash-1017, dahono/deepseek-v4.1-flash-1017
- Rank
- #25
- by active users
- Active users
- 30
- Down 4 from 34 active users in the previous 7 days. vs previous 7 days
- Adoption
- 4.9%
- of 613 accounts
- Tokens
- 19B
- cache included
- Est. cost
- $192
- at public list prices
- First seen
- Sep 24, 2026
- in any submission
Nov 7–13, 2026 (UTC)613 accounts100% of tokens from an identified modelData through Nov 13, 2026, 07:09 UTCHow we count
Daily active users
Distinct accounts that used this model on each UTC day.
4 active users on Nov 13, 2026
Show daily values
| Date (UTC) | Active users | Tokens |
|---|---|---|
| Nov 7, 2026 | 17 | 3B |
| Nov 8, 2026 | 15 | 2.7B |
| Nov 9, 2026 | 21 | 3.9B |
| Nov 10, 2026 | 17 | 4.1B |
| Nov 11, 2026 | 15 | 4B |
| Nov 12, 2026 | 14 | 1.3B |
| Nov 13, 2026 | 4 | 18.9M |
Token mix
How the 19B tokens split. Cache reads are usually most of an agent's tokens and are priced far below input.
- Input4.9%
- Output0.2%
- Cache read95%
- Cache write0%
- Reasoning<0.1%
Agents that carry DeepSeek V4.1 Flash 1017
Each agent's share of this model's tokens, and how many accounts used the model through it.
Oh My Pi45% of this model's tokens5active users
OpenCode33% of this model's tokens7active users
Claude Code16% of this model's tokens4active users
Hermes Agent3.5% of this model's tokens9active users9Router1.0% of this model's tokens1active users
OpenClaw0.8% of this model's tokens2active users
DeepSeek Harness0.5% of this model's tokens3active users
ZCode0.4% of this model's tokens1active users
Pi0.4% of this model's tokens5active users
Command Code<0.1% of this model's tokens1active users
Goose<0.1% of this model's tokens1active users- cc-mirror/curl<0.1% of this model's tokens1active users
Benchmarks
The best result for DeepSeek V4.1 Flash 1017 in each benchmark Tokscale tracks. Each benchmark has its own unit, run conditions and source, so compare a score only with other models on the same benchmark. Rank is among the models Tokscale matched in that benchmark.
- Epoch Capabilities IndexOverall · Epoch AI159.4
Epoch Capabilities Index: 159.4
- Model
- DeepSeek V4.1 Flash 1017
- Listed as
- DeepSeek V4.1 Flash 1017
- Conditions
- Not reported
- Interval
- 157.0–162.0
- Unit
- ECI
- Model released
- Oct 17, 2026
- Retrieved
- Nov 12, 2026
Source · Epoch AI, ‘Capabilities & benchmarking’, epoch.ai/benchmarks (CC BY 4.0)
Rank #22 of 158 matched modelsNo conditions reportedModel released Oct 17, 2026Source: epoch.ai (opens in a new tab) - CursorBenchAgentic coding · Cursor, collected by Epoch AI47.7%
CursorBench: 47.7%
- Model
- DeepSeek V4.1 Flash 1017
- Listed as
- DeepSeek V4.1 Flash 1017 Max
- Conditions
- Max
- Unit
- score
- Model released
- Oct 17, 2026
- Retrieved
- Nov 12, 2026
Best of 4 reported results for this model.
Source · CursorBench results by Cursor, collected by Epoch AI, ‘Capabilities & benchmarking’, epoch.ai/benchmarks (CC BY 4.0)
Show 3 more results with other run conditionsHide the other results
- Listed as DeepSeek V4.1 Flash 1017 Medium46.3%
CursorBench: 46.3%
- Model
- DeepSeek V4.1 Flash 1017
- Listed as
- DeepSeek V4.1 Flash 1017 Medium
- Conditions
- Medium
- Unit
- score
- Model released
- Oct 17, 2026
- Retrieved
- Nov 12, 2026
Source · CursorBench results by Cursor, collected by Epoch AI, ‘Capabilities & benchmarking’, epoch.ai/benchmarks (CC BY 4.0)
- Listed as DeepSeek V4.1 Flash 1017 High45.6%
CursorBench: 45.6%
- Model
- DeepSeek V4.1 Flash 1017
- Listed as
- DeepSeek V4.1 Flash 1017 High
- Conditions
- High
- Unit
- score
- Model released
- Oct 17, 2026
- Retrieved
- Nov 12, 2026
Source · CursorBench results by Cursor, collected by Epoch AI, ‘Capabilities & benchmarking’, epoch.ai/benchmarks (CC BY 4.0)
- Listed as DeepSeek V4.1 Flash 1017 Low44.9%
CursorBench: 44.9%
- Model
- DeepSeek V4.1 Flash 1017
- Listed as
- DeepSeek V4.1 Flash 1017 Low
- Conditions
- Low
- Unit
- score
- Model released
- Oct 17, 2026
- Retrieved
- Nov 12, 2026
Source · CursorBench results by Cursor, collected by Epoch AI, ‘Capabilities & benchmarking’, epoch.ai/benchmarks (CC BY 4.0)
- GPQA DiamondReasoning · Epoch AI93.2%
GPQA Diamond: 93.2%
- Model
- DeepSeek V4.1 Flash 1017
- Listed as
- deepseek-v4.1-flash-1017_max
- Conditions
- Max
- Unit
- % correct
- Run date
- Oct 19, 2026
- Retrieved
- Nov 12, 2026
Source · Epoch AI, ‘Capabilities & benchmarking’, epoch.ai/benchmarks (CC BY 4.0)
Near its ceiling: the top score is 96.5%, and Epoch AI estimates the reachable maximum at 97%. Differences between top models are small.
- FrontierMath Tiers 1–3 (v2)Reasoning · Epoch AI71.2%
FrontierMath Tiers 1–3 (v2): 71.2%
- Model
- DeepSeek V4.1 Flash 1017
- Listed as
- deepseek-v4.1-flash-1017_max
- Conditions
- Max
- Unit
- % solved
- Run date
- Oct 19, 2026
- Retrieved
- Nov 12, 2026
Source · Epoch AI, ‘Capabilities & benchmarking’, epoch.ai/benchmarks (CC BY 4.0)
Near its ceiling: the top score is 95.1%, and Epoch AI estimates the reachable maximum at 95%. Differences between top models are small.
- SimpleQA VerifiedKnowledge · Epoch AI42.5%
SimpleQA Verified: 42.5%
- Model
- DeepSeek V4.1 Flash 1017
- Listed as
- deepseek-v4.1-flash-1017_max
- Conditions
- Max
- Unit
- % correct
- Run date
- Oct 19, 2026
- Retrieved
- Nov 12, 2026
Source · Epoch AI, ‘Capabilities & benchmarking’, epoch.ai/benchmarks (CC BY 4.0)
Pricing
List prices from models.dev in US dollars per 1M tokens, retrieved Nov 12, 2026. DeepSeek is the lab's own API and the reference price for the estimates below.
Price at real agent usageEstimate at list prices
What 1M tokens cost at the reference list price when they split into input, output and cache the way Tokscale users' agents used them. It is not what anyone paid.
- At this model's mix
- $0.01
- Est. per 1M tokens
95% cache reads - At the community mix
- $0.01
- Est. per 1M tokens
96% cache reads - 3:1 input/output blend
- $0.26
- Per 1M tokens at list prices
No cache
Cache reads were 95% of DeepSeek V4.1 Flash 1017 tokens in the last 7 days and 96% of all tokens. The 3:1 blend, (3 × input + output) ÷ 4, is the usual comparison and ignores cache.
Assumptions
- DeepSeek list prices per 1M tokens: input $0.15, output $0.60, cache read $0.003.
- This model's mix is its own input, output, cache and reasoning tokens in the last 7 days.
- The community mix is the tokens of every identified model in the last 7 days.
- This is an estimate at list prices, not what anyone paid.
- Reasoning tokens are priced as output.
- Long-context tiers, batch pricing and discounts are not applied.
- Token mixes come from public Tokscale submissions. Hidden accounts are excluded.
- No cache-write price is listed, so cache writes are priced as input.
List prices by provider
- DeepSeekReference
deepseek-flash$0.15 input per 1M tokens$0.60 output per 1M tokens$0.003 cache read per 1M tokensCache write not listed1.05M tokens of context - OpenRouter
deepseek/deepseek-v4.1-flash-1017$0.006 input per 1M tokens$0.50 output per 1M tokens$0.005 cache read per 1M tokensCache write not listed1.05M tokens of context - Vercel AI Gateway
deepseek/deepseek-v4.1-flash-1017$0.30 input per 1M tokens$1.20 output per 1M tokens$0.007 cache read per 1M tokensCache write not listed1.05M tokens of context - Fireworks AI
accounts/fireworks/routers/deepseek-flash-latest$0.30 input per 1M tokens$1.20 output per 1M tokens$0.006 cache read per 1M tokensCache write not listed1M tokens of context
Show 2 more providersShow fewer providers
- Deep Infra
deepseek-ai/DeepSeek-V4.1-Flash-1017$0.20 input per 1M tokens$0.60 output per 1M tokens$0.006 cache read per 1M tokensCache write not listed1.05M tokens of context - Baseten
deepseek-ai/DeepSeek-V4.1-Flash-1017$0.30 input per 1M tokens$1.20 output per 1M tokens$0.007 cache read per 1M tokensCache write not listed1.05M tokens of context
Specs
From models.dev (MIT), retrieved Nov 12, 2026, as the lab or the first listed provider reports them. A dash means models.dev does not list the value.
- Context window
- 1.05M tokens
- Max output
- 393K tokens
- Input
- Text, Image
- Output
- Text
- Knowledge cutoff
- May 2025
- Release date
- Oct 17, 2026
- Open weights
- Yes
- Reasoning
- Yes
- Tool calling
- Yes
Top public profiles
Public profiles that used DeepSeek V4.1 Flash 1017 most in this period, by tokens. Hidden accounts are never listed.
- 1
Brad Lishman@bradlishman7.8Btokens$62est. cost7active days$62 est. cost · 7 active days
- 2
Andrei Branga@andreibranga5.4Btokens$65est. cost6active days$65 est. cost · 6 active days
- 3
Park Hee Chan@channprj2.5Btokens<$1est. cost2active days<$1 est. cost · 2 active days
- 4
mandoworks@mandoworks603.7Mtokens$7est. cost6active days$7 est. cost · 6 active days
- 5
Donghyeon Seo@donghyeon23414.1Mtokens$7est. cost4active days$7 est. cost · 4 active days
- 6
Jelloeater@Jelloeater392Mtokens—est. cost7active daysno price · 7 active days
- 7
MinYoul Yang@alpoxdev351.9Mtokens—est. cost4active daysno price · 4 active days
- 8
xingguangcuican@xingguangcuican6666338.7Mtokens$15est. cost3active days$15 est. cost · 3 active days
- 9
Kukuh@kukuhpe229.4Mtokens$23est. cost5active days$23 est. cost · 5 active days
- 10
iM 4 S@iMashedGT181.5Mtokens$3est. cost4active days$3 est. cost · 4 active days
More from DeepSeek
Other DeepSeek models in the same period, by active users.
- DeepSeek V4.1 Flash15% adoption94active users
- DeepSeek Flash6.2% adoption38active users
- DeepSeek V4.1 Flash Free5.5% adoption34active users
- DeepSeek V4 Pro1.5% adoption9active users
- DeepSeek V4 Flash1.3% adoption8active users
- DeepSeek V4.1 Flash 1M1.0% adoption6active users
Sources and attribution
Each score keeps its own source, unit and run conditions. Tokscale does not combine benchmarks into one score, and a missing score means the model was not evaluated, not that it scored zero.