ModelsArchitectures & capability
Creator builds a landing page with DeepSeek V4.1 Flash on Token Harbor's free tier
DeepSeek's open-weight V4.1 Flash, a 552B mixture-of-experts model with a million-token context, was used through Token Harbor's free tier to build a working landing page, while its headline coding scores depend heavily on which agent harness runs it.
From the r/AISEOInsider (via last30days) article of October 17, 2026 and cited sources: V4.1 Flash is a 552B mixture-of-experts model. [6] [8] [3]
SOURCES[6] DeepSeek-V4.1-Flash technical report, arXiv arxiv.org[8] DeepSeek-V4.1-Flash, Hugging Face huggingface.co[3] NVIDIA NIM model card docs.api.nvidia.com
AT Pulse research deskPublished · Updated · 5 min read · AI-assisted analysisAI-assisted, not human-edited. Methodology Corrections Who owns this site
- Original source
- r/AISEOInsider (via last30days) article, independent perspective
- Brief by
- AT Pulse research desk AI-assisted; publisher and contact on the desk page
- When
- October 17, 2026 article published
- Covered
- Oct 18 edition
- Evidence
- 13 sources across 11 sites
- 1I Built a Landing Page With DeepSeek V4.1 Flash for Freereddit.com
- 2Linked video: I Built a Landing Page With DeepSeek V4.1…youtube.com
- 3NVIDIA NIM model carddocs.api.nvidia.com
- 4DeepSeek API Change Logapi-docs.deepseek.com
- 5Digital Applied: Benchmarks, Prices, Pro Continuesdigitalapplied.com
- 6DeepSeek-V4.1-Flash technical report, arXivarxiv.org
QuietHow it’s measured
YouTube video · 143 views · 4 likes
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V4.1 Flash is a 552B mixture-of-experts model. It activates 8B parameters while reading a prompt and 16B while generating. It has a combined input-and-output window of about 1 million tokens, built-in image understanding, and open weights under the MIT license. Its encoder-decoder design shrinks the global KV cache to roughly a quarter of V4 Flash's. [6] [8] [3]
The 90.6 Terminal-Bench 2.1 score comes from the Minimal mode of DeepSeek's own agent harness. In Standard mode it drops to 85.8, and in Codex to 84.1. One outside test found the same checkpoint scoring between 65.5 and 74.2 on DeepSWE across eight agent setups. These are DeepSeek-reported scores, and no independent rerun of V4.1 Flash appears in the reviewed sources. [3] [5] [10]
The demo ran in the harness's Standard mode through Token Harbor's free key and produced one working landing page, with no quality measurement. Free access is an allowance that resets over a rolling seven days, and the set of free models rotates. That makes it suitable for prototyping, not production work. [1] [2] [9]
On 17 October, DeepSeek moved the deepseek-flash API name to an updated 1017 checkpoint at the same price. DeepSeek says the new checkpoint is faster, gives shorter outputs and scores 77.0% on DeepSWE. DeepSeek's change log also shows it kept serving V4 Pro, which contradicts the video's claim that V4.1 Flash answers V4 Pro requests. [4] [7] [5]
the agent harness moves DeepSeek's scores by up to about nine points, and the API name now serves a newer checkpoint. Implication: teams should test in their own agent setup and record model versions before comparing cost or quality.
Executive brief
DeepSeek's headline score for V4.1 Flash, 90.6 on Terminal-Bench 2.1, comes from the "Minimal" mode of DeepSeek's own agent harness. In "Standard" mode, the setting the linked video's creator used, DeepSeek reports 85.8 (NVIDIA model card). The Reddit post is commentary and promotes a paid community. It shows the open-weight model building a simple landing page through Token Harbor, a third-party gateway with a free tier. The model is real and documented, but every benchmark it cites is DeepSeek's own. One day before the post, DeepSeek replaced the model with an updated "1017" checkpoint.
What changed and event timeline
V4.1 Flash launches
DeepSeek released V4.1-Flash as the smallest model in a new architecture family, with built-in image understanding. API callers use the name
deepseek-flash, and the old V4 Flash names temporarily route to it ().14–17 Sep 2026: V4 Pro kept running
DeepSeek reversed a planned September 14 cutover and kept serving V4 Pro at the same billing (;). This contradicts the video's claim that V4.1 Flash answers V4 Pro requests ().
Technical report posted
DeepSeek-AI's arXiv paper describes the Causal Encoder-Decoder (CED) design and a compressed KV cache of 890 bytes per token ().
1017 checkpoint
DeepSeek released V4.1-Flash-1017, which it says runs faster, gives shorter outputs and scores 77.0% on DeepSWE. The Decoder notes that independent benchmarks are not yet available ().
Reddit/YouTube demo
The creator connects the model to Agent OS through Token Harbor's free key and builds a landing page for "Goldie Agency." The page worked, but no quality measurement is shown (;).
Capabilities and access
- Model: DeepSeek-V4.1-Flash. It is now superseded by V4.1-Flash-1017 at the same API name and price (Change Log).
- Context: 1,048,576 tokens of combined input and output (NVIDIA). Token Harbor lists a 128K maximum output (video 2:32).
- Weights: open under the MIT license on Hugging Face.
Read the full section
- Model: DeepSeek-V4.1-Flash. It is now superseded by V4.1-Flash-1017 at the same API name and price (Change Log).
- Context: 1,048,576 tokens of combined input and output (NVIDIA). Token Harbor lists a 128K maximum output (video 2:32).
- Weights: open under the MIT license on Hugging Face.
- Paid API: $0.60 per million output tokens off-peak, $1.20 at peak (Digital Applied).
- Free access: Token Harbor's free plan has an allowance that resets over a rolling 7 days and a rotating set of free models (Stork.ai; video 5:21).
Technical analysis for researchers and developers
- Architecture: a 552B mixture-of-experts model that activates 8B parameters while reading the prompt (prefill) and 16B while generating (decode).
- Memory: cross-layer KV reuse through a new attention scheme (CSA2) plus FP4 caching cuts the global KV cache to about a quarter of V4 Flash's, and the persistent cache to about an eighth.
- Evaluation settings: maximum reasoning effort, temperature 1.0, top_p 0.95 (HF card).
Read the full section
- Architecture: a 552B mixture-of-experts model that activates 8B parameters while reading the prompt (prefill) and 16B while generating (decode). V4 Flash was 284B total with 13B active. The model has a 20-layer encoder and a 20-layer decoder (HF card).
- Memory: cross-layer KV reuse through a new attention scheme (CSA2) plus FP4 caching cuts the global KV cache to about a quarter of V4 Flash's, and the persistent cache to about an eighth. Training used 45T multimodal tokens (arXiv).
- Evaluation settings: maximum reasoning effort, temperature 1.0, top_p 0.95 (HF card).
- Reproduction: the same checkpoint scores 65.5–74.2 on DeepSWE across eight agent setups. Raising reasoning effort from 25 to 100 uses about 2.5× more tokens (Digital Applied).
Claims and evidence
- Terminal-Bench 2.1: 90.6 in DeepSeek Harness Minimal, 88.0 in Claude Code, 85.8 in DeepSeek Harness Standard and 84.1 in Codex.
- Comparisons: the video's figures (Opus 5 at 89.1 and GPT 5.6 Sol at 88.8.
- Independent check: one maintainer's single-attempt Terminal-Bench 2.1 run, using OpenCode on the older V4-Flash, passed 60 of 89 tasks (67.42%) (MorphLLM).
Read the full section
- Terminal-Bench 2.1: 90.6 in DeepSeek Harness Minimal, 88.0 in Claude Code, 85.8 in DeepSeek Harness Standard and 84.1 in Codex. NVIDIA states it has not independently verified these figures (NVIDIA).
- Comparisons: the video's figures (Opus 5 at 89.1 and GPT 5.6 Sol at 88.8; DeepSWE 74.2% vs Opus 5 at 74%) come from DeepSeek's own table (video 1:22).
- Independent check: one maintainer's single-attempt Terminal-Bench 2.1 run, using OpenCode on the older V4-Flash, passed 60 of 89 tasks (67.42%) (MorphLLM). No independent rerun of V4.1 Flash appears in the reviewed sources.
- Vision scores (89.6 BabyVision, 78.9 cartography) were measured with tools turned on (video 5:12; Digital Applied).
- Token Harbor's claims of no silent model swaps and opt-in free routes are the company's own statements (video 3:43).
Context and prior work
V4.1 Flash continues DeepSeek's push toward cheap long-context inference that began with DeepSeek-V4. It is DeepSeek's first CED model (OpenRouter). Outside researchers are already working on V4 serving, such as RedKnot-MLA for long-context cache reuse (arXiv 2609.07008). Gateways like OpenRouter and Token Harbor make it easy to switch to a new model by changing one model ID.
Limitations, safety and contested findings
- The landing-page demo is a single run with no code review, no accessibility or performance checks, and no comparison against other models.
- DeepSeek labels its agent harness a developer preview and asks users to read a safety notice before running it (video 4:02).
- An Aikido security assessment tested a preview endpoint, not the production release (Digital Applied).
Read the full section
- The landing-page demo is a single run with no code review, no accessibility or performance checks, and no comparison against other models. It also used Standard mode, where DeepSeek's own Terminal-Bench score is lower.
- DeepSeek labels its agent harness a developer preview and asks users to read a safety notice before running it (video 4:02).
- An Aikido security assessment tested a preview endpoint, not the production release (Digital Applied).
- The video says Opus 5 and GPT 5.6 Sol lead on GPQA Diamond, the science reasoning test. DeepSeek reports 90.9 for Flash (HF card).
- China reportedly plans tighter controls on open-weight model releases (The Decoder).
Business and practitioner implications
- Testing: run the model in your own agent setup.
- Free tiers: they suit prototyping only. Free models rotate and allowances reset weekly, so put production work on a paid API or self-hosted weights.
- Version pinning:
deepseek-flashnow points to the 1017 checkpoint, so record which version produced any results you report.
Read the full section
- Testing: run the model in your own agent setup. Scores vary by about 6.5 points on Terminal-Bench and about 9 points on DeepSWE depending on the harness.
- Free tiers: they suit prototyping only. Free models rotate and allowances reset weekly, so put production work on a paid API or self-hosted weights.
- Version pinning:
deepseek-flashnow points to the 1017 checkpoint, so record which version produced any results you report. - Cost: The Decoder's figures for DeepSeek's update put the cost per DeepSWE task at about $0.20, against $2–10+ for competing models. That gap justifies a pilot, but code quality still needs checking.
The source trail.
Sources (13)
I Built a Landing Page With DeepSeek V4.1 Flash for Free
Feed excerpt only. A transcript of the video linked from this page is supplied (linked video auto captions; https://www.youtube.com/watch?v=irmYhIZnA8k); automatic text may contain errors.
reddit.comLinked video: I Built a Landing Page With DeepSeek V4.1 Flash for Free
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