Day 7 · 2026-10-30
15 launches checked60 claims quoted word for word87 user reports linked10 verified fixes linked6 corrections publishedLive 7 days5 of 5 founding sponsor spots left
Day 7Launch day vs. day 7
AI launches, checked against real usersLast re-stamped 30 Oct 2026

AI models / Compare

DeepSeek-V4.1-Flash-1017 vs EmbeddingGemma 2: which delivered after 7 days?

EmbeddingGemma 2 got the stronger stamp (Delivered) against DeepSeek-V4.1-Flash-1017 (Partly), based on the user reports linked on each receipt.

DeepSeek-V4.1-Flash-1017EmbeddingGemma 2
VerdictPartlyDelivered
Claims held24
Claims mixed21
Claims failed00
Claims unclear00
User reports1214
Launched17 Oct 20266 Oct 2026
Last stamped25 Oct 202625 Oct 2026

No. 008 · DeepSeek

DeepSeek-V4.1-Flash-1017

PartlyDay 7 verdict

It is live and MIT-licensed as promised, but users split on whether it finishes faster and codes better; some see shorter runs, others overthinking.

Launched
17 Oct 2026
Stamped
25 Oct 2026
Claims
✓2 held · ~2 mixed · ✗0 failed · ?0 unclear
Reports
12 (4 praise · 4 problem · 4 mixed)

What they claimed

“Now live on our website, app, and API.”
✓ Holds
“Stronger benchmark performance, faster task completion, and shorter outputs than the previous version”
~ Mixed
“DeepSeek-V4.1-Flash-1017 uses reinforcement learning with self-feedback to improve end-to-end project development”
~ Mixed
“This repository and the model weights are licensed under the MIT License.”
✓ Holds

Full receipt

No. 009 · Google

EmbeddingGemma 2

DeliveredDay 7 verdict

The open weights, the shared text-image-audio space and the code gains check out in independent tests; text-only users get no boost, as Google said.

Launched
6 Oct 2026
Stamped
25 Oct 2026
Claims
✓4 held · ~1 mixed · ✗0 failed · ?0 unclear
Reports
14 (6 praise · 5 problem · 3 mixed)

What they claimed

“Today, we’re launching EmbeddingGemma 2, expanding beyond text to unify code, images, video, and audio in a shared embedding space.”
✓ Holds
“Built on the Gemma 4 architecture and released under a commercially permissive Apache 2.0 license, EmbeddingGemma 2 has 740 million parameters, making it optimal for on-device inference.”
✓ Holds
“Using Matryoshka Representation Learning (MRL), developers can dynamically truncate output vectors from 768 dimensions down to 512, 256, or 128 dimensions.”
✓ Holds
“EmbeddingGemma 2 matches the strong multilingual text performance of EmbeddingGemma while delivering a significant 9.92-point improvement on code performance (in MTEB Code, from 68.76 to 78.68), making it well-suited for local codebase indexing, semantic code search, and coding agent retrieval.”
✓ Holds
“We worked closely with the following partners to ensure EmbeddingGemma 2 works immediately where you build:”
~ Mixed

Full receipt

Both stamps come from the public user reports linked on each receipt, under the same method. Verdicts are opinion and are never paid for.

Keep comparingall AI models