DeepSeek Flash inverted the economics of agent products

Hacker News

There is an adversarial relationship between developers and big model labs. Model labs charged developers higher API prices to subsidize their own agent harness offerings. Think Anthropic charging 5x higher Claude API prices to subsidize consumer subscriptions. So Cursor in a way was subsidizing their own direct competitor. DeepSeek V4 Flash totally inverted this relationship. Now you have a model that beats even Sonnet in some benchmarks and is totally opensourced. Now inference providers are racing to the bottom to optimize and give cheaper hosting. Every player with a non-SOTA is now racing to swap over to stop paying the big model lab tax, even Microsoft is switching Copilot to use DeepSeek. On switching over to Deepseek: - we noticed over a 100x cost decrease while similar or better performance then Gemini 3 Flash - insane saving from the cached input tokens: $0.002/1 Million tokens - both DeepSeek Flash and GLM 5.2 are text-only models, so clearly multimodal training is not worth the additional cost. Language is just a much more efficient sparse representation of the world/reasoning than vision - we had a early bet on a text-only web agent harness, and now with DeepSeek this results in unique cost advantages. - we rewrote our harness as a callable DSL library that a model can generate code to execute on. DeepSeek has proven phenomenal on code generation to drive an agent harness. - I would highly recommend everyone to rewrite their harness to be text-only and callable via executable code leveraging DeepSeek V4 Flash.

Visit product View on Hacker NewsJun 25, 2026

Share card

Actual performance

9points

Made the leaderboard

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: agent, cursor, claude · Missing: mac, agents, macos

96%96% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting

89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, efficient · Missing: plus, platform, intuitive

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: subscription, training · Missing: arr, mrr, revenue

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

DeepSeek-V4-Flash-073177%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Frontier agent intelligence at Flash prices

Product Hunt+324API

Warp – Run DeepSeek v4.1 Flash with 5 GB of RAM at 3.77 tok/s63%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Warp – Run DeepSeek v4.1 Flash with 5 GB of RAM at 3.77 tok/s

Hacker News9

Optimizing DeepSeek's NSA for TPUs – A Kernel Worklog48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Optimizing DeepSeek's NSA for TPUs – A Kernel Worklog

Hacker News2

DeepSeek My User Agent32%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

DeepSeek My User Agent

Hacker News360

Flashover - online flash decompiler47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Flashover - online flash decompiler

Hacker News2

Thoughts on Flash in 2023, in Flash, in 202353%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Thoughts on Flash in 2023, in Flash, in 2023

Hacker News267

ESS60 Products51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

ESS60 is the formulation of C60

Indie Hackerscommitment-full-time

Evo Dyne Products30%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Evo Dyne Products

Indie Hackers

Savvygt Savana55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Products

Product Hunt+15

InstagramApi42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Sass Products

TrustMRRDeveloper Tools

Browse all products Analyze your launch