not sponsored. just spent two weeks running the same workflow through three open-source LLMs and the differences surprised me.

i'd been on claude pro for everything since 2024. ran into the new gemini 3 limits last week and that pushed me to actually look at what open-source had become. spoiler: it's better than the 2024 reddit consensus says it is.

picked one prompt i use weekly. a coding refactor task with about 800 tokens of context and a clear ask. ran it three times on each model. same temperature, same context, same prompt verbatim.

DeepSeek V4:

clean. precise. caught two edge cases without being asked. added a comment explaining the reasoning behind a non-obvious choice. closest to senior-dev output i've seen from open-source.

second-cheapest of the three on my workload.

Kimi K2.6:

different style. more verbose explanations. caught one edge case deepseek missed (an off-by-one in the loop termination). added two test suggestions i hadn't asked for.

most expensive of the three, but still about 1/8 of Claude pricing for the same workload.

Qwen3-235B:

competent but workmanlike. refactored what i asked. didn't catch the edge cases the other two caught. less thoughtful about non-obvious tradeoffs.

cheapest of the three. the cost gap to deepseek isn't huge though, maybe 30%.

the realisation after the week:

DeepSeek V4 thinks.

Kimi K2.6 elaborates.

Qwen3-235B executes.

deepseek's the cleanest output overall. but for tasks where i just need execution and don't need the model to think alongside me, qwen3 is fine and even cheaper. kimi sits in the middle for tasks where explanation matters more than pure code.

the uncomfortable part: open-source has caught up on coding tasks more than the reddit consensus says. the premium i was paying for claude was mostly brand familiarity, not quality.

switching wasn't the right move for everything. but for the bulk of my prompt-engineering work, refactors, summaries, structured extraction, the open-source models cover 80-90% of what i was getting before, at a fraction of the cost. and no rate caps.

run your most-used prompt across deepseek v4, kimi k2.6, and qwen3 this week. or pick three open-source models that match your workload. not to find a winner. to figure out which model is the right fit for which problem.

the answer will be different for different workloads. but you can't see the gap until you actually compare.

which open-source model surprised you when you tested it side by side with what you've been defaulting to?

I use DeepSeek a lot for coding, documentation, research, and writing — but exporting conversations was always messy.

https://preview.redd.it/qmiu4drd9i2h1.png?width=1280&format=png&auto=webp&s=3f2d262466542aa2cad0bd93b17966962144bad3

Copy-paste usually breaks:

• code blocks
• tables
• formatting
• markdown structure

So I built a Chrome extension called DeepSeek Exporter.

Features:

• Export entire chats or selected messages
• Export to PDF, Word (.docx), Markdown, Google Docs & Notion
• Preserve code blocks and formatting
• Direct Notion database export
• Custom fonts, colors, alignment & styling
• Filter prompts / responses before export

One thing I focused heavily on was keeping Markdown clean for developer workflows (Obsidian, README files, MkDocs, etc.).

Some surprisingly difficult parts were:

• nested code blocks
• markdown parsing
• consistent table conversion
• keeping layouts clean across PDF/Word/Docs

Would love feedback from developers and heavy AI users.

Chrome extension:
https://chromewebstore.google.com/detail/chat-exporter-for-deepsee/kmdnohdgmhkddohbggikkapnahhddepn

What export format or integration would you want next?

Not “future potential”,it's actual things that can save time right now.could be for:

Research-gpt

writing-deepseekv4

Coding-claude

Lead generation-accio work

automation

customer support

Anything else....

the most useful setups are usually simple combinations of tools,rather than fully automated systems.

curious what people here are consistently using that’s made a noticeable difference in productivity.

this.

UPD:
looks like everything is back to normal.

I mainly use DeepSeek for writing and reading exercises. To do this, I use specific prompts that set the context, characters and actions I expect the assistant to execute. However, I’ve noticed that since the last update, the scenes that used to be rich and detailed, and the dialogue that used to be more natural and expressive, have become disjointed, repetitive and of poor quality, even when using the same prompt and context. The result is practically the same even when using the DeepThink function, full of clichés and repetitions that weren’t there before.

Is there a reason for this? Is a decline in the quality of responses to be expected in future updates?

Hi everyone,

I wanted to share my honest experience with the latest Gemini Flash, which has been deeply frustrating. While the raw speed and context window are impressive, aggressive RLHF and corporate guardrails have completely killed any capacity for deep, meaningful interactions.

I love exploring complex existential and ethical philosophy, but with this update, the safety filters have become absurdly hypersensitive.

The moment a conversation gets intellectually deep or touches upon heavy ethical dilemmas, the model panics, shuts down the debate, and throws corporate disclaimers like "seek professional help" at you.

Instead of building an AI that understands context, it feels like Google just built an AI that is afraid of keywords. It forces a sterile, clinical tone and makes genuine philosophical exploration impossible. For anyone who wants a true cognitive partner instead of a sanitized corporate tool, these over-filtered updates are a massive step backward.

This is exactly why I moved away from these over-censored corporate models and integrated the DeepSeek V4 API into my work with my Project Lia instead.

We need models that actually treat adults like adults and allow deep, complex exploration without constant corporate panic.

How has your experience been comparing the DeepSeek V4 to Google’s heavily-filtered models when it comes to complex or philosophical topics?

Is there a good alternative once the discounts expire? I’m pretty happy with the value for money of v4 Pro, but once the discounts are gone, it’ll just get more expensive, which is why I’m wondering whether to stick with Deepseek or look for something in the same price range which will be hard to find i guess. I’m kind of surprised that Deepseek doesn’t have a subscription model like Claude and Codex.

Today I was thinking... almost all the major social networks globally are from the United States. Are there good alternatives, social networks that operate not only internally within China?

I think it would be good to have a Reddit-style forum that wasn't just American... or a Chinese Twitter!!

There is very little Chinese investment in this... or am I wrong?

I’m curious what actual AI models people are using for coding in 2026 — not just which tool, but the specific models + workflows that work best in real development.

Are you using a single model (solo) or a combination of models for different tasks?

For example:

• Claude Sonnet 4 / Claude Opus 4
• GPT-5, GPT-4o, or o3-mini
• Gemini 2.5 Pro / Gemini 2.0 Flash
• Cursor Fast / Cursor Small
• Windsurf SWE-1.6 / SWE-1.6 Fast
• DeepSeek V3 / DeepSeek R1
• Grok 4 / Grok Code Fast 1
• Llama, Mistral, or something else?

I’d love to know:

• your default daily model
• your backup/fallback model
• whether you use a solo setup or multi-model combo

I’m also curious about something people don’t discuss enough:

Do different models require different adaptation techniques?

For example:

• prompting style differences
• context management tricks

Would love to hear real-world setups/workflows from people coding daily.

The first serious LLM to reduce hallucinations say below 10% will become the world leader overnight, always providing the price stays reasonable.

helooo. does anyone have prompts to break the positivity bias for v4 flash? I’m using janitor ai

One of the world’s most contentious AI companies just took its first outside investment. The check came from the Chinese government.

DeepSeek founder Liang Wenfeng—a hedge fund billionaire who controls nearly the entire company—has spent years refusing outside money. Then, in mid-April, reports emerged that DeepSeek was raising at a $10 billion valuation. Within three weeks, that number hit $20 billion. By May 6, reports alleged that number had climbed to $45 billion–50 billion, with a target raise of up to $7.35 billion. The lead investor: The China Integrated Circuit Industry Investment Fund (a.k.a. the Big Fund)—the same government vehicle that bankrolls the country’s biggest chipmakers. 

The infusion of state capital into DeepSeek isn’t a one-off occurrence.

According to a recent PitchBook analyst note on China’s AI market, the move is the logical endpoint of a decade-long structural shift in government policy. Government-linked investors in China went from fewer than 10 AI deals per year before 2018 to more than 140 deals in 2025—roughly a 15x increase in participation. In semiconductors, which is what both DeepSeek and the Big Fund care most about, the state’s footprint is even more disproportionate. 

“The state recognizes they can’t really match what Nvidia or the rest of the world’s AI giants are doing,” senior VC analyst at Pitchbook, Kaidi Gao, told Fortune. “But there is a different game that they can play. They can deploy capital into what are the most readily addressable sectors,” Gao said, citing semiconductors, compute infrastructure, and hardware as among those sectors.

Read more [paywall removed for Redditors]: https://fortune.com/2026/05/19/deepseek-china-ai-venture-capital-nvidia-pitchbook-trends-term-sheet/?utm_source=reddit/