DeepSeek V4 Flash

Hey, thought I share it, used to be on Claude a lot, and at work on GPT/Codex via OpenCode. Anyhow Claude sucks and so I went 2 months ago on a path to try things in paralell. And now not so long ago when the Claude Subscription ran out, to fully replace it with Chinese models at home. So, long story short. My main drive is Minimax, with Kimi for understanding screenshots, via copy and paste. But sometimes these get stuck. And the xiaomi model is good, but slow and expensive, deepseek v4 pro is good but slow and innefficient and expensive. So did like two days now ,with the deepseek-v4-flash, and based on that I can recommend it. Crazy fast, 100 to 150 TPS, super good. It is a bit outdated and needs reminding (e.g. context7) on what we use these days, but oh man is it good, helps get stuff unstuck. So if you didn't already, It is probably less than $1 a day of API usage, try it. This is the good stuff, cheap, crazy fast, and good. Fuck claude and anthropic.

I started using this model after seeing positive reviews about it both on Reddit and in X. This is the closest thing to Opus 4.5 since Opus 4.5 (haven't tried Chat GPT 5.5). If you haven't tried it yet and you want a good model with competitive pricing for software engineering, you are missing out. Good luck! ————- EDIT ————- Most people pointed out this model is great at instruction following and implementation. For planning, I should use the pro variant or Claude’s main models (Opus or Sonnet). Thanks for the feedback, folks. r/opencode - DeepSeek V4 Flash via OpenCode Go (https://preview.redd.it/deepseek-v4-flash-is-magical-v0-herrkbjda85h1.png?width=776&format=png&auto=webp&s=d15be98a8a60526762eb1f29018e2da5bf111585) DeepSeek V4 Flash via OpenCode Go

In case you're not aware already, the DeepSeek V4 series is finally getting supported on llama.cpp with this PR (https://github.com/ggml-org/llama.cpp/pull/24162)! The PR is at a very early stage right now, so only try it if you're consciously willing to experiment out of curiosity and accept severe stability/performance tradeoffs. It runs very slow (5-6 tps), GPU and FA support need work, etc., but it is reliable-enough already for correctness. This is my most anticipated model and I had some time to spare, so I ended up downloading the HF model for DS-V4-Flash and quantizing it myself using the PR(Made a custom 3-bit quant to mimic the full-sized model's tensor layout). And wow! The model perfectly addresses the crucial three pillars for local inference IMO: The model's intelligence is amazing for its size. First time a local model in this size range actually feels comparable to frontier models, and I'm not exaggerating. Fares a lot better against quantization since it's natively an FP4-FP8 hybrid. This is crucial for local deployment and is my primary problem with models like MiniMax M2.7, where I'm not happy even with UD-Q4_K_XL. Incredibly efficient with context window scaling. Consumes way less KV cache size with no flash attention! Qwen 3.5/3.6 series is also a huge hit amongst the local community since it addresses the three pillars above way better than its competitors. However, I feel the DeepSeek model has levelled it up even further, and I predict it will easily dominate the 80-140GB model space for many more months to come. Huge shoutout and thanks to fairydreaming for their relentless work on getting DSA implemented (https://github.com/ggml-org/llama.cpp/pull/21149), and to am17an and pwilkin for taking this up! Really looking forward to this PR getting merged!