DeepSeek V4 Flash vs Amazon Nova Pro

WTF, I could just use the expensive ai models of opencode go for planning, writing specs and then use opencode zen deepseek v4 flash max for implementation. I am loving this opencode, loving the freebies

Just wanted to share that I used u/LegacyRemaster slightly modified (Q4\_K\_M conversion support) DeepSeek V4 [CUDA repo](https://github.com/Fringe210/llama.cpp-deepseek-v4-flash-cuda) (based on u/antirez [work](https://github.com/antirez/llama.cpp-deepseek-v4-flash)) to convert and run Q4\_K\_M [DeepSeek V4 Pro](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro) on my Epyc workstation (Genoa 9374F, 12 x 96GB RAM, single RTX PRO 6000 Max-Q) and it worked right from the start:

(base) phm@epyc:~/projects/llama.cpp-deepseek-v4-flash-cuda/build-cuda$ ./bin/llama-cli -m ../models/DeepSeek-V4-Pro-Q4_K_M.gguf --no-repack -ub 128 --chat-template-file ../models/templates/deepseek-ai-DeepSeek-V3.2.jinja
ggml_cuda_init: found 1 CUDA devices (Total VRAM: 97247 MiB):
Device 0: NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition, compute capability 12.0, VMM: yes, VRAM: 97247 MiB

Loading model...

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build : b8936-44c7b01de
model : DeepSeek-V4-Pro-Q4_K_M.gguf
modalities : text

available commands:
/exit or Ctrl+C stop or exit
/regen regenerate the last response
/clear clear the chat history
/read <file> add a text file
/glob <pattern> add text files using globbing pattern

> who are you?

[Start thinking]
Okay, the user is asking "who are you?" This is a simple, introductory question. I need to introduce myself clearly and warmly. I should state my name, creator, and key features that are most relevant to a new user. I can mention that I'm free, my context window, knowledge cutoff, file support, and availability on web and app. I'll end with an open invitation for further questions to keep the conversation going.
[End thinking]

Hi there! I'm DeepSeek, an AI assistant created by the Chinese company DeepSeek (深度求索). I'm here to help you with questions, creative tasks, problem-solving, and pretty much anything you're curious about!

Here's a bit about me:
- **Free to use** - no charges for chatting with me
- **1M context window** - I can handle huge amounts of text at once (like entire book trilogies!)
- **Knowledge cutoff: May 2025** - I'm reasonably up-to-date
- **File upload support** - I can read text from images, PDFs, Word docs, Excel files, and more
- **Web search capability** - though you need to manually enable it via the search button
- **Available on web and mobile app** - with voice input support on the app

I'm a pure text-based model, so I can't "see" images directly, but I can read any text in uploaded files. I aim to be warm, helpful, and detailed in my responses.

What can I help you with today? 😊

[ Prompt: 12.2 t/s | Generation: 8.6 t/s ]

> /exit

Exiting...
common_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted |
common_memory_breakdown_print: | - CUDA0 (RTX PRO 6000 Blackwell Max-Q Workstation Edition) | 97247 = 4022 + ( 92472 = 87766 + 84 + 4621) + 753 |
common_memory_breakdown_print: | - Host | 793994 = 793954 + 0 + 39 |
~llama_context: CUDA_Host compute buffer size of 39.1719 MiB, does not match expectation of 15.3535 MiB

The model file is 859GB.

Update: ran some lineage-bench prompts to see if the model has healthy brain and no problems so far.

So, I was testing DeepSeek-R1 with a math problem I found in a textbook for 9-year-olds **(yes, really)**, and the model managed to crack it.

The problem was:

`"Find two 3-digit palindromic numbers that add up to a 4-digit palindromic number. Note: the first digit of any of these numbers can't be 0."`

[R1 starts thinking...](https://preview.redd.it/ml5hnng3rwge1.jpg?width=1800&format=pjpg&auto=webp&s=1456610eeff8d8b9a122d86fbb44967f84f682d9)

Now, here’s where it gets interesting. R1 thought for a bit, found the correct answer in its `<think></think>` block, then went ahead to output it—but made a mistake.

[R1 makes a mistake...](https://preview.redd.it/77bke6q1swge1.jpg?width=1800&format=pjpg&auto=webp&s=d6eac07677fe576be9e699776a2134cba1d15c62)

Before even finishing its response, it caught its own error, backtracked, and corrected itself on the fly outside of the`<think></think>` block.

[R1 corrects itself...](https://preview.redd.it/yc3zjamsswge1.jpg?width=1800&format=pjpg&auto=webp&s=903d42998593e95a68ff32006b7bac6335df9f1e)

[R1's final answer.](https://preview.redd.it/j8vgvxn3twge1.jpg?width=1800&format=pjpg&auto=webp&s=b189fce4a099ed9182b315c2164a1071a4a32104)

[DeepSeek-R1 complete answer.](https://pastebin.com/0Ayv77LN)

Regarding the problem, **no other LLM solved it, except for** [**OpenAI o1**](https://pastebin.com/YCRR521W).

So now I’m wondering—**what's holding them back?** Is it the tokenizer's weaknesses? The sampling parameters (even when all where at the recommended settings they failed)? Or maybe, just maybe, non-thinking LLMs are really that bad at math?

Would love to hear thoughts on this.

Unsuccessful attemps by other models:

* [chatgpt-4o-latest-20241120](https://pastebin.com/r8VKHrcA)
* [claude-3-5-sonnet-20241022](https://pastebin.com/tXc7wGVz)
* [phi-4](https://pastebin.com/zGzQJ8B5)
* [amazon-nova-pro-v1.0](https://pastebin.com/vt54UFBe)
* [gemini-exp-1206](https://pastebin.com/eSN4y6E0)
* [llama-3.1-405b-instruct-bf16](https://pastebin.com/jVj1KcMF)
* [qwen-max-2025-01-25](https://pastebin.com/ZRLfhEfU)

Hi all.

I've been testing out different models and providers to see what is the best bang for buck you can get for around $20 if you are not running local models.

I have a Hermes agent running on a VM with 6GB RAM, which I got for an absolute steal of $45 per year (check out the LowEndTalk forum for cheap VPS deals). I use it mainly to maintain a dashboard that does the following:

* Gather news on specific topics from various sources. It then curates them to see if they align with my interests (eg. no sensasionalist crap), summarizes and deduplicates articles.
* Check the latest benchmarks on different models
* Scrape my favourite webcomics from Instagram, RSS feeds, Bluesky, whatever, so they are all in one place.

It also maintains the VPS, so I have it install docker containers for stuff I want, like Mealie or whatever.

Lastly, I synced my Obsidian vault where I keep a list of people with birthdays, notes etc. So it can remind me who's birthday it is and what I can buy for them, or other stuff like that. My Obsidian is also where it keeps track of my health stuff. Diet, gym log, etc.

So, I've been playing around with the following providers. In all cases except Codex and OpenRouter, I used Kimi K2.6 as my main model, and usually tried Gemma4 for some of the tools and auxiliary models:

* Ollama Cloud - $20 per month
* OpenCode Go - $10 per month
* NanoGPT - $12 per month (I think you can get $8 if you find a ref link)
* OpenAI Codex - $20
* OpenRouter - Free Models only

Here are my findings.

# Ollama Cloud

Very stable. Charges per GPU hours instead of tokens, so as models get more efficient, you actually gain mode usage. Some people say it's a bit slow, but in my experience it was never slow enough to be problematic.

I actually had a hard time hitting my usage limits. I had to run my Hermes Agent, as well as 2 pretty big coding tasks simultaneously before I hit my 5 hour window limit, and this only happened once. The rest of the time, I barely cracked 25%. For Hermes alone, you will likely never hit that limit.

Cons, are that you are limited to 3 concurrent connections. Meaning, my example of 2 coding cases and Hermes was pushing it. If I had to chat to Hermes and a cron job fired that used a model, it errored out because I went over the limit of 3 connections. This is something to keep in mind for people running multiple agents or lots of cron jobs and such.

# OpenCode Go

I felt like this was ever so slightly less stable than Ollama, but not enough to be a problem or to stay away from it. Speed was fine, I honestly didn't feel much of a difference between OpenCode and Ollama. You pay $10 per month, and essentially get $60 worth of credits.

One might think $60 credits is not much, but whether it is an efficiency thing or just the fact that we aren't paying Anthropic pricing, it stretched very far. I never hit my limits. Just like Ollama, on average usage I barely got to 25-30% weekly. Unlike Ollama, you don't have concurrency limits.

The con for me is that it didn't have the model I wanted for tool calls, Gemma 4. They don't have that on here. They have DeepSeek which is cheap and fast, but Gemma 4 is cheap, fast AND multimodal. Useful for curating news articles or webcomics.

# NanoGPT

This one seemed sketchy AF at first. It's clearly meant for a specific crowd. It has a ton uncensored text models included in the sub, as well as uncensored image models (Qwen Image and Z Image Turbo) with 100 free image generations per day. They allow you to load up with crypto (or visa if you don't have crypto) and sign in with only a passkey, no need to enter an email or anything, allowing for a degree of anonymity.

Kimi on this one was VERY verbose. It thought a lot, and then would output that as messages in Telegram, meaning the chat context grew very, very fast and had to compress every couple of messages. They had Gemma 4 though (a bunch of variations), and using them for tool calls worked fine. Of this list, NanoGPT had the most models available on the sub. Usage limits seemed a lot lower than Ollama and OpenCode. Also worth noting, since the model naming on this one is a bit weird, if you are relying on your main model to maintain it's own config, you need to give it the *exact* model you want to use. If you just tell it to use "Gemma 4" then high chance it will take the one not in your sub and complain about you needing to top up credits first.

# Codex

Currently testing. Ran it for a day and weekly usage is already at 30%. Didn't even push it that hard. Using GPT 5.5 on it. It feels like it is running an excessive number of tool calls whenever I give it a task. Doing random searches, terminal commands, notes, etc. I'll see if I hit my weekly in 3 days or not. I probably will.

# OpenRouter

The standard free models are extremely unreliable and often hit rate limits. However they also frequently have preview models that work very nicely for a week or 3, and are worth at the very least using for tool calls. They recently had Tencent Hy3 for free which even now is topping the LLM Leaderboard on OpenRouter. It is very much worth having an OR API key in your back pocket that you can plug into an auxiliary function or some cron jobs to save usage when things like this happen.

# Honorable Mention

**Nous Portal** \- You pay $20, you get $22 credits. Not a lot of savings. However they do have some free models from time to time as well. Right now they have Step 3.5 Flash and Deepseek V4 Flash for free. Need to top up your wallet before you can use them though. Like OpenRouter, worth having a key in your back pocket for the occasional freebie.

# My plan going forward

Once this month's codex runs out, I think I will likely stick with **OpenCode Go + NanoGPT**. I will use OpenCode Go for my main model, profiles, and maybe a bit of coding, and NanoGPT for auxiliary models and free image generation. I am paying $8 per month for Nano instead of $12, not sure how I got that discount, think it was an affiliate link probably. This means, my total setup will be **$18 per month** (or $22 if you don't get a discount) and I have access to a TON of models. I then still have some credits in Nous Portal and OpenRouter on the off chance I need something very niche.

The hype posts make it sound perfect. "INSANE (FREE!)." "makes $2,000/mo cloud stacks obsolete." "The gap is just 30 minutes of setup."

I've been running this stack for a few weeks now. It IS genuinely good. But nobody talks about the tradeoffs honestly. So here's the full picture.

**The setup (it's real and it's actually $0):**

Cloud route (fastest to try, still $0):

bash

ollama launch openclaw --model deepseek-v4-flash:cloud

One command. installs ollama, pulls the model, configures openclaw, launches the gateway. connect telegram. your agent is live.

Fully local route (after you have the hardware):

bash

ollama pull deepseek-r1:14b
# then configure openclaw to use the local ollama provider

Point openclaw at it with `api: "ollama"` And everything runs on your machine. data never leaves your network. no API keys. no subscriptions. genuinely $0 forever.

For the V4 Flash cloud route through Ollama, the model runs on Ollama's US-hosted servers. still free. still no API key needed. but your prompts do leave your machine, they just go to Ollama's infrastructure instead of DeepSeek's directly.

**What you gain (the real stuff):**

Privacy. Your data stays on your machine (fully local route) or at minimum stays within US infrastructure (ollama cloud route). nothing goes to Anthropic, OpenAI, or DeepSeek's Beijing servers.

Zero ongoing cost. No per-token billing. no subscription. No surprise $350 bills from a runaway cron job. The worst case is your electricity bill goes up.

No provider dependency. Anthropic can't ban your subscription. OpenAI can't change their pricing. DeepSeek can't rate limit you. Your agent runs whether the internet is on or off (fully local only).

DeepSeek V4 is genuinely capable. 1M token context window. mixture-of-experts architecture (1.6 trillion total parameters, 49 billion active per token for V4 Pro). strong at coding, reasoning, and agentic tasks. This isn't a toy model.

**Now here's what you actually lose:**

**Speed.** This is the big one nobody mentions in the hype posts. Local inference on consumer hardware is noticeably slower than cloud APIs. On a 16GB GPU running deepseek-r1:14b, expect maybe 15-25 tokens per second. Claude Sonnet on API gives you 120 tokens per second. You feel the difference on every single interaction. CPU-only setups are borderline unusable for agent work.

**Raw capability ceiling.** DeepSeek V4 Flash is excellent. but it's not Opus 4.7 or GPT-5.5 on the absolute hardest tasks. complex multi-step reasoning, nuanced creative work, flawless error recovery during tool chains. The gap is real on the top 10% of difficulty. for the other 90%? genuinely comparable.

**Hardware barrier.** The hype posts forget to mention you need actual hardware.

8GB VRAM: qwen 7b or deepseek-r1:1.5b. functional but limited.

16GB VRAM: deepseek-r1:14b. good enough for most agent tasks. the sweet spot for most people.

24GB+ VRAM: deepseek-r1:32b or V4 Flash quantized. best local experience. Requires a serious GPU or mac with unified memory.

V4 Pro locally? forget it unless you have a mac studio with 128GB+ unified memory. not happening on consumer hardware.

if you don't have 16GB+ VRAM, the fully local path is frustrating. use the ollama cloud route instead (still free, just not fully local).

**Reliability.** cloud APIs have teams monitoring uptime, handling failures, scaling capacity. your local setup has you. if ollama crashes at 3am, your morning briefing doesn't arrive. if your GPU overheats, your agent dies. if a power outage hits, everything stops. you are the sysadmin, the devops team, and the on-call engineer. all at once.

**Tool calling consistency.** local models are flakier on tool calls than cloud models. they'll occasionally skip a step in a multi-tool chain, hallucinate a tool result, or say "done!" when nothing happened. the smaller the model, the worse this gets. deepseek-r1:14b handles simple tool chains fine. complex 5+ step workflows get shaky.

**Setup and maintenance.** "30 minutes of setup" is optimistic. if everything works first try, maybe. but model downloads take time (14b is \~9GB, 32b is \~20GB). quantization issues happen. ollama config quirks appear. context limits in practice don't always match specs. updates aren't automatic. you're on bleeding edge with occasional bugs.

**The honest assessment:**

This stack is legitimately transformative for three types of people:

privacy-focused users who won't send data to cloud providers under any circumstances. The fully local route is the real deal. nothing leaves your machine.

tinkerers who enjoy the process of optimizing and maintaining their own setup. if debugging ollama configs at midnight sounds fun to you, this is your stack.

budget-constrained users who have the hardware but not the monthly budget. if you have a decent GPU sitting idle, this is free compute you're already paying for.

for everyone else? honestly, a hybrid setup makes more sense. run deepseek locally for routine daily tasks (briefings, simple research, drafts). fall back to a cloud API for the 10% of tasks that need frontier reasoning. your local setup handles the volume. the cloud handles the hard stuff.

**The one thing the hype posts get right:**

The gap between local and cloud is closing fast. A year ago, running an AI agent locally was a joke. Today, DeepSeek V4 Flash through ollama genuinely rivals cloud offerings for most daily agent use cases. a year from now, the gap might not matter for anyone.

But today, in May 2026, "fully local $0 agent" comes with real tradeoffs. knowing them upfront is the difference between a setup that lasts and one you abandon after a frustrating weekend.

If you're going to try it, start with the Ollama cloud route:

bash

# zero download, zero config, free
ollama launch openclaw --model deepseek-v4-flash:cloud

See if agent workflows are useful to you at all before investing in local hardware and fully-offline setup.

And if you don't want to manage any of this, there are managed platforms with free tiers that handle the infrastructure.

My customers have confidential data, they won't even use AWS.

I've been trying to solve this problem for them and they are more than fine with buying an on-premise device for Local LLMs + AI Agents.

Up until today, I have been extremely dissapointed with every model not named Opus.

However, Deepseek 4 Flash is doing near-Opus level performance. This is something I can actually use.

Upon this whole process things I dont understand:

>How are Qwen 35b people are using it? Not even sonnet can do the job.

>Do Mac users just say they are using local LLMs but not actually? That stuff is unbelievably slow. Heck, even with NVIDIA GPUs, it can be a bit frustrating when doing 1M tokens.

Anyway, thanks China for the free LLM. Not sure what they get out of it, I'm running it locally.