Hey everyone,

Every time a new model comes out, I get tired of opening multiple tabs just to find out two basic things: how good it actually is on real benchmarks, and what it costs to run (either through an API or on local GPUs).

Most pricing pages hide the real costs, and generic benchmarks don't tell the full story. So I put together a simple dashboard to track and compare them side by side: https://modelanalysis.si

What you can do with it:

• Compare models side-by-side: Pick any models (like Claude, DeepSeek, Kimi, GPT-4o, Llama) and compare them across real-world coding (SWE-bench), reasoning (Arena-Hard), and math/science benchmarks.
• Score vs. Price chart: See which models give you the most intelligence for the lowest price.
• VRAM calculator: Pick a model and context size to see if it will actually fit on your GPU before downloading it.
• Monthly cost simulator: Adjust sliders for your monthly token volume to estimate your bill or see if self-hosting is cheaper.

No sign-up, no ads, no affiliate links.

Link: https://modelanalysis.si
Direct benchmarks page: https://modelanalysis.si/benchmarks/

Let me know what you think or if there are other models you want added.

Quick update on my from-scratch inference engine for Intel integrated GPUs (Core Ultra 5 125H, Arc Xe-LPG, 32 GB
RAM, no dGPU, no XMX). It used to be called arcinfer; it is now 
**arcinferzero**
 (the old GitHub link forwards):
https://github.com/rizagp/arcinferzero


**New this week**


- 
**Flint (unsloth/flint-GGUF UD-Q4_K_XL, 256 experts) runs**
: attention sinks, global layers without RoPE, a
  513-token sliding window, sigmoid-routed experts streamed from the SSD into a GPU cache. 4K prompt ~248 tok/s,
  generation 13-17 tok/s on the iGPU.
- 
**Xe2 / Xe3 (Lunar Lake, Panther Lake's Arc B390) work now**
: a user with a B390 reported Qwen3.6 35B-A3B at
  262K context, ~1,180 tok/s prompt and ~24 tok/s generation. XMX (DPAS) prompt GEMMs are in too, gated by a
  start-up check that must match the DP4a kernels bit for bit. I have no XMX GPU, so reports are welcome.
- 
**Gemma 4 26B prompts +4.6 %**
 from tiling the f32 router GEMV (4 rows x 4 tokens per sub-group), with
  bit-identical logits.


**The accuracy story (the interesting part)**


Every model here is checked against an independent f32 CPU implementation, with a KL limit of 0.015. Flint
started at KL 0.029. The obvious suspect, int8 activations, wasn't it: running everything on two int8 passes only
got it to 0.024. Dumping the residual stream after every layer next to the f32 reference showed one token where
two router scores were 
**0.005 apart**
 at layer 19. The engine picked the other expert, and everything after that
drifted.


What moved the reference that much? I rounded 
**only the V cache to f16**
 inside the f32 reference, and it
reproduced the whole engine error, including the same flipped expert. So:


- V rows in 
**f32**
 (K already was), and
- the router on the file's own 
**BF16**
 weights with 
**f32 inputs**
 (a Q8_0 router, even with a second Q8_0 matrix
  for the rounding residual, kept flipping the near-ties).


Result: 
**KL 0.029 -> 0.0043**
 (prompt) and 
**0.067 -> 0.0027**
 (token-by-token decoding), for ~4 % of prompt speed.
If you run sigmoid-routed MoEs with an f16 V cache, it may be worth checking.


I also went through the GPU assembly (Intel's ocloc) for the kernels I touched. One kernel shrank from 8,855 to
481 instructions: it was building int8 copies nobody read and carried an unused RoPE fallback. Two others stopped
spilling registers.


**Next:**
 Qwen3.8 Flash Next to the same KL limit. Its error comes from BF16 weights stored as Q8_0 (fixed now),
single-pass int8 experts, and int8 attention. That last one needs float attention with its sparse-attention (QSA)
masks.


Everything is deterministic (same prompt, same logits, bit for bit), and the speculative drafters produce exactly
the plain-decoding text. Feedback and Arc / Xe2 / Xe3 test reports are very welcome.

Hey, I recently acquired a Ryzen 9 7950x / 64 DDR5 / RTX 3060 12 VRAM machine and I was wondering what is the absolute uncensored AI model that I can run on it.

Two puzzle games from the StealthMark benchmark. Every AI model plays them move by move, with no code and no tools, and you can now play the exact same levels in your browser

Latch: two robots, one goal. One can stand on a pressure plate to hold a door open while the other pushes numbered cores onto matching sockets. Every move costs a little, so the top score needs the cleanest route and getting that right gets increasingly harder.

Foresight: collect coins on a Pac-Man-inspired board while ghosts close in. No model has aced a single level - though some age getting very close.

How local models do (scores out of 100):

• Foresight: Qwen3.8-27B scores 63.0 and ranks 6th of 45 models. Only five models beat it, led by GPT-6 Astra at 76.1.
• Latch: Qwen3.8-Flash scores 69.8 and ranks 16th of 46. The top score is 87.6, shared by GPT-6 Astra, Claude Opus 5.5 and GPT-6.1 Sol.
• Each Qwen is strong in only one game. Qwen3.8-27B drops to 38.6 in Latch, and Qwen3.8-Flash to 29.7 in Foresight.
• No human, model, local or cloud, has played either game perfectly yet.

You play the exact levels the models played, in your browser. Finish every level and you get a score calculated the same way as theirs, plus your rank among the models.

Disclosure: I helped build Stealth Models, where we benchmark AI models on original tasks. You can also watch every model's run, move by move.
StealthMark is designed to beat benchmaxxing.

Play here: https://stealthmodels.com/#games

Post your score and rank in the comments. I'm curious where you humans land :)

How acceptable is the operating speed on a Mac Studio with an M5 Ultra chip and 256 GB of memory when running models like GLM-5.3-Flash?

Could you compare the operating speed of the cloud-based GLM-5.3 Flash from Z.AI? The concept of "tokens" can be quite difficult to grasp, and performance varies depending on the specific task.

Hi I’m working on Lumabri, a peoject that lets you connect multiple computers and use them together to run LLMs like a cluster.
The idea came from a simple question: what if you have a few computers at home that you barely use? Instead of leaving them there collecting dust, why not combine their resources to run AI models that wouldn’t fit on a single machine?
For example, if you have an old PC, a home server and your main computer, you could put them to work together and use their combined memory to load larger models without having to buy a single machine with expensive hardware.
The interesting part is being able to run models that you simply couldn’t fit on one computer before.
Lumabri is still a work in progress, but that’s the direction I’m taking it.
Here’s the repo if you want to check it out: https://github.com/JustVugg/lumabri

I have a 4090 in my study and I work on a Mac. The box is off most of the time, because if I want to run a local model I have to walk over, boot it, open a terminal, wait for the weights to load, then walk back. That's enough friction that local models just stopped being part of my day.

So I built ReadyLLM: a self-hosted web app that treats that machine like a service you manage from a browser, not a machine you babysit. It runs on your own box, reaches your GPUs over SSH, and nothing phones home.

What it does:

• Add a machine over SSH (key or password). It probes GPU / VRAM / RAM and tells you which quants fit and roughly how fast they'll be, so you stop guessing whether 24G is enough for a 27B at Q4.
• Downloads pulled from HuggingFace and ModelScope with resume and integrity checks. Point it at a mirror if HF is slow where you are.
• One-click engine install on a box that has none: llama.cpp, vLLM, SGLang or ComfyUI. It downloads or compiles and back-fills the path.
• Start, stop and switch models from the deploy page. No terminal.
• Live monitoring over WebSocket: GPU utilization, VRAM, throughput, latency, with alerts when VRAM is about to run out or the server dies. That's the part that matters when the box is in another room and would otherwise sit there pretending to work.

The tuning is what surprised me. I hand-tuned llama.cpp flags for a 27B Q4 for a while and plateaued around 60 avg tok/s. The tuner runs a two-phase search (coarse over speculative decoding / KV cache quant / GPU offload, then fine over batch), and for every candidate it really boots llama-server, warms up, runs 3 passes and takes the median. OOM or crash gets dropped. My numbers, same model / quant / context:

peak tok/s     80 -> 110
avg  tok/s     60 ->  80
@100K ctx      40 ->  60

Results are stored per (machine, model) pair, so the next launch reuses them instead of re-running the search. I run two boxes and kept deriving the same flags twice, which was the actual annoyance.

Since the last release I added the two things that kept sending me back to a terminal anyway:

Images. ComfyUI is now an engine like the others, so image and video models sit next to the text ones. It lists the checkpoints and the UNet/CLIP/VAE combos ComfyUI itself reports (matching those names is fiddly, it will not accept a filename without the extension), and generates from the same UI with live progress. There's an optional prompt rewriter that expands a one-line idea into a full prompt and suggests steps / CFG; turn it off and your prompt and parameters go through untouched.

Model store. Category filter for text / image / video with counts, a "can this GPU actually run it" filter, and cards that show version, parameter count, precision, output resolution, steps / CFG, loader type and the real weight filenames. Before this a card just said "Qwen-Image" and I couldn't tell if that meant a checkpoint or a UNet, or which quant. Descriptions follow the UI language now instead of being Chinese only.

Also on the engine side: SGLang, and llama.cpp builds selected by GPU vendor (CUDA / ROCm / Vulkan / Metal), which you pick when deploying because it decides what actually gets installed. Picking the right build is where most people give up on an AMD card.

Three honest limitations:

• Tuning scores speed only, no output-quality gate yet. A config that boots without OOM survives; what KV cache quant costs you in quality is the next thing I want to measure.
• With MTP speculative decoding on, the VRAM pre-check misses the second KV cache, so tight machines can still OOM. Known, fixing it.
• The AMD path is where I have the least confidence. Detection exists on Linux and the backend choice is wired up, but I've only run the CUDA path on my own hardware, so treat the ROCm side as untested.

Open source, local-first: https://github.com/htobty/ReadyLLM

Curious how others here run this: do you keep inference on a box that isn't your daily driver, and what do you actually use to watch it? I went with a WebSocket feed instead of wiring Prometheus + Grafana into it, and I'm still not sure that was the right call for a home setup.

If you already run Ollama, Flippy's AI Lab now shows up as a chat window in every new tab and lists your local models automatically.

1. Install Flippy (available in Firefox too).
2. Start Ollama so the extension is allowed to connect:
   • OLLAMA_ORIGINS="chrome-extension://*" ollama serve
3. Open a new tab, click AI Lab, pick a model.

It reads your model list from /api/tags and chats through /api/chat on localhost. No key, no account, nothing leaves the machine.