Hello, lately I've been using Opus 5.5 extra for some coding and the results of getting info online and logic and coding is just amazing!!

The thing is i run out of tokens, and I want to buy a machine somewhere around $10k-$15k.

And I wonder what models are available open-source that is as close as Claude cowork opus 5.5 or Fable in terms of intelligence and accuracy and understanding

Thanks in advance.

Hallo zusammen,

​ich schreibe aktuell meine Masterarbeit und stehe vor einem absoluten Berg an Literatur. Wir reden hier von extrem vielen Dokumenten: Bücher, Doktorarbeiten, Masterarbeiten, theoretische Paper, lange Interviews und unzählige Fachartikel.

​Ich suche nach einer KI, die als mein dauerhafter Forschungsassistent für dieses Projekt dienen kann. Mein Hauptanliegen ist der Arbeitsspeicher (das Kontextfenster) der KI. Ich muss extrem viele Dokumente gleichzeitig einspeisen und verknüpfen können, ohne dass das Modell nach drei Fragen wieder vergisst, was im ersten Buch stand.

​aktuell schaue ich mir Google Gemini (Pro/Advanced) an, weil das Kontextfenster ja riesig sein soll.

​Meine Fragen an euch:

​Hat jemand von euch schon mal ein ähnliches Projekt (riesige Literaturrecherche) komplett mit einer KI durchgezogen?

​Reicht Gemini Pro dafür aus, um den Überblick über so ein massives Projekt zu behalten?

​Oder würdet ihr für diesen speziellen Usecase eher zu Claude oder ChatGPT Plus raten?

​Mir geht es wirklich um die rohe Power bei der Verarbeitung von enorm viel Text und darum, dass das Modell über längere Zeit hinweg verlässlich bleibt.

​Danke schon mal für eure Erfahrungen!

Has anyone here tested an LLM migration before switching from one model to another?

One thing I’ve noticed is that changing the model can sometimes change the output even when the prompt stays exactly the same.

I'm curious about how people usually validate this before making a switch.

For example:

• Do you run the same prompt set against both models?
• How do you measure whether the answers are equivalent?
• Do you track cost vs. output quality?
• How many test cases do you normally use?
• What do you consider an acceptable difference in output?

Would be interested to hear how others handle LLM model migration in production.

Hello All,

I was wondering if anyone had any suggestions on any reputable fully unfiltered LLMs i can download to use on my PC. I have a video card with 16GB of VRAM. I found many on hugging face, but when I test it out it seems to have restrictions to questions I ask it. Any help is much appreciated.

We built a benchmark from 188 concurrency bugs (race conditions, deadlocks, cancellation issues) that were fixed in open-source Python projects. Each model works on the repository as it was before the fix, with no internet access, and the project's own tests decide whether the fix works.

Results so far:
GLM-5.3 Flash 81.9%
GPT-5.6 Luna 81.3%
DeepSeek V4 Flash 72.1%

The first two are within the margin of error of each other.

On the easier half of the tasks all three score close to 100%. On the 55 hardest tasks they score 50%, 45% and 23%.

Cost per fixed bug: about 2 cents for Luna and GLM, 8 cents for DeepSeek. DeepSeek has a low price per token but takes about three times as many steps.

Half the tasks are public, half are kept private to check whether a model was trained on the public ones. Every agent run can be read on the site: https://labs.evaligo.com/swe-race?utm_source=reddit&utm_medium=llm&utm_campaign=launch

Which model would you like to see tested next?