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Cogito v2 Preview Llama 70B

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Cogito v2 Preview Llama 70B is an open-weights language model from Deep Cogito, released July 2025. Its published size is 70.55B parameters and a 131k-token context window. Licence: llama3.1.

Catalogue identity notes

deepcogito/cogito-v2-preview-llama-70B @1e1d12e (created 2025-07-27, licence llama3.1). Deep Cogito v2 Preview 70B (Jul 2025) hybrid reasoning model. Independent rows: EuroEval English (both datasets).

Model information sources

Release date ↗ · Weights licence ↗

HF safetensors metadata 70,553,706,496 parameters (deepcogito/cogito-v2-preview-llama-70B @1e1d12e).

61.3±7.2#204 of 949 LLMsfrom 2 benchmark results across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of Cogito v2 Preview Llama 70B, out of 1132 with a score, then more from Deep Cogito.

#modelscorewhereRAMreleased
264QwQ-Plus Alibaba61±2api $1.20/M2025-04
265Grok 4 Fast SpaceXAI (xAI)61±2comparison only2025-09
266Cogito v2 Preview Llama 70B Deep Cogito61±7local~43GB2025-07
267GLM-4.5V Z.ai61±2api $0.90/M +local~69GB2025-08
268Llama 3.1 Nemotron Ultra 253B v1 NVIDIA61±2local~82GB2025-04
more from Deep Cogito
255Cogito v1 Preview Qwen 32B Deep Cogito62±7local~21GB2025-03
272Cogito v1 Preview Llama 70B Deep Cogito61±7local~43GB2025-03
285Cogito v1 Preview Qwen 14B Deep Cogito60±7local~10GB2025-03

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests2 not in a task · 77% of its score
testresultvs the others in its rundifficultycountswhy
EuroEval HellaSwag (English), test split, 5-shot generative MC, accuracy91.9% ✎▃·▁▁▂▁▂▅█▇▄▁ beats 97/100medium · avg 68%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 51%noisy
EuroEval Life in the UK (English knowledge MC), test split, 5-shot, accuracy92.5% ✎▁▁▁▁▁▁▁▂▄█▄▃ beats 127/146easy · avg 83%▇▇▇▇▇▇▇▇▇▇▇ 26%noisy

$ where to run it

llama.cpp · recommended

version

  1. Install llama.cpp · Homebrew and winget installs name the commands llama-cli and llama-server instead.
    macOS / Linux
    curl -LsSf https://llama.app/install.sh | sh
    Windows
    irm https://llama.app/install.ps1 | iex

    other ways to install ↗

  2. Run Cogito v2 Preview Llama 70B · needs ~42.5 GB · from huggingface.co/lmstudio-community
    chat in the terminal
    llama cli -hf lmstudio-community/cogito-v2-preview-llama-70B-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf lmstudio-community/cogito-v2-preview-llama-70B-GGUF:Q4_K_M

LM Studio

version

  1. Install LM Studio · Open the app once before using the lms command.

    download it (macOS / Windows / Linux) ↗

  2. Run Cogito v2 Preview Llama 70B · needs ~42.5 GB · from huggingface.co/lmstudio-community

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/lmstudio-community/cogito-v2-preview-llama-70B-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run Cogito v2 Preview Llama 70B · needs ~42.5 GB · from huggingface.co/lmstudio-community
    chat in the terminal
    ollama run hf.co/lmstudio-community/cogito-v2-preview-llama-70B-GGUF:Q4_K_M

MLX (Mac)

version

  1. Install MLX (Mac) · Apple silicon Macs only, text only.
    with uv
    uv tool install mlx-lm
    or
    pip install mlx-lm
  2. Run Cogito v2 Preview Llama 70B · needs ~39.7 GB, Mac only · from huggingface.co/mlx-community
    chat in the terminal
    mlx_lm.chat --model mlx-community/cogito-v2-preview-llama-70B-4Bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mlx-community/cogito-v2-preview-llama-70B-4Bit

Other ways to run it

  1. Run it as published · needs ~141.1 GB, NVIDIA only · from huggingface.co/deepcogito (official)
    vllm serve deepcogito/cogito-v2-preview-llama-70B --tensor-parallel-size 4

Official weights and documentation: huggingface.co/deepcogito ↗

$ how the score was worked out · EuroEval leaderboards (result… 77%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
EuroEval leaderboards (results collection), English generative leaderboard2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 77%MIT
prior · pull toward the middle▇▇▇▇▇▇▇ 23%
weighted average= 61.3

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