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Gemma 1.1 7B IT

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Gemma 1.1 7B IT is an open-weights language model from Google, released April 2024. Its published size is 8.54B parameters and a 8k-token context window. Licence: gemma.

Catalogue identity notes

Arena entry 'gemma-1.1-7b-it' (text). LMArena's leaderboard table (arena-leaderboard space @6f2eeeb) labels 'gemma-1.1-7b-it' as 'Gemma-1.1-7B-it' (Google, Gemma license), linking https://huggingface.co/google/gemma-1.1-7b-it. Maker repo google/gemma-1.1-7b-it @065a528: licence tag gemma. 8,537,680,896 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-04: Google Gemma releases page (ai.google.dev/gemma/docs/releases): 'April 5, 2024 Release of Gemma 1.1'.

36.9±0.7#578 of 719 LLMsfrom 12 benchmark results across 8 sources

$ how it compares

The LLMs ranked either side of Gemma 1.1 7B IT, out of 828 with a score, then more from Google.

#modelscorewhereRAMreleased
669Qwen2.5 Coder 0.5B Instruct Alibaba37±1local~1.8GB2024-11
670Granite 20B Code Base (8K) IBM37±1local~15GB2024-04
671Gemma 1.1 7B IT Google37±1local~7.1GB2024-04
672Code Llama 7B Python Meta37±1local~5.8GB2023-08
673Dolphin 2.2.1 Mistral 7B Cognitive Computations37±2local~6.1GB2023-10
more from Google
9Gemini 4 Argon (limited preview) Google82±1limited preview2026-09
23Gemini 3.8 Flash Google77±1api $1.50/M2026-09
32Gemini 3.7 Flash Google76±1api $1.50/M2026-08

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆██▇▆▄▃▁ #402 of 458 · 69% of its score · 3 tests

Beats DeepSeek R1 Distill Qwen 7B, Qwen3.5 2B, DeepSeek R1 Distill Llama 8B here, though they rank higher overall. Trails Qwen2.5 1.5B Instruct, WizardLM 13B V1.2, Code Llama 7B Instruct here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
HumanEval42.7% ✎▁·▁▁▃▄▆▅█▃▁▁ beats 26/99easy · avg 79%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 38%sharp · at its level
HumanEval+35.4% ✎▁·▁▁▁▃▄▄▄█▃▁ beats 24/99easy · avg 74%▇▇▇▇▇▇▇▇▇▇ 26%sharp · at its level
MBPP+45.0% ✎▁···▁▁▂▅▇█▆▂ beats 10/75easy · avg 68%▇▇ 5.0%at its level
chat & writing▁▁▁▃▅▄▅█▆▆▇▃ #324 of 393 · 12% of its score · 3 tests

Beats OpenThinker3 7B, Gemma 4 E2B, Code Llama 70B Instruct here, though they rank higher overall. Trails LFM2 350M, Gemma 3 1B, Flan-T5 XXL here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Arena-Hard (v0.1, GPT-4-1106 judge)12.1% ✎▂▁▁▅█▅▄▃▃▅▃▁ beats 6/60medium · avg 52%▇▇ 4.1%sharp
Polyrating rating (bias-aware re-fit of Chatbot Arena votes, mid-2024)1097.0 ✎▂▁▂▁▃▇█▆▇▄▂▂ beats 43/95avg 1201▇▇ 4.0%blunt
LMArena (text)1182.5 ✎▁▁▁▂▃▃▃▆▆▇█▃ beats 57/379avg 1268▇▇ 3.9%blunt
other tests6 not in a task · 19% of its score
testresultvs the others in its rundifficultycountswhy
LiveBench 2024-07-2618.8% ✎▁▁▁▂▂▃▂█▆▃▅▃ beats 14/61medium · avg 41%▇▇▇▇ 11%typical
MedQA (USMLE, 4 options)45.7% ✎▁▁··▄▁▁··▄▃█ beats 9/17easy · avg 76%▇▇ 4.5%sharp · at its level
MedMCQA44.4% ✎▂▂··▄▄▂▂·▄██ beats 8/17medium · avg 68%▇ 2.4%at its level
Open PT-LLM Leaderboard average (9 Portuguese tasks)53.9% ✎▃▁▁▁▁▁▁▃▂█▅▁ beats 73/276medium · avg 73%▇ 0.8%at its level · noisy
Vectara hallucination leaderboard, 2025 dataset (HHEM-2.3, factual consistency rate)83.0% ✎▁▁▃▃▄▄█▆▃▂▂▁ beats 8/140very easy · avg 94%▇ 0.4%blunt · noisy
PubMedQA70.8% ✎▂·▂▆·▄▂▄▄▂█▂ beats 10/17medium · avg 77%▇ 0.3%blunt · at its level

$ where to run it

llama.cpp · recommended

  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 Gemma 1.1 7B IT · needs ~5.3 GB · from huggingface.co/ggml-org
    chat in the terminal
    llama cli -hf ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf ggml-org/gemma-1.1-7b-it-Q4_K_M-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 Gemma 1.1 7B IT · needs ~5.3 GB · from huggingface.co/ggml-org

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

version

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run Gemma 1.1 7B IT · needs ~5.62 GB · from ollama.com
    chat in the terminal
    ollama run gemma:7b-instruct-v1.1-q4_K_M

MLX (Mac)

  1. Install MLX (Mac) · Apple silicon Macs only, text only.
    with uv
    uv tool install mlx-lm
    or
    pip install mlx-lm
  2. Run Gemma 1.1 7B IT · needs ~5.9 GB, Mac only · from huggingface.co/mlx-community
    chat in the terminal
    mlx_lm.chat --model mlx-community/gemma-1.1-7b-it-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mlx-community/gemma-1.1-7b-it-4bit

Official weights and documentation: huggingface.co/google ↗

$ how the score was worked out · EvalPlus / EvalPerf published… 69%, LiveBench model judgments (HF… 11%, Open Medical-LLM Leaderboard… 7% and 5 more

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
EvalPlus / EvalPerf published evaluation runs3▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 69%Apache-2.0
LiveBench model judgments (HF livebench/model_judgment, 2024-10-22 revision) with the LiveBench question datasets1▇▇▇ 11%Apache-2.0 (stated in the LiveBench datasheet)
Open Medical-LLM Leaderboard results (HF dataset openlifescienceai/results)3▇▇ 7%Apache-2.0
Arena-Hard-Auto v0.1 leaderboard (repository README, pre-v2)1▇ 4%Apache-2.0
Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation (arXiv:2409.00696v3)1▇ 4%CC-BY-4.0
LMArena leaderboard dataset1▇ 4%CC BY 4.0
Open Portuguese LLM Leaderboard (archived)1▇ 1%Apache-2.0
Vectara hallucination leaderboard (vectara/hallucination-leaderboard repository)1▇ 0%Apache-2.0
prior · pull toward the middle▇ 0%
weighted average= 36.9

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json