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DeepSeek-Coder 7B Instruct v1.5

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DeepSeek-Coder 7B Instruct v1.5 is an open-weights language model from DeepSeek, released January 2024. Its published size is 6.91B parameters. Licence: deepseek.

Catalogue identity notes

Comparison-only. Maker repo deepseek-ai/deepseek-coder-7b-instruct-v1.5 @2a050a4: licence tag other (deepseek). 6,910,365,696 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-01: month the maker's Hugging Face repo was created (createdAt 2024-01-25). DeepSeek-Coder v1.5 instruct.

47.6±0.7#386 of 719 LLMsfrom 9 benchmark results across 3 sources

$ how it compares

The LLMs ranked either side of DeepSeek-Coder 7B Instruct v1.5, out of 828 with a score, then more from DeepSeek.

#modelscorewhereRAMreleased
461Hermes 2 Theta Llama-3 70B Nous Research48±2local~47GB2024-06
462Granite 4.0 H Tiny (7B-A1B) IBM48±1local~5.6GB2025-09
463DeepSeek-Coder 7B Instruct v1.5 DeepSeek48±1local~5.9GB2024-01
464Qwen2 57B-A14B Instruct Alibaba48±2local~35GB2024-06
465Qwen2.5 Coder 3B Instruct Alibaba48±1local~3.3GB2024-11
more from DeepSeek
21DeepSeek V4.1 Flash DeepSeek78±1api $0.26/M +local~537GB2026-09
31DeepSeek V4 Pro (0813) DeepSeek76±1api $1.63/M +local~1010GB2026-08
35DeepSeek V3.2 Speciale DeepSeek76±3comparison only

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆██▇▆▄▃▁ #291 of 458 · 86% of its score · 7 tests

Beats Qwen3-VL 32B Instruct, Solar Open 100B (102B-A12B), GLM-4.7-Flash here, though they rank higher overall. Trails DeepSeek LLM 67B Chat, WizardLM 70B V1.0, Yi 1.5 9B Chat here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
HumanEval +175.6% ✎▁·▁▁▃▄▆▅█▃▁▁ beats 74/99easy · avg 79%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 51%sharp · at its level · several runs
HumanEval+71.3% ✎▁·▁▁▁▃▄▄▄█▃▁ beats 75/99easy · avg 74%▇▇▇▇▇▇▇▇▇▇▇ 27%sharp · at its level
MBPP+62.2% ✎▁···▁▁▂▅▇█▆▂ beats 42/75easy · avg 68%▇▇ 5.0%at its level
CodeReviewQA: change localisation (hard), invariant accuracy4.6% ✎█▂▁▁▃▂▂▃▅▅▅▇ beats 14/47medium · avg 36%▇ 1.3%sharp · at its level · noisy
CodeReviewQA: solution identification (hard), invariant accuracy11.4% ✎▃··▁▂▃▃▂█▃▄▂ beats 9/47medium · avg 54%▇ 1.2%sharp · at its level · noisy
CodeReviewQA: automated code refinement, exact match rate36.3% ✎▁▁▁▁▁▁▂▂▃█▅▄ beats 35/47hard · avg 33%▇ 0.9%at its level
CodeReviewQA: change type recognition, invariant accuracy58.1% ✎▁▁▁······▁▃█ beats 9/47easy · avg 67%▇ 0.0%blunt · at its level · noisy
other tests1 not in a task · 14% of its score
testresultvs the others in its rundifficultycountswhy
EvoEval (overall)48.8% ✎▂▂▃▄▄█▁▄▅▃▁▃ beats 34/42medium · avg 55%▇▇▇▇▇ 14%at its level

$ 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 DeepSeek-Coder 7B Instruct v1.5 · needs ~4.2 GB · from huggingface.co/Edmon02
    chat in the terminal
    llama cli -hf Edmon02/deepseek-coder-7b-instruct-v1.5-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf Edmon02/deepseek-coder-7b-instruct-v1.5-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 DeepSeek-Coder 7B Instruct v1.5 · needs ~4.2 GB · from huggingface.co/Edmon02

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/Edmon02/deepseek-coder-7b-instruct-v1.5-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 DeepSeek-Coder 7B Instruct v1.5 · needs ~4.2 GB · from huggingface.co/Edmon02
    chat in the terminal
    ollama run hf.co/Edmon02/deepseek-coder-7b-instruct-v1.5-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~13.82 GB · from huggingface.co/deepseek-ai
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='deepseek-ai/deepseek-coder-7b-instruct-v1.5', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Weights and documentation: huggingface.co/deepseek-ai ↗

$ how the score was worked out · EvalPlus / EvalPerf published… 64%, Top Leaderboard Ranking = Top… 32%, CodeReviewQA: The Code Review… 3%

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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 64%Apache-2.0
Top Leaderboard Ranking = Top Coding Proficiency, Always? EvoEval: Evolving Coding Benchmarks via LLM — authors' own runs2▇▇▇▇▇▇▇▇▇▇ 32%CC BY 4.0
CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models (arXiv:2503.16167v2)4▇ 3%CC-BY-4.0
prior · pull toward the middle▇ 0%
weighted average= 47.6

$ 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