Coding / Qwen

Qwen2.5 Coder · 7B

Code generation, repair, and reasoning in a compact open-weight coding model.

Verified sourceSource checked 9/23/2026
LOCALRENTED GPUOPEN WEIGHTS
Freshness 5 out of 100. Repository age: 782 days old, Sep 17, 2024. Measures age, not quality.
Popularity 82 out of 100. 1,302,537 downloads in 30 days; 865 lifetime likes. Checked Nov 7, 2026. Measures adoption, not quality.
What do these scores mean?

Two ways to compare models. Neither score measures answer quality, benchmark performance, or whether a model fits your machine.

Freshness · release age

A new release starts at 100. The score halves every 180 days: 100 × 2^(−age in days / 180). When the release date is unavailable, a repository date is a fallback—not proof of when the model was released. Missing dates stay unscored.

Check the date source
Popularity · adoption

Downloads over the past 30 days carry 80% of the score; lifetime likes carry 20%. Both use a logarithmic scale, capped at 10 million downloads and 100,000 likes. If likes are unavailable, downloads carry all the weight. Missing download statistics stay unscored.

round(80 × min(1, log10(1 + downloads) / 7) + 20 × min(1, log10(1 + likes) / 5))

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Scores as of Nov 8, 2026. Statistics checked Nov 7, 2026. Statistics are marked for refresh after 72 hours.Full scoring method
WORKFLOWS FOR THIS MODEL 1

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At a glance

Parameters
7.61B
License
Apache 2.0
Disk space
4.7 GB
Software
Ollama, llama.cpp
View the model source
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Ways to run it

Ollama · Windows, macOS, Linux

Ollama lists a 4.7 GB package; runtime and context need extra memory.

ollama run qwen2.5-coder:7b
Official instructions

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Mistral Small 4 119B A6B is an Apache-2.0 licensed hybrid model from mistralai that combines general instruction, reasoning, and Devstral-style capabilities in one model. It uses a Mixture-of-Experts architecture with 119B total parameters, 6.5B activated per token, 128 experts with 4 active, and a 256k context length. It supports multimodal input (text and image) with text output, multilingual support across dozens of languages, system prompts, function calling, JSON output, and configurable reasoning effort. The model card also mentions optional efficiency resources: an eagle head for speculative decoding and an NVFP4 checkpoint. It is tagged for vLLM and offered in safetensors format.

Freshness 33 out of 100. Repository age: 289 days old, Jan 23, 2026. Measures age, not quality.
Popularity 62 out of 100. 29,619 downloads in 30 days; 472 lifetime likes. Checked Nov 7, 2026. Measures adoption, not quality.
MODEL SIZE119B total · 6.5B active
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Qwen3-Coder-Next · 80B A3B

Qwen3-Coder-Next is an open-weight text-generation model from Qwen/Qwen3-Coder-Next, licensed under apache-2.0. It is designed specifically for coding agents and local development. The model has 80B total parameters with 3B activated parameters, a 262,144-token native context length, and a hybrid architecture combining Gated DeltaNet, Gated Attention, and Mixture-of-Experts layers. It supports only non-thinking mode and does not generate thinking blocks.

Freshness 34 out of 100. Repository age: 282 days old, Jan 30, 2026. Measures age, not quality.
Popularity 77 out of 100. 428,592 downloads in 30 days; 1,735 lifetime likes. Checked Nov 7, 2026. Measures adoption, not quality.
MODEL SIZE80B total / 3B active
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A 12B-total, 2.5B-active open-weight thinking model for coding and tool use. Start with JetBrains’ official GGUF on a suitable desktop, or serve the BF16 checkpoint with vLLM on a larger GPU.

Freshness 83 out of 100. Release age: 49 days old, Sep 20, 2026. Measures age, not quality.
Popularity 55 out of 100. 11,306 downloads in 30 days; 193 lifetime likes. Checked Nov 7, 2026. Measures adoption, not quality.
MODEL SIZE12B total · 2.5B active