Best Local Coding LLM in 2026: Qwen2.5-Coder vs DeepSeek-Coder-V2 vs Codestral
Three open-weight coding models are worth taking seriously for local inference in 2026: Qwen2.5-Coder, DeepSeek-Coder-V2-Lite, and Codestral. The question isn’t which one “wins” — it’s which one your GPU can actually run at a useful speed, and whether you’re optimizing for chat-style code generation or IDE autocomplete.
The answer splits cleanly by VRAM tier. At 8GB, one model dominates by benchmark. At 12–16GB, you’re choosing between a dense model and a Mixture-of-Experts approach with meaningfully different trade-offs. At 24GB, the right answer depends on whether you spend most of your day pressing Tab in VS Code or asking Claude-style chat questions to a coding assistant. Below is the breakdown — with verified benchmark numbers and real VRAM requirements.
The models at a glance
| Model | Params | VRAM (Q4_K_M) | HumanEval | Context | License |
|---|---|---|---|---|---|
| Qwen2.5-Coder 7B-Instruct | 7B | ~5 GB | 88.4% | 128K | Apache 2.0 |
| Qwen2.5-Coder 14B-Instruct | 14B | ~10 GB | between 7B and 32B | 128K | Apache 2.0 |
| DeepSeek-Coder-V2-Lite | 16B (2.4B active) | ~12–13 GB | 81.1% (Python) | 128K | DeepSeek custom |
| Codestral 25.01 | 22B | ~18 GB | 86.6% | 256K | Mistral custom |
| Qwen2.5-Coder 32B-Instruct | 32B | ~20 GB | 92.7% | 128K | Apache 2.0 |
A few notes on reading that table. VRAM figures are for Q4_K_M quantization with a small context window; add 1–2 GB for a 16K context budget. DeepSeek-Coder-V2-Lite is a Mixture-of-Experts model: 16B total parameters, but only 2.4B active per token — it’s more comparable in inference speed to a ~7B dense model, not a 16B one. And HumanEval measures “given this Python docstring, write the function” — important, but not the whole story if your main use case is autocomplete.
8GB VRAM: Qwen2.5-Coder 7B is the obvious call
If your GPU has 8GB of VRAM — RTX 3070, RTX 4060, RX 7600 — Qwen2.5-Coder 7B-Instruct is not a compromise. It’s a genuinely impressive model. The 7B-Instruct variant scores 88.4% on HumanEval pass@1 and 84.1% on the harder HumanEval+ benchmark, according to the Qwen2.5-Coder technical report. For a 7B model you run at home with no API fees, those are numbers that sit alongside top-tier closed models from two years ago.
At Q4_K_M quantization, the model file is around 4.7 GB and sits comfortably in 8GB VRAM with room for context. Speed on an RTX 4090 lands around 100–130 tokens per second in Ollama or llama.cpp, per the Home GPU LLM Leaderboard at awesomeagents.ai; on a 12GB RTX 3060, 7B Q4 benchmarks come in around 42 tok/s according to community inference speed tests, which is fast enough for interactive sessions.
The 128K context window means you can feed entire files — 2,000-line Python files included — without chunking. That matters for the “refactor this function” use case more than you’d expect.
The one gap: fill-in-the-middle autocomplete isn’t Qwen2.5-Coder 7B’s specialty. It supports FIM, but if your primary workflow is tab-completion in an editor, consider using the 1.5B model for the autocomplete slot and the 7B for chat, exactly the way the Continue.dev dual-model setup works.
# Install via Ollama (defaults to Q4_K_M)
ollama pull qwen2.5-coder:7bVerdict: 8GB VRAM has a clear answer. Everything else in this tier is a step down.
12–16GB VRAM: Dense vs MoE trade-off
This is where the choice gets interesting. Two models deserve consideration:
Qwen2.5-Coder 14B-Instruct (dense, 14B parameters)
The 14B-Instruct sits between the 7B and 32B on benchmarks — scaling consistently with size across the Qwen2.5-Coder family. At Q4_K_M, the Ollama model file is roughly 8.5–9.0 GB, requiring about 10–11 GB of VRAM including the KV cache at standard context lengths. An RTX 3080 (10GB) can run it in a pinch; an RTX 3080 Ti (12GB) or RTX 4070 12GB is the comfortable minimum.
The 14B generates tokens faster than the DeepSeek option below, because all 14B parameters are active and the memory bandwidth usage is predictable. Apache 2.0 license means no commercial restrictions.
DeepSeek-Coder-V2-Lite (MoE, 16B total / 2.4B active)
This model scores 81.1% on HumanEval (Python) and 68.8% on MBPP+, per the DeepSeek-Coder-V2 paper. The MoE architecture is the key: despite 16B total parameters, only 2.4B are active per token, which means inference cost is closer to a 2.4B dense model in compute — but quality is closer to a 16B model because the router has access to specialized experts.
The Q4_K_M GGUF is 10.36 GB according to the bartowski/DeepSeek-Coder-V2-Lite-Instruct-GGUF page on Hugging Face, requiring about 12–13 GB of VRAM to run fully on GPU. It fits on a 12GB card with slim margin; a 16GB card (RTX 4060 Ti 16GB, RTX 4070 16GB) runs it comfortably.
The 128K context window and explicit 338-programming-language support are genuine advantages. The DeepSeek custom license doesn’t restrict personal or small-business use, but large-scale commercial deployment may require review.
Which one to pick at 12–16GB VRAM:
| Priority | Recommendation |
|---|---|
| Best code generation quality | DeepSeek-Coder-V2-Lite |
| Fastest inference / lowest latency | Qwen2.5-Coder 14B |
| Simplest deployment and no license concerns | Qwen2.5-Coder 14B |
| Widest language coverage | DeepSeek-Coder-V2-Lite (338 languages) |
| Running on exactly 12GB VRAM | Qwen2.5-Coder 14B (more headroom) |
For most developers, Qwen2.5-Coder 14B is the safer default — simpler MoE-free inference, Apache 2.0, and faster generation. DeepSeek-Coder-V2-Lite is worth trying if you work in less common languages or find you need the quality edge on complex multi-file tasks.
# Qwen2.5-Coder 14B (defaults to Q4_K_M)
ollama pull qwen2.5-coder:14b
# DeepSeek-Coder-V2-Lite
ollama pull deepseek-coder-v2:16b-lite-instruct-q4_K_M
# Or check available tags: https://ollama.com/library/deepseek-coder-v224GB VRAM: Generation vs autocomplete split
At 24GB — RTX 3090, RTX 4090, or a used 3090 from eBay — you have two serious options with a clear use-case split.
Qwen2.5-Coder 32B-Instruct: the benchmark leader
The 32B-Instruct variant scores 92.7% on HumanEval and 87.2% on HumanEval+, making it the strongest open-weight coding model that fits on a single consumer 24GB card. At Q4_K_M, the model file is around 18–20 GB, which fits in a 24GB card with room for context — tight, but workable if you’re not running a 50K-token context window simultaneously.
This is the model you want when you’re asking it to architect a new service, review 500 lines of code, write a test suite from scratch, or debug a complex async issue. Chat-style code generation is where 92.7% HumanEval actually shows up.
Apache 2.0 license. No commercial restrictions.
ollama pull qwen2.5-coder:32bCodestral 25.01: the autocomplete champion
Codestral 25.01 scores 86.6% on HumanEval — good, but below Qwen2.5-Coder 32B. However, it reaches 95.3% average FIM pass@1 across Python, JavaScript, and Java in the January 2025 update, which is the highest fill-in-the-middle score of any model in 2025, including closed ones.
What does that mean practically? When your cursor is in the middle of a function and you press Tab, Codestral completes it correctly at a rate no other locally-runnable model matches. That’s the case for most developers during most of their working hours.
Codestral 25.01 also added 256K context (vs 32K in the original) and a 2× speed improvement over the original release. At Q4_K_M quantization, Codestral 22B requires approximately 17.7 GB of VRAM according to the willitrunai.com hardware guide — more comfortable headroom on 24GB than the 32B Qwen, and you can push Q5_K_M without spilling to system RAM.
The license situation for Codestral is worth noting: you can download weights via Mistral’s site for local use, but check current terms before production commercial deployment.
Which to actually run at 24GB
If you’re using a local coding assistant primarily as an IDE tab-completion model (the way GitHub Copilot works), Codestral 25.01 wins outright on the metric that matters — FIM accuracy.
If you’re using it primarily as a chat-based coding partner for architecture, debugging, and code review sessions, Qwen2.5-Coder 32B wins on generation quality.
The right answer if you have a 24GB card: run both with model switching in Continue.dev. Use Codestral 25.01 for autocomplete and Qwen2.5-Coder 32B for the chat sidebar. The Continue.dev dual-model setup guide covers exactly this configuration — one model for tab completions, one for chat, with Ollama serving both.
Why HumanEval doesn’t tell the whole story for IDE users
HumanEval asks a model to write a standalone Python function from a docstring. It’s a clean, reproducible benchmark — useful for comparison — but it doesn’t reflect what most developers actually do with a coding model.
Real IDE usage is dominated by:
- Tab completion (fill-in-middle, cursor in a function body)
- Line/block completion (multi-line autocomplete based on file context)
- Chat with codebase context (ask about files already open in editor)
HumanEval measures only the third category, and only for standalone Python functions. A model that tops HumanEval but has poor FIM performance — stale completions, repeated code, wrong variable names — is genuinely worse for daily developer use than the benchmarks imply.
Codestral’s FIM-first design philosophy is the reason it remains competitive at 24GB despite not having the highest HumanEval number. For the majority of developers who spend most of their AI coding time pressing Tab rather than chatting, FIM accuracy is the number that moves the needle.
Honest take
8GB VRAM: Run Qwen2.5-Coder 7B-Instruct. 88.4% HumanEval at 5GB VRAM is not a consolation prize — it’s a genuinely competitive score that would have been top-tier two years ago. The only question is whether to pair the 1.5B model for FIM autocomplete with the 7B for chat (the right call if you’re in Continue.dev) or just run the 7B for everything.
12–16GB VRAM: Default to Qwen2.5-Coder 14B for simplicity and speed. Try DeepSeek-Coder-V2-Lite if you want to squeeze more quality out of the tier — especially if you work in non-Python languages where the 338-language training shows up.
24GB VRAM: Codestral 25.01 for FIM/autocomplete, Qwen2.5-Coder 32B for chat. If you can only run one, pick based on how you actually use the tool: Tab-heavy workflow = Codestral. Chat-heavy = 32B Qwen.
The Apache 2.0 licensing on the entire Qwen2.5-Coder family is a real advantage if you’re building tooling on top of these models — DeepSeek and Codestral have commercial restrictions that add friction at scale.
Related articles
- Setting Up a Local AI Coding Stack with Continue.dev + Ollama — dual-model config (FIM + chat), full config.yaml
- Q4 vs Q5 vs Q6 vs Q8 Quantization: Real Quality Loss Numbers — how much performance you give up dropping to Q4
- Best Local AI Models by VRAM Tier — broader model comparison across LLM categories
- Best AI Coding Tool by Language (2026) — which IDE extension pairs best with each model tier, by language
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- Qwen2.5-Coder Technical Report — Qwen Team (arXiv:2409.12186v2) — HumanEval scores for 7B-Instruct (88.4%) and 32B-Instruct (92.7%)
- DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models — arXiv:2406.11931 — DeepSeek-Coder-V2-Lite HumanEval Python 81.1%, MBPP+ 68.8%
- bartowski/DeepSeek-Coder-V2-Lite-Instruct-GGUF — Hugging Face — Q4_K_M file size 10.36 GB, quantization VRAM guidance
- Codestral 25.01 Specifications — apxml.com — 256K context, 2× speed vs original Codestral
- Mistral AI Codestral 25.01 Benchmark Review — index.dev — 86.6% HumanEval, 95.3% FIM pass@1
- Qwen2.5-Coder 14B VRAM Requirements — willitrunai.com — Q4_K_M ~9 GB file, 12 GB VRAM target
- Qwen2.5 Speed Benchmark — Qwen ReadTheDocs — 7B BF16 inference speed on A100 (40 tok/s Transformers, 84 tok/s vLLM)
- qwen2.5-coder:14b — Ollama Library — model tag sizes and quantization options
- Codestral 22B VRAM Requirements — willitrunai.com — Q4_K_M requires ~17.7 GB VRAM
- Home GPU LLM Leaderboard — awesomeagents.ai — RTX 4090 tokens/sec for 8B class models
- Local LLM Speed: RTX 3060 Benchmark Results — Ajit Singh — RTX 3060 ~42 tok/s for 7B Q4
- Codestral — Mistral AI Official Announcement — model architecture, FIM capability, licensing
Last updated May 22, 2026. Benchmarks reflect model versions available as of that date. Newer quantized builds and inference optimizations may change the speed numbers.
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