Best Local Coding Models in 2026: Qwen3-Coder vs DeepSeek vs Codestral

Quick Facts

• Sweet-spot pick (May 2026): Qwen3-Coder 30B at Q4_K_M on a 24 GB GPU.
• Laptop / 8–10 GB GPU pick: Qwen3-Coder 7B at Q4_K_M (~5 GB).
• Long-context pick: DeepSeek Coder V3 at Q4_K_M on 48 GB+ of VRAM.
• Speed pick: Codestral 22B at Q4_K_M — fastest inference among the 22B+ tier.
• Enterprise / audit-friendly pick: Granite Code 34B (IBM, Apache 2.0).
• Niche-language pick: StarCoder 2 15B (Rust, Lua, Haskell, Solidity coverage).
• VRAM math at Q4_K_M: roughly (parameters in B) × 0.6 GB plus 2–4 GB context overhead.
• Licences are not equal. Qwen3-Coder, DeepSeek Coder V3, and Granite Code are Apache 2.0. Codestral has the Mistral Non-Production licence with separate commercial terms. Kimi K2.7 Code ships under a Modified MIT licence that requires attribution for large-scale deployments. StarCoder 2 ships under BigCode OpenRAIL-M.

How the Six Local Coding Models Compare in 2026

Which One Should You Pick

Qwen3-Coder: The Default Local Coding Pick

• Sizes: 30B (the headline model) and 7B (the laptop and 8 GB GPU model). Both are dense transformers (not mixture-of-experts).
• Context window: 256K tokens for the 30B; 128K for the 7B. Among the largest in this set without going to MoE-derived models.
• Training emphasis: code-heavy multilingual training corpus with strong coverage of Python, TypeScript/JavaScript, Java, C++, Go, and Rust. Tool-calling examples are part of the post-training mix.
• Licence: Apache 2.0 — commercial use allowed without separate licensing, attribution preserved.
• VRAM at Q4_K_M: the 30B fits in roughly 17–18 GB, leaving headroom on a 24 GB card for context and tooling. The 7B fits in roughly 5 GB.
• Tool-calling reliability: strongest of the open-weight coding models for harnesses with strict tool schemas (Cline, Continue.dev Agent mode). The XML-format reliability that Cline depends on is high on the 30B; less reliable on the 7B.
• Where it shines: general-purpose coding (Python, TypeScript), large-context tasks (whole-file refactors), tool-using agent loops.
• Where it falls short: the 7B variant is good for its size but cannot match the 30B on multi-step reasoning. Niche languages (Lua, Haskell, Solidity) get less coverage than StarCoder 2.

DeepSeek Coder V3: The Long-Context Heavyweight

• Architecture: MoE — far higher total parameters than active parameters at inference. The full model is heavier on disk and VRAM than its active count would suggest.
• Context window: 128K tokens with extension techniques pushing usable context further depending on quantization and inference engine.
• Training emphasis: code and reasoning. Strong on Python, TypeScript, C++, and Go. Multi-step planning and chain-of-thought-style reasoning are notable strengths.
• Licence: Apache 2.0 — commercial use allowed.
• VRAM at Q4_K_M: the full V3 needs 48 GB+ for comfortable inference. Distilled and smaller MoE-derived variants targeting 24 GB cards exist; verify the variant on the model card before downloading.
• Tool-calling reliability: strong on harnesses that allow OpenAI-style tool calls; slightly weaker than Qwen3-Coder on Cline's strict XML schema in May 2026.
• Where it shines: whole-repo reasoning, long-context tasks, multi-step agentic plans.
• Where it falls short: the hardware bar is the highest of any model in this guide. On a 24 GB card, the smaller derivative variants are the only viable option, and they trail the full model on long-context tasks.

Codestral 22B: The Speed Pick With a Clear Commercial Path

• Sizes: 22B (the headline). Fits comfortably at Q4_K_M on a 16 GB GPU with context headroom.
• Context window: 32K tokens. Smaller than Qwen3-Coder and DeepSeek; sufficient for single-file and most multi-file edits but limiting on whole-repo work.
• Training emphasis: broad multi-language code coverage with strong Python, TypeScript, Java, C++, and Bash performance.
• Licence: Mistral Non-Production licence by default; commercial use requires Mistral Commercial licensing (paid). This is unusual in this set and is the most important fact for teams shipping a commercial product on top of the model.
• VRAM at Q4_K_M: ~13 GB — fits on a 16 GB GPU with room for context, comfortable on 24 GB.
• Inference speed: faster than Qwen3-Coder 30B at the same quantization, and faster than DeepSeek Coder V3 by a wide margin. The quality-vs-speed trade-off is the cleanest of the six.
• Where it shines: real-time autocomplete on a 16–24 GB card, EU-team workflows where the Mistral commercial relationship matters, fast iteration in chat.
• Where it falls short: the 32K context is the smallest in this set after StarCoder 2. The licence story is more involved than Apache 2.0 — read the Mistral Non-Production terms before integration.

Kimi K2.7 Code: The Server-Class Agentic Pick

• Sizes: one size — 1T total parameters with ~32B active per token (mixture-of-experts). There is no small variant, so there is no laptop path.
• Context window: 256K tokens, carried over from Kimi K2.6.
• Training emphasis: coding-specialised and agentic. Moonshot reports roughly 30% fewer reasoning tokens than K2.6 and stronger long-horizon performance — the difference shows on multi-step tasks, not on single-function completions.
• Licence: Modified MIT — commercial use permitted, with an attribution requirement for large-scale deployments. Read the modification before shipping it in a product.
• Memory footprint: the smallest published quantisation of the identical-architecture K2.6 is ~340 GB, needing roughly 350 GB of combined RAM and VRAM for usable speeds. INT4 is the practical local format. This is a multi-GPU server or a very large unified-memory machine, not a 24 GB card.
• Tool-calling reliability: its design point is agentic tool use over long horizons, which is where it separates from the dense coding models here. If your workload is inline completion rather than multi-step agent loops, the extra hardware buys you little.
• Where it shines: teams that already own inference hardware and run long agentic coding jobs — repo-wide refactors, multi-file migrations, sustained tool loops.
• Where it falls short: the hardware bar rules it out for almost every individual developer. If you are working on one machine, Qwen3-Coder 30B is the better default and the gap on everyday coding is far smaller than the 10x hardware cost implies.

Granite Code: The Enterprise / Audit-Friendly Pick

• Sizes: 34B (the headline) and 8B (laptop / 8 GB GPU). Dense transformers, not MoE.
• Context window: 128K tokens.
• Training emphasis: code-heavy multi-language with deliberate documentation of training corpora — a positioning choice that matters more for procurement than for code quality.
• Licence: Apache 2.0 — the same posture as Qwen3-Coder and DeepSeek Coder V3.
• VRAM at Q4_K_M: ~20 GB for the 34B, ~5 GB for the 8B.
• Coding direction: trails Qwen3-Coder 30B on most public benchmarks; competitive with Codestral on Python and Java, behind on TypeScript.
• Where it shines: procurement-driven choices, enterprise audit contexts, regulated-industry deployments where the model card's data-provenance documentation is itself a deliverable.
• Where it falls short: absolute coding capability is below the leaders. If procurement is not a binding constraint, Qwen3-Coder is the stronger choice on the same hardware budget.

StarCoder 2: The Niche-Language Pick

• Sizes: 15B (the practical local pick), 7B, 3B. Dense transformers.
• Context window: 16K tokens — the smallest in this set, and the main constraint.
• Training emphasis: breadth over depth — trained on hundreds of programming languages with deliberate coverage of less-common languages. Python and TypeScript performance trails the leaders, but Rust, Lua, Haskell, and Solidity coverage is best-in-class for open-weight models.
• Licence: BigCode OpenRAIL-M — a responsible-AI licence with use-case restrictions. Read the licence before commercial integration; it is more permissive than Mistral Non-Production but more restrictive than Apache 2.0.
• VRAM at Q4_K_M: ~9 GB for the 15B — fits comfortably on a 12 GB or 16 GB card.
• Where it shines: niche-language work (Rust, Lua, Haskell, Solidity, Elm, Julia), polyglot codebases, language coverage that other models do not provide.
• Where it falls short: the 16K context window is the smallest in this set; absolute Python and TypeScript performance is below Qwen3-Coder and DeepSeek.

VRAM Math by Quantization Level

• Q4_K_M (the default): strongest balance of size and quality for most coding work. Roughly 0.6 GB per billion parameters. A 30B model fits in ~18 GB; a 7B fits in ~5 GB.
• Q5_K_M: roughly 0.75 GB per billion parameters. A 30B model needs ~22 GB. Quality recovery is small but measurable on multi-step reasoning.
• Q6_K: roughly 0.85 GB per billion parameters. A 30B model needs ~26 GB. Worth the headroom on a 32 GB card.
• Q8_0: roughly 1.05 GB per billion parameters. A 30B model needs ~32 GB. Closest to FP16 quality at half the VRAM.
• FP16 (no quantization): roughly 2.0 GB per billion parameters. A 30B model needs ~60 GB. Used only for fine-tuning or research; never for local inference.
• Context VRAM cost: scales with sequence length. As a rule of thumb, expect ~1 GB per 32K tokens of active context on coding models — meaningful for DeepSeek Coder V3 and Qwen3-Coder long-context use.
• Tooling overhead: Ollama, LM Studio, and llama.cpp each add ~500 MB to ~1 GB on top of the model and context. Allow 2–4 GB total headroom for active tooling.

Context Window Comparison

Licensing Comparison

Decision Tree: Which Should You Pick

• 1. How much VRAM do you have? Under 12 GB: Qwen3-Coder 7B. 12–16 GB: Qwen3-Coder 7B or Codestral 22B. 24 GB: Qwen3-Coder 30B. 48 GB+: DeepSeek Coder V3 (full).
• 2. Are you shipping inside a commercial product? Yes: prefer Apache 2.0 (Qwen3-Coder, DeepSeek Coder V3, Granite Code). Avoid Codestral unless you are paying for Mistral Commercial licensing.
• 3. Do you need a context window above 32K? Yes: skip Codestral and StarCoder 2. Pick Qwen3-Coder, DeepSeek, or Granite Code (Kimi K2.7 Code too, if you have the hardware).
• 4. Are you writing niche languages (Rust, Lua, Haskell, Solidity)? Yes: StarCoder 2 15B, despite the 16K context limit.
• 5. Are you in a regulated industry where licence and training-data provenance need procurement defence? Yes: Granite Code 34B is the easiest case to make.
• 6. Still unsure? Default to Qwen3-Coder — 30B if you have a 24 GB GPU, 7B otherwise. Re-evaluate when you outgrow it.

Common Mistakes Picking a Local Coding Model

• Mistake 1: picking the model with the highest leaderboard score regardless of hardware. A model that does not fit at Q4_K_M with 2–4 GB of headroom will spill to disk and become unusable for interactive coding. VRAM is the binding constraint for most readers.
• Mistake 2: trusting the claimed context window as the practical working window. Coding models lose attention quality past roughly half the claimed context. Plan for the practical window, not the headline number.
• Mistake 3: skipping the licence read. Codestral on a commercial product without a Mistral Commercial licence is a procurement failure. Kimi K2.7 Code's Modified MIT terms require attribution once you deploy at scale. Read the licence before integration.
• Mistake 4: ignoring tool-call reliability when picking for an agent harness. Cline's strict XML schemas, Continue.dev's Agent mode, and any MCP-based loop all rely on the model emitting tool calls cleanly. Coding-tuned 30B+ models reliable; 7B-class models often fail.
• Mistake 5: not pairing a small autocomplete model with the larger chat model. A 30B chat model is overkill for sub-200 ms autocomplete. Run a 1.5B–7B autocomplete model alongside the chat model — total VRAM stays manageable, latency stays interactive.
• Mistake 6: not re-checking the model card every six months. Open-weight model lines update; quantization recipes improve; licences occasionally tighten. The default pick today is not necessarily the default in November 2026.

Sources

• Qwen3-Coder Model Card on Hugging Face — Architecture, parameter count, context window, licence, and vendor-reported benchmark direction for Qwen3-Coder 30B.
• DeepSeek Coder V3 Model Card — MoE architecture details, context window, licence, and benchmark direction for DeepSeek Coder V3.
• Codestral Model Card — Architecture, context window, and licence terms for Codestral 22B.
• Mistral Commercial Licensing — Terms required for commercial use of Codestral and other Mistral non-production-licensed models.
• Kimi K2.7 Code on Hugging Face — Moonshot AI's model cards, quantisations, and the Modified MIT licence text.
• Granite Code Model Cards (IBM) — Sizes, context windows, training-data documentation, and Apache 2.0 licence for Granite Code.
• StarCoder 2 Model Cards (BigCode) — Sizes, context windows, language coverage, and BigCode OpenRAIL-M licence.
• Ollama Model Library — Quantization variants, file sizes, and pull commands for each model above.
• BigCode OpenRAIL-M Licence Text — The full licence text and use-case restrictions for StarCoder-line models.

Frequently Asked Questions

Related Reading

• Replace GitHub Copilot With a Local LLM — practical application context: cost math, hardware sizing, and which model fits which workflow.
• Continue.dev vs Cline vs Aider: Best Local Coding Agent in 2026 — pairs with model choice; the harness layer beneath the model.
• Local Coding LLM Without Internet — niche use case context for fully offline workflows.
• LLM Quantization Explained — the deeper technical layer behind the VRAM math used throughout this guide.
• Best Local LLMs in 2026 — general-purpose model authority for non-coding workloads.
• Power Local LLM Hub — full guide library.
• Qwen Local Deployment: Complete Production Guide 2026 -- complete production setup for Qwen coding models — Docker, API server, multi-GPU