Qwen3-Coder Ollama: Complete Tag List,
Model Sizes & Install Guide

If you've been searching for Qwen3-Coder Ollama tags, you're not alone. The query "qwen3-coder ollama tags 2026" has been one of the fastest-growing searches in the Ollama ecosystem this spring, and for good reason: Alibaba shipped Qwen3-Coder quietly but powerfully, and the Ollama library page doesn't exactly hold your hand. There are 10 available tags across three model sizes — 30B, 480B, and the newer Next variant — and picking the wrong one can leave you staring at an OOM error or wasting hours on a 290GB download you didn't need. This guide covers every available tag, exact pull commands, precise VRAM requirements per quantization, and a direct benchmark comparison against Qwen2.5-Coder so you know whether it's actually worth switching.

Does Qwen3-Coder actually exist on Ollama?

Yes, and it's been there since late 2025. The confusion comes from the naming gap: people search for qwen3-coder ollama and land on the Qwen2.5-Coder page, or find unofficial community uploads, and wonder if the real thing exists. It does. The official library entry is ollama.com/library/qwen3-coder and it has over 4.5 million pulls as of june 2026 — that's a very active model.

Qwen3-Coder is Alibaba's dedicated agentic coding model family, built on top of the Qwen3 architecture. The "3" in the name refers to the third generation of Qwen models, not the parameter count. The key architectural features that separate it from Qwen2.5-Coder are the Mixture-of-Experts (MoE) design — which activates only a fraction of parameters per token — a native 256K token context window, and training data that skews heavily toward multi-turn agentic workflows and tool-calling rather than pure code completion.

All Available Qwen3-Coder Ollama Tags (Full List, june 2026)

There are currently 10 official tags on the Qwen3-Coder library page. They fall into two size families — 30B and 480B — each available in multiple quantizations. Here's the complete picture:

Tag data sourced directly from ollama.com/library/qwen3-coder/tags, june 2, 2026. The 30B and latest tags share the same underlying file hash (06c1097efce0).

VRAM Requirements per Tag

The MoE architecture of Qwen3-Coder means the numbers here behave differently from dense models like Qwen2.5-Coder. All 30B parameters are loaded into memory (because MoE still loads all expert weights), but only 3B activate per token during inference. This gives you faster generation speeds than a typical 30B dense model, but the full model weight still needs to fit somewhere — VRAM or system RAM via CPU offloading.

Context window VRAM overhead assumes FP16 KV cache (default). Use OLLAMA_KV_CACHE_TYPE=q8_0 to roughly halve the context overhead — this is important if you want to use the full 256K window on consumer hardware.

Exact Ollama Pull Commands for Every Tag

Here are the copy-paste ready commands. The model name format is always qwen3-coder:<tag>.

For most users (RTX 4080/5080, 16–20GB VRAM)

Downloads 19GB. This is the :latest tag and the recommended starting point.

Higher accuracy (RTX 4090 or 32GB+ VRAM)

Downloads 32GB. ~2% quality improvement over Q4_K_M. Worth it if you have the VRAM headroom.

Full precision research (M4 Max 64GB / dual 4090)

Downloads 61GB. Maximum fidelity to the original model weights. For benchmarking or fine-tune prep.

Cloud-routed 480B (no GPU required)

Routes to Ollama's hosted 480B inference. Subject to rate limits and internet dependency.

Run after pulling

Check VRAM usage after loading

Qwen3-Coder-Next: The Hidden Gem

Separate from the main qwen3-coder library, there's also qwen3-coder-next on Ollama — a distinct model, not just a tag variant. Qwen3-Coder-Next is built on the Qwen3-Next-80B-A3B-Base architecture with hybrid attention and MoE. Despite having only 3 billion active parameters per token (out of 80B total), it was specifically agentic-trained at scale with reinforcement learning on verifiable coding tasks.

The headline stat: Qwen3-Coder-Next achieves over 70% on SWE-Bench Verified using the SWE-Agent scaffold, which is genuinely competitive with larger proprietary models. For context, a full 480B Qwen3-Coder reaches similar territory — the Next variant essentially delivers frontier agentic coding performance at a fraction of the active compute cost. On SWE-bench Pro (the harder benchmark using longer multi-turn agentic tasks), it outperforms or matches several models with 10–20× more active parameters.

Qwen3-Coder vs Qwen2.5-Coder: Which to Use in 2026?

The question I see most in the GSC queries: should I switch from Qwen2.5-Coder to Qwen3-Coder on Ollama? The honest answer depends on your VRAM and use case. Here's the direct comparison:

Best GGUF Coding Model 2026: Qwen vs DeepSeek vs CodeGemma 14B

When searching for the best gguf coding model 2026 qwen deepseek codegemma 14b, the landscape is competitive. Here is how Qwen3-Coder fits in:

• Qwen3-Coder (30B MoE): The top choice for context length (256K) and complex agentic tasks. Highly recommended for multi-file generation.
• DeepSeek-Coder V2: Excellent for logic and math, but often requires more VRAM for its larger mixture-of-experts setups unless heavily quantized.
• CodeGemma 14B: A highly efficient dense model that is great for standard code completion tasks, but falls short of Qwen's MoE architecture in complex refactoring.

Clarification: Ollama Qwen3.5 Models List 2026

A common search term we see is "ollama qwen3.5 models list 2026". Note that as of june 2026, Qwen3.5 does not exist yet. Users searching for this are likely confusing the older Qwen2.5 series with the latest Qwen3 models detailed on this page. If you want the absolute latest qwen coder model ollama 2026, stick to the Qwen3-Coder tags listed above.

Benchmarks: Qwen3-Coder vs the Field

Benchmark conditions vary across evaluations. HumanEval pass@1 is 0-shot. SWE-bench Verified uses the SWE-Agent scaffold unless otherwise noted. Community-reported scores may differ from official model card figures based on prompt formatting.

The key takeaway from these numbers: Qwen3-Coder's biggest lead over Qwen2.5-Coder isn't on HumanEval (where the 32B dense model still holds its own), but on the agentic SWE-bench benchmarks that measure real-world code repair across multi-file repositories. If your workflow involves answering "why is this GitHub issue failing?" rather than "complete this function," Qwen3-Coder's training focus pays off.

Setup Guide: Run Qwen3-Coder in 5 Minutes

Assuming you already have Ollama installed (version 0.5+ recommended). If not, step one covers that.

1. Install Ollama (skip if already installed):
   curl -fsSL https://ollama.com/install.sh | sh
2. Set the KV cache env var before anything else:
   export OLLAMA_KV_CACHE_TYPE=q8_0

   This halves context memory overhead — critical for 256K context on consumer hardware. Add to .bashrc or .zshrc to make it permanent.

3. Pull the 30B model (recommended for 16–20GB VRAM):
   ollama pull qwen3-coder:30b

   19GB download. Allow 10–30 minutes depending on your internet speed.

4. Run a quick test:
   ollama run qwen3-coder:30b "Write a Python async function that retries HTTP requests with exponential backoff"
5. Connect to VS Code via Continue.dev:

   Install Continue extension → Settings → Provider: Ollama → Model: qwen3-coder:30b → Set context length: 32768 (or higher if VRAM allows).

6. Verify memory usage:
   nvidia-smi # Linux # macOS: Activity Monitor → Memory → GPU Memory Used

Decision Tree: Which Qwen3-Coder Tag for Your Hardware?

• You have 8GB VRAM or less: Qwen3-Coder is not for you yet. Use qwen2.5-coder:7b — it's still excellent at 88.4% HumanEval on 4.6GB VRAM.
• You have 16GB VRAM (RTX 4080, RTX 5080): qwen3-coder:30b with CPU offload. Set OLLAMA_KV_CACHE_TYPE=q8_0 and keep context under 64K for smooth generation.
• You have 20–24GB VRAM (RTX 4090, A5000): qwen3-coder:30b fits fully in VRAM. You can push to ~128K context comfortably.
• You have 32GB+ VRAM (dual 3090 / A6000 / M4 Max 64GB): qwen3-coder:30b-a3b-q8_0 — the quality bump is worth it at this tier.
• You prioritize agentic multi-turn tasks over raw code completion: qwen3-coder-next — same VRAM footprint as 30B, better SWE-bench scores.
• You need 480B without buying GPUs: qwen3-coder:480b-cloud — cloud-routed, no download, rate-limited.
• You need maximum HumanEval scores at 20GB VRAM: qwen2.5-coder:32b (dense) is still competitive here — Qwen3-Coder's wins are on agentic/SWE benchmarks, not raw pass@1.

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Frequently Asked Questions