Qwen3.6 27B
Q4 GGUF download: unsloth/Qwen3.6-27B-GGUF - 16.82 GB on disk, read 2026-10-10. VRAM figures above are estimates; this is the actual file size.
Intelligence benchmarks
Artificial Analysis indexes - compared with the best open and proprietary models
Intelligence
21.4
AA Index
Coding
53.7
AA Index
Agentic
18.5
AA Index
Intelligence Index - Qwen3.6 27B vs. the field
Best open-weight models (you can run locally) and leading proprietary models for context.
Coding Index comparison
Agentic Index comparison
Benchmark data from Artificial Analysis · updated 2026-10-19.
Hosted API cost & speed
What it costs to rent this model instead of running it locally
| Metric | Value |
|---|---|
| Input (per 1M tokens) | $0.60 |
| Output (per 1M tokens) | $3.60 |
| Median speed (hosted API) | 58 tok/s |
Median across hosted providers, measured by Artificial Analysis - updated 2026-10-19. Local llama.cpp speed depends on your hardware — see the hardware pages for estimates.
Standard benchmarks
Performance across standard evaluations
| Benchmark | Score |
|---|---|
| MMLU | 86.1 |
| HumanEval | 82.6 |
| MT-Bench | 8.7 |
| GSM8K | 93 |
| GPQA Diamond | 84.2 |
Source: Artificial Analysis component evals - updated 2026-10-19. Other scores are carried over from the site's initial dataset - no source on record, not re-verified.
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
ollama run qwen3.6:27bvllm serve Qwen/Qwen3.6-27BNew to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
Qwen3.6 27B
Qwen3.6 27B is Alibaba's latest flagship dense model with native vision-language capabilities, 262K context, and state-of-the-art agentic coding performance. It achieves 75% on SWE-bench Verified - the best of any model under 70B.
What makes Qwen3.6 special
- Agentic coding - 75.0% SWE-bench Verified, 51.2% SWE-bench Pro
- Vision-language - native image and video understanding
- 262K native context - extensible to 1M+ tokens via YaRN
- Thinking mode - step-by-step reasoning with MTP (Multi-Token Prediction)
- Multi-Token Prediction - up to 4 tokens predicted simultaneously
- 5.15M monthly downloads - one of the most popular models on HF
Benchmarks
| Benchmark | Qwen3.6 27B | Qwen3 32B |
|---|---|---|
| MMLU-Pro | 86.1 | 83.4 |
| AIME 2026 | 94.1 | - |
| SWE-bench Verified | 75.0 | - |
| GPQA Diamond | 85.5 | - |
| LiveCodeBench v6 | 80.7 | - |
| MMMU (Vision) | 82.3 | - |
VRAM math
| Quant | VRAM | Recommended Hardware |
|---|---|---|
| Q4_K_M | ~16 GB | RTX 4090 |
| Q5_K_M | ~20 GB | RTX 4090 |
| Q8_0 | ~31 GB | RTX 5090 |
| FP16 | ~56 GB | Dual RTX 5090 |
How to run
ollama run qwen3.6:27bFrom the community
What users are saying
“Qwen 3.6 122B, if released, would literally be superior to anything else in it's class. Qwen team, hopefully you decide to release it. The community thanks you.”
“FP8 version is 55% the size of BF16 so you would still need 2 RTX 4090s to run that which is not much cheaper to rent than 5090s”
“Hello, Qwen3.6 27B is very strong. However, it thinks too long in many cases even with a simple task. Hope that you guys will release Qwen3.7 27B and make it thinks shorter and more efficient. Thank so much.”
Frequently asked
Quick answers to common questions
How much VRAM does Qwen3.6 27B need?
Qwen3.6 27B with 28B parameters needs approximately 16 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is Qwen3.6 27B better than other Qwen models?
Qwen3.6 27B scores 86.1 on MMLU and 82.6 on HumanEval. It has 28B parameters with 262,144 context - a strong choice for coding, vision, agents.
What license is Qwen3.6 27B under?
Qwen3.6 27B is released under the Apache 2.0 license, making it suitable for most commercial and personal projects.
What hardware runs Qwen3.6 27B well?
With 28B parameters, Qwen3.6 27B requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for Qwen3.6 27B?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~20 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.
How long can Qwen3.6 27B's context window handle?
Qwen3.6 27B supports a 262,144-token context window - enough for very long documents, codebases, or multi-turn conversations. Real-world usable context may vary by implementation.
What models compete with Qwen3.6 27B?
Qwen3.6 27B competes with other models in its class. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Compare & pair with
Similar models and recommended hardware
Nearby options
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