Ling 3.1 Flash 1019
Q4 GGUF download: bartowski/Ling-3.1-flash-1019-GGUF - 39.7 GB on disk, read 2026-10-30. 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
29.5
AA Index
Coding
23.3
AA Index
Agentic
40.1
AA Index
Intelligence Index - Ling 3.1 Flash 1019 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 2026-10-31.
Hosted API cost & speed
What it costs to rent this model instead of running it locally
| Metric | Value |
|---|---|
| Input (per 1M tokens) | $0.30 |
| Output (per 1M tokens) | $0.90 |
Median across hosted providers, measured by Artificial Analysis - updated 2026-10-31. Local llama.cpp speed depends on your hardware — see the hardware pages for estimates.
Standard benchmarks
Performance across standard evaluations
| Benchmark | Score |
|---|---|
| HLE | 43.5 |
| SciCode | 56.8 |
Source: Artificial Analysis component evals - updated 2026-10-31.
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
vllm serve inclusionAI/Ling-3.1-flash-1019New to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
Ling 3.1 Flash 1019 is a 572.1B-parameter mit model from InclusionAI. It scores 29.5 on the Artificial Analysis Intelligence Index (coding 23.3). At Q4_K_M it needs roughly 332 GB of VRAM, placing it in the 48 GB+ / multi-GPU hardware tier.
Benchmarks
Artificial Analysis Intelligence Index - Ling 3.1 Flash 1019 vs. leading closed models:
| Model | Intelligence | Coding | GPQA |
|---|---|---|---|
| Ling 3.1 Flash 1019 | 29.5 | 23.3 | - |
| Gemini 4 Carbon | 59.4 | 50.4 | - |
| Claude Opus 5.5 | 58.4 | 51.7 | - |
| GPT-6 Astra | 56.9 | 49.1 | 96.1 |
| GPT-6.1 Sol | 54.5 | 46.8 | - |
| Claude Sonnet 5.5 | 54.2 | 50.5 | - |
Source: Artificial Analysis (2026-10-31).
Popularity
Ling 3.1 Flash 1019 has 1,090 downloads in the last month on HuggingFace and 82 likes.
Frequently asked
Quick answers to common questions
How much VRAM does Ling 3.1 Flash 1019 need?
Ling 3.1 Flash 1019 with 572.1B parameters needs approximately 332 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is Ling 3.1 Flash 1019 better than other inclusionAI models?
Ling 3.1 Flash 1019 has 572.1B parameters with 262,144 context - a strong choice for general use.
What license is Ling 3.1 Flash 1019 under?
Ling 3.1 Flash 1019 is released under the mit license, making it suitable for most commercial and personal projects.
What hardware runs Ling 3.1 Flash 1019 well?
With 572.1B parameters, Ling 3.1 Flash 1019 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 Ling 3.1 Flash 1019?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~406 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.
How long can Ling 3.1 Flash 1019's context window handle?
Ling 3.1 Flash 1019 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 Ling 3.1 Flash 1019?
Ling 3.1 Flash 1019 competes with other 286B–858B. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Nearby options
Similar models and compatible hardware by spec