G9v3-39A5B
Intelligence benchmarks
Artificial Analysis indexes - compared with the best open and proprietary models
Intelligence
31.4
AA Index
Agentic
28.7
AA Index
Intelligence Index - G9v3-39A5B vs. the field
Best open-weight models (you can run locally) and leading proprietary models for context.
Agentic Index comparison
Benchmark data from Artificial Analysis · updated 2026-08-10.
Standard benchmarks
Performance across standard evaluations
| Benchmark | Score |
|---|---|
| GPQA | 75.6 |
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.
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Deep dive
Notes, sources, and the full write-up
G9v3-39A5B is a 39B-parameter apache-2.0 model from AI9Stars. It scores 30.9 on the Artificial Analysis Intelligence Index. At Q4_K_M it needs roughly 23 GB of VRAM, placing it in the 12–24 GB GPU hardware tier.
Benchmarks
Artificial Analysis Intelligence Index - G9v3-39A5B vs. leading closed models:
| Model | Intelligence | Coding | GPQA |
|---|---|---|---|
| G9v3-39A5B | 30.9 | - | 75.6 |
| Claude Opus 5 (max) | 60.7 | - | 93.2 |
| Claude Fable 5 (with fallback) | 59.9 | - | 92.6 |
| GPT-5.6 Sol (max) | 58.9 | - | 94.1 |
| GPT-5.6 Terra (max) | 55 | - | 92.5 |
| Muse Spark 1.2 (xhigh) | 54.1 | - | 90.4 |
Source: Artificial Analysis (2026-08-06).
Popularity
G9v3-39A5B has 37 downloads in the last month on HuggingFace and 43 likes.
Frequently asked
Quick answers to common questions
How much VRAM does G9v3-39A5B need?
G9v3-39A5B with 39B parameters needs approximately 23 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is G9v3-39A5B better than other ai9stars models?
G9v3-39A5B has 39B parameters with 131,072 context - a strong choice for general use.
What license is G9v3-39A5B under?
G9v3-39A5B is released under the apache-2.0 license, making it suitable for most commercial and personal projects.
What hardware runs G9v3-39A5B well?
With 39B parameters, G9v3-39A5B 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 G9v3-39A5B?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~28 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.
How long can G9v3-39A5B's context window handle?
G9v3-39A5B supports a 131,072-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 G9v3-39A5B?
G9v3-39A5B competes with other 20B–59B. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Nearby options
Similar models and compatible hardware by spec
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