The April 2025 refresh of Qwen at 8B. Native mixed-mode reasoning: the model can 'think' before answering when triggered, or answer directly for simple queries — configurable per request. Apache 2.0. A strong upgrade over Qwen 2.5 7B on math and code, with much better instruction following.
- Parameters
- 8B
- Context length
- 33K
- Modality
- text
- Released
- 2025-04-29
- Tokenizer
- Qwen 3
Memory & hardware
- VRAM (fp16)
- 16 GB
- VRAM (Q4)
- 4.8 GB
- Recommended
- RTX 3090 or Apple M-series
- Quantizations
- fp16, q8_0, q5_k_m, q4_k_m, gguf
Benchmarks
Hosted inference pricing
USD per million tokens.
| Provider | Input | Output | |
|---|---|---|---|
| openrouterCheapest | $0.117 | $0.455 | Visit ↗ |
Run it yourself
Drop-in commands for the three most common open-source inference paths. The Ollama tag is a best-effort match against the registry; verify the size variant before pulling.
vllm serve Qwen/Qwen3-8B
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")
model = AutoModelForCausalLM.from_pretrained(
"Qwen/Qwen3-8B", device_map="auto", torch_dtype="auto"
)Qwen/Qwen3-8B Related models
Same family or similar size — useful when shopping around.
Apache-2.0-licensed 7B model with surprisingly strong reasoning and multilingual chops. Qwen 2.5 trains on a larger and more carefully filtered corpus than the original Qwen series, and the 7B variant punches well above its weight on coding and math benchmarks. A strong default for cost-sensitive chat workloads and for fine-tuning experiments where the Apache licence simplifies downstream redistribution.
- Context
- 128K
- License
- apache-2-0
- VRAM Q4
- 4.2 GB
The workhorse 8B instruction-tuned model. Excellent quality-to-cost ratio and the broadest ecosystem support of any open-weights model — every major inference engine, fine-tuning library, and quantization toolchain has a 3.1 8B preset. Fits in 24 GB of VRAM at fp16, ~6 GB at Q4. Strong default for production chat where 70B is overkill, for fine-tuning on a specialist task, and for any workload where you want a known-good baseline.
- Context
- 128K
- License
- llama-3
- VRAM Q4
- 4.8 GB
NousResearch's community-driven fine-tune on the Llama 3.1 8B base. Tuned for strong tool use, function calling and steerable persona behaviour. Inherits Llama 3's community licence and its 128K context.
- Context
- 128K
- License
- llama-3
- VRAM Q4
- 4.8 GB
Mid-tier Gemma. Strong general-purpose chat model at small scale. The Gemma Terms of Use permit commercial use subject to Google's prohibited-use policy.
- Context
- 8K
- License
- gemma
- VRAM Q4
- 5.4 GB
The original 7B RLHF chat model. Historically important — the first widely-adopted commercially-usable open-weights chat model. Still cited as a baseline in most 2024–25 papers.
- Context
- 4K
- License
- llama-2
- VRAM Q4
- 4.2 GB
TII's latest dense 7B from December 2024. Strong scores on commonsense reasoning benchmarks. TII's Falcon licence permits royalty-free commercial use with attribution.
- Context
- 33K
- License
- falcon-2
- VRAM Q4
- 4.2 GB