Mistral 7B v0.3
The original Mistral 7B refresh with 32K context and extended vocabulary. Permissive Apache 2.0 weights and the first widely-deployed sliding-window-attention model. Still useful in 2026 for very-low-cost inference and as a baseline for fine-tuning experiments.
- Parameters
- 7B
- Context length
- 33K
- Modality
- text
- Released
- 2024-05-22
- Tokenizer
- LlamaTokenizer
Memory & hardware
- VRAM (fp16)
- 14 GB
- VRAM (Q4)
- 4.2 GB
- Recommended
- RTX 3060 12GB (Q4)
- Quantizations
- fp16, q8_0, q5_k_m, q4_k_m
Benchmarks
Hosted inference pricing
No provider we track publishes a per-token price for this model today. What each one used to offer is listed below.
No longer listed
Providers that used to serve this model. We don't republish their old rates — the dates below are the provider's own.
- togetherNot in Together AI’s serverless catalogue when we checked on 20 September 2026.Source ↗
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.
ollama run mistral
vllm serve mistralai/Mistral-7B-v0.3
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.3")
model = AutoModelForCausalLM.from_pretrained(
"mistralai/Mistral-7B-v0.3", device_map="auto", torch_dtype="auto"
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