Qwen 3 8BvsMistral 7B Instruct
Complete comparison between Qwen 3 8B (8B parameters, Alibaba) and Mistral 7B Instruct (7B, Mistral AI). VRAM required by quantization, tokens/second measured on 4 reference GPUs, verdict by use case, license, installation commands. All figures are calculated from catalog data—nothing is copied and pasted between pages.
In brief
| Feature | Qwen 3 8B | Mistral 7B Instruct |
|---|---|---|
| Settings | 8B | 7B |
| Family | Qwen | Mistral |
| Author | Alibaba | Mistral AI |
| Origin | CN | FR |
| License | Apache 2.0 | Apache 2.0 |
| Context | 131,072 tokens | 32 768 tokens |
| Output | April 2025 | September 2023 |
Memory footprint
Approximate VRAM required for inference with an average context window. The winner (in green) is the model that uses less — smaller models have the edge.
| Quantization | Qwen 3 8B | Mistral 7B Instruct |
|---|---|---|
| Q4_K_M (lightweight) | 5 GB | 5 GB |
| Q5_K_M (balanced) | 6 GB | 6 GB |
| Q8 (near-lossless) | 9 GB | 9 GB |
| FP16 (max quality) | 16 GB | 16 GB |
| CPU-only RAM | 10 GB | 8 GB |
Estimated speed (tokens/second)
Estimates based on the best quantization that can fit on each GPU. Actual figures depend on the prompt, context, and engine (llama.cpp, vLLM, MLX). Methodology.
| Reference GPU | Qwen 3 8B | Mistral 7B Instruct |
|---|---|---|
| RTX 4090 (24 GB) | 90 tok/s · FP16 | 90 tok/s · FP16 |
| RTX 4080 (16 GB) | 35 tok/s · FP16 | 35 tok/s · FP16 |
| RTX 3060 12GB (12 GB) | 12 tok/s · Q8 | 12 tok/s · Q8 |
| Apple M4 Pro (48 GB) (36 GB) | 35 tok/s · FP16 | 35 tok/s · FP16 |
Verdict by use case
For each common use case, we indicate which of the two is better suited based on its tags, size, and specialization.
Your private, free ChatGPT on your machine in 1 hour — LM Studio, Ollama, Open WebUI, your documents, no cloud.
- Lifetime online access
- PDF + files
- 30-day money-back guarantee
Strengths and weaknesses
Qwen 3 8B
Hybrid thinking/fast mode. 119 languages, 32k native (131k via YaRN).
- Toggleable thinking mode
- 119 languages
- 131k ctx (with YaRN)
- Apache 2.0
- Verbose thinking mode
- Less mature than Qwen 2.5 in terms of ecosystem
Installation
Mistral 7B Instruct
The classic French model. Fast, versatile, and an excellent base to get started.
- Excellent quality-to-speed ratio
- Good in French
- Fully permissive Apache license
- Mature ecosystem (fine-tunes, quantization)
- Worse than 2024+ models at reasoning
- 32k context only
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Qwen 3 8B or Mistral 7B Instruct?
On an RTX 4090, Qwen 3 8B runs at FP16 (~90 tok/s), Mistral 7B Instruct in FP16 (~90 tok/s). In pure throughput, Mistral 7B Instruct wins. See the configurator to test your exact GPU.
Qwen 3 8B or Mistral 7B Instruct: which is better in French?
For formal French (writing, summarization, translation), Mistral 7B Instruct has the edge—trained on a dense French-language corpus. The other remains usable, but for less demanding use cases.
Which uses the least VRAM?
Both require the same VRAM in Q4 (5 GB). The choice then comes down to the license or perceived quality.
Can these models be used in commercial production?
Qwen 3 8B is under Apache 2.0 — free for commercial use. Mistral 7B Instruct is under Apache 2.0 — also free to use. For SaaS, prefer Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: Mistral 7B Instruct (7B). More capable: Qwen 3 8B (8B). If you're unsure, run the configurator with your GPU and use case — it will decide based on both.
Alternatives to consider
If neither works for you, here are some neighboring models you could explore.