Mistral Nemo 12B Instruct vs Qwen 3 8B

The two models at a glance

About Mistral Nemo 12B Instruct

Mistral AI and NVIDIA's co-developed 12B instruct model with 128k context, the Tekken tokenizer, and strong European multilingual coverage. Strengths: 128k context window, Strong European multilingual performance, Apache 2.0 license, Efficient Tekken tokenizer reduces token counts.

About Qwen 3 8B

Alibaba's 8B dense model with a toggleable thinking mode and broad multilingual coverage. Punches well above its weight for an 8B and runs comfortably on a single consumer GPU. Strengths: Hybrid thinking/fast modes switchable per request, Strong multilingual performance across 119 languages, Up to 131K context via YaRN (32K native), Apache 2.0 — clean commercial use.

How they compare

Mistral Nemo 12B Instruct comes from Mistral AI and Qwen 3 8B from Alibaba, they belong to the Mistral and Qwen families respectively. This comparison is built entirely from structured specs — parameter count, VRAM by quantization, context window, license, and published benchmark scores — so the verdict below reflects measurable differences rather than marketing claims.

At 12B vs 8B parameters, Mistral Nemo 12B Instruct is the larger of the two. At Q4, Qwen 3 8B fits in about 5 GB of VRAM versus 7 GB for the other — a 2 GB difference that matters on consumer GPUs.

The two models target different sweet spots: Mistral Nemo 12B Instruct is tuned for chat, general, multilingual, fr, while Qwen 3 8B leans toward chat, general, reasoning, multilingual. Match the model to your dominant workload rather than to raw size.

On a typical mid-range GPU, Qwen 3 8B pushes roughly 35 tokens/sec versus 25, so it is the more responsive choice for interactive or high-volume use. For long-context work, Qwen 3 8B offers the bigger window (128k vs 125k tokens).

Memory, quantization & throughput

Across quantization levels, Mistral Nemo 12B Instruct requires Q4 ≈ 7 GB, Q5 ≈ 9 GB, Q8 ≈ 13 GB, FP16 ≈ 24 GB, while Qwen 3 8B requires Q4 ≈ 5 GB, Q5 ≈ 6 GB, Q8 ≈ 9 GB, FP16 ≈ 16 GB. In practice Mistral Nemo 12B Instruct fits an 8 GB card at Q4, so plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity of Q8 or FP16.

Without a GPU, Mistral Nemo 12B Instruct needs roughly 16 GB of system RAM to run on CPU and Qwen 3 8B about 10 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 25 tokens/sec from Mistral Nemo 12B Instruct and 35 from Qwen 3 8B, scaling up to 70 and 90 tokens/sec on high-end hardware.

Which fits your GPU

Here is the highest-quality quantization of each model that fits common GPU memory budgets, so you can match Mistral Nemo 12B Instruct or Qwen 3 8B to the card you actually own:

• On a 8 GB GPU: Mistral Nemo 12B Instruct runs at Q4 (7 GB); Qwen 3 8B runs at Q5 (6 GB).
• On a 12 GB GPU: Mistral Nemo 12B Instruct runs at Q5 (9 GB); Qwen 3 8B runs at Q8 (9 GB).
• On a 16 GB GPU: Mistral Nemo 12B Instruct runs at Q8 (13 GB); Qwen 3 8B runs at FP16 (16 GB).
• On a 24 GB GPU: Mistral Nemo 12B Instruct runs at FP16 (24 GB); Qwen 3 8B runs at FP16 (16 GB).

Benchmark scores

Reported benchmarks for Mistral Nemo 12B Instruct: MMLU 68, HellaSwag 83.5, Winogrande 76.8.

Reported benchmarks for Qwen 3 8B: MMLU-Pro 68.7, GPQA 60, LiveCodeBench 54.4.

Bottom line: which should you pick?

• Pick Qwen 3 8B for long-context work (up to 128k tokens).
• Pick Qwen 3 8B for lower VRAM and faster inference; pick Mistral Nemo 12B Instruct for maximum headline quality.
• Pick Mistral Nemo 12B Instruct if your workload is fr.
• Pick Qwen 3 8B if your workload is reasoning.

Which GPU should you buy to run Mistral Nemo 12B Instruct?

To run Mistral Nemo 12B Instruct locally at Q4, you need ~7 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).

Frequently asked questions

What is the difference between Mistral Nemo 12B Instruct and Qwen 3 8B?

The headline differences: Mistral Nemo 12B Instruct is a 12B model and Qwen 3 8B is 8B; their context windows differ (125k vs 128k tokens). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.

Can Mistral Nemo 12B Instruct and Qwen 3 8B run on a 24 GB GPU?

At a Q4 quantization, Mistral Nemo 12B Instruct needs about 7 GB of VRAM and fits comfortably on a 24 GB GPU; Qwen 3 8B needs about 5 GB and fits comfortably on a 24 GB GPU. Qwen 3 8B is the lighter option for tight VRAM budgets.

Which is faster, Mistral Nemo 12B Instruct or Qwen 3 8B?

Qwen 3 8B is the smaller model (8B vs 12B), so on the same hardware it runs faster and uses less memory. The larger model trades speed for headline quality.

What licenses do Mistral Nemo 12B Instruct and Qwen 3 8B use?

Mistral Nemo 12B Instruct is licensed under Apache 2.0 and Qwen 3 8B under Apache 2.0.

Which has the longer context window, Mistral Nemo 12B Instruct or Qwen 3 8B?

Qwen 3 8B has the larger context window (128k vs 125k tokens), so it handles longer documents and codebases in a single prompt.