Llama 3.1 8B vs Mistral 7B Instruct

The two models at a glance

About Llama 3.1 8B

Meta's Llama 3.1 8B, the open-weight benchmark of 2024. A 128k context, well-behaved instruction follower with the largest ecosystem in the open-source world. Strengths: 128k context window, Strong instruction following and coding, Enormous ecosystem of fine-tunes and integrations, Solid quality-to-size ratio.

About Mistral 7B Instruct

Mistral AI's breakout 7B instruct model. Still a go-to baseline for fast, low-cost inference and the most fine-tuned open-weight model in the wild. Strengths: Excellent quality-to-speed ratio for a 7B, Fully permissive Apache 2.0 license, Mature ecosystem of fine-tunes, GGUFs, and quants, Solid multilingual coverage, including strong French.

How they compare

Llama 3.1 8B comes from Meta and Mistral 7B Instruct from Mistral AI, they belong to the Llama and Mistral 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 8B vs 7B parameters, Llama 3.1 8B is the larger of the two. At Q4, Mistral 7B Instruct fits in about 5 GB of VRAM versus 6 GB for the other — a 1 GB difference that matters on consumer GPUs.

Where they overlap on benchmarks, Llama 3.1 8B takes HumanEval with 72.6 against 30.5 — a decisive 42.1-point margin. On MMLU the edge goes to Llama 3.1 8B (73 vs 60.1). For workloads weighted toward that benchmark, Llama 3.1 8B is the stronger default.

On a typical mid-range GPU, Mistral 7B Instruct pushes roughly 35 tokens/sec versus 30, so it is the more responsive choice for interactive or high-volume use. For long-context work, Llama 3.1 8B offers the bigger window (128k vs 32k tokens).

Memory, quantization & throughput

Across quantization levels, Llama 3.1 8B requires Q4 ≈ 6 GB, Q5 ≈ 7 GB, Q8 ≈ 10 GB, FP16 ≈ 18 GB, while Mistral 7B Instruct requires Q4 ≈ 5 GB, Q5 ≈ 6 GB, Q8 ≈ 9 GB, FP16 ≈ 16 GB. In practice Llama 3.1 8B 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, Llama 3.1 8B needs roughly 10 GB of system RAM to run on CPU and Mistral 7B Instruct about 8 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 30 tokens/sec from Llama 3.1 8B and 35 from Mistral 7B Instruct, scaling up to 80 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 Llama 3.1 8B or Mistral 7B Instruct to the card you actually own:

• On a 8 GB GPU: Llama 3.1 8B runs at Q5 (7 GB); Mistral 7B Instruct runs at Q5 (6 GB).
• On a 12 GB GPU: Llama 3.1 8B runs at Q8 (10 GB); Mistral 7B Instruct runs at Q8 (9 GB).
• On a 16 GB GPU: Llama 3.1 8B runs at Q8 (10 GB); Mistral 7B Instruct runs at FP16 (16 GB).
• On a 24 GB GPU: Llama 3.1 8B runs at FP16 (18 GB); Mistral 7B Instruct runs at FP16 (16 GB).

Benchmark scores

Reported benchmarks for Llama 3.1 8B: MMLU 73, HumanEval 72.6, GPQA 46.7.

Reported benchmarks for Mistral 7B Instruct: MMLU 60.1, HellaSwag 81.3, HumanEval 30.5.

Bottom line: which should you pick?

• Pick Mistral 7B Instruct if you need a permissive (Apache 2.0) license for commercial deployment.
• Pick Llama 3.1 8B for long-context work (up to 128k tokens).
• Pick Mistral 7B Instruct for lower VRAM and faster inference; pick Llama 3.1 8B for maximum headline quality.
• Pick Llama 3.1 8B if HumanEval performance is your priority (72.6 vs 30.5).

Which GPU should you buy to run Llama 3.1 8B?

To run Llama 3.1 8B locally at Q4, you need ~6 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).

Frequently asked questions

What is the difference between Llama 3.1 8B and Mistral 7B Instruct?

The headline differences: Llama 3.1 8B is a 8B model and Mistral 7B Instruct is 7B; their context windows differ (128k vs 32k tokens); they ship under different licenses (Llama 3 Community vs Apache 2.0). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.

Can Llama 3.1 8B and Mistral 7B Instruct run on a 24 GB GPU?

At a Q4 quantization, Llama 3.1 8B needs about 6 GB of VRAM and fits comfortably on a 24 GB GPU; Mistral 7B Instruct needs about 5 GB and fits comfortably on a 24 GB GPU. Mistral 7B Instruct is the lighter option for tight VRAM budgets.

Llama 3.1 8B vs Mistral 7B Instruct for coding — which is better?

On HumanEval, Llama 3.1 8B leads with 72.6 vs 30.5 (a 42.1-point gap), making it the stronger pick for code generation.

Which is faster, Llama 3.1 8B or Mistral 7B Instruct?

Mistral 7B Instruct is the smaller model (7B vs 8B), so on the same hardware it runs faster and uses less memory. The larger model trades speed for headline quality.

Which license is safer for commercial use, Llama 3.1 8B or Mistral 7B Instruct?

Mistral 7B Instruct ships under Apache 2.0, a permissive license with no usage restrictions, whereas the other is under Llama 3 Community — check its terms before commercial deployment.

Which has the longer context window, Llama 3.1 8B or Mistral 7B Instruct?

Llama 3.1 8B has the larger context window (128k vs 32k tokens), so it handles longer documents and codebases in a single prompt.