Mixtral 8x7B vs Llama 3.1 70B
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | Mixtral 8x7B | Llama 3.1 70B |
|---|---|---|
| Parameters | 47B | 70B |
| Author | Mistral AI | Meta |
| License | Apache 2.0 | Llama 3 Community |
| Context window | 0k | 0k |
| VRAM at Q4 | 26 GB | 40 GB |
| VRAM at Q5 | 32 GB | 48 GB |
| VRAM at Q8 | 50 GB | 75 GB |
| VRAM at FP16 | 94 GB | 140 GB |
| Use cases | chat, general, moe | chat, general |
Verdict
Both models sit in a similar size class. The pick depends on tags, license, and benchmarks rather than raw parameter count.
For unambiguous commercial use, Mixtral 8x7B has the safer license (Apache 2.0) compared to Llama 3 Community.
The two models at a glance
About Mixtral 8x7B
The Mistral AI MoE that popularized open-weight sparse models. Eight 7B experts deliver 47B-class output, but you pay 47B-class VRAM costs. Strengths: Quality well above dense models of equivalent active params, Strong coding and multilingual performance, Apache 2.0 license, Battle-tested in production stacks.
About Llama 3.1 70B
Meta's Llama 3.1 70B, the open-weight model that first felt like a credible GPT-4 alternative. Needs serious hardware — think dual 3090s or an A100. Strengths: Benchmark-leading quality for open-weight 70B, 128k context, Strong reasoning and code generation, Mature serving stack in vLLM, TGI, llama.cpp.
How they compare
Mixtral 8x7B comes from Mistral AI and Llama 3.1 70B from Meta, they belong to the Mistral and Llama 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 47B vs 70B parameters, Llama 3.1 70B is the larger of the two. At Q4, Mixtral 8x7B fits in about 26 GB of VRAM versus 40 GB for the other — a 14 GB difference that matters on consumer GPUs.
Where they overlap on benchmarks, Llama 3.1 70B takes HumanEval with 80.5 against 40.2 — a decisive 40.3-point margin. On MMLU the edge goes to Llama 3.1 70B (86 vs 70.6). For workloads weighted toward that benchmark, Llama 3.1 70B is the stronger default.
On a typical mid-range GPU, Mixtral 8x7B pushes roughly 12 tokens/sec versus 6, so it is the more responsive choice for interactive or high-volume use. For long-context work, Llama 3.1 70B offers the bigger window (128k vs 32k tokens).
Memory, quantization & throughput
Across quantization levels, Mixtral 8x7B requires Q4 ≈ 26 GB, Q5 ≈ 32 GB, Q8 ≈ 50 GB, FP16 ≈ 94 GB, while Llama 3.1 70B requires Q4 ≈ 40 GB, Q5 ≈ 48 GB, Q8 ≈ 75 GB, FP16 ≈ 140 GB. In practice Mixtral 8x7B spills past 24 GB even 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, Mixtral 8x7B needs roughly 48 GB of system RAM to run on CPU and Llama 3.1 70B about 64 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 12 tokens/sec from Mixtral 8x7B and 6 from Llama 3.1 70B, scaling up to 35 and 20 tokens/sec on high-end hardware.
Benchmark scores
Reported benchmarks for Mixtral 8x7B: MMLU 70.6, HumanEval 40.2, HellaSwag 86.7.
Reported benchmarks for Llama 3.1 70B: MMLU 86, GPQA 48, HumanEval 80.5.
Bottom line: which should you pick?
- Pick Mixtral 8x7B if you need a permissive (Apache 2.0) license for commercial deployment.
- Pick Llama 3.1 70B for long-context work (up to 128k tokens).
- Pick Mixtral 8x7B for lower VRAM and faster inference; pick Llama 3.1 70B for maximum headline quality.
- Pick Llama 3.1 70B if HumanEval performance is your priority (80.5 vs 40.2).
- Pick Mixtral 8x7B if your workload is moe.
Which GPU should you buy to run Llama 3.1 70B?
To run Llama 3.1 70B locally at Q4, you need ~40 GB of VRAM. The best value for this is a Apple Mac Studio (64+ GB unified memory).
As an Amazon Associate, BestLLMfor earns from qualifying purchases, at no extra cost to you. It does not influence our independent rankings.
Frequently asked questions
What is the difference between Mixtral 8x7B and Llama 3.1 70B?
The headline differences: Mixtral 8x7B is a 47B model and Llama 3.1 70B is 70B; their context windows differ (32k vs 128k tokens); they ship under different licenses (Apache 2.0 vs Llama 3 Community). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.
Can Mixtral 8x7B and Llama 3.1 70B run on a 24 GB GPU?
At a Q4 quantization, Mixtral 8x7B needs about 26 GB of VRAM and needs more than 24 GB (multi-GPU or heavier offload); Llama 3.1 70B needs about 40 GB and needs more than 24 GB. Mixtral 8x7B is the lighter option for tight VRAM budgets.
Mixtral 8x7B vs Llama 3.1 70B for coding — which is better?
On HumanEval, Llama 3.1 70B leads with 80.5 vs 40.2 (a 40.3-point gap), making it the stronger pick for code generation.
Which is faster, Mixtral 8x7B or Llama 3.1 70B?
Mixtral 8x7B is the smaller model (47B vs 70B), 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, Mixtral 8x7B or Llama 3.1 70B?
Mixtral 8x7B 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, Mixtral 8x7B or Llama 3.1 70B?
Llama 3.1 70B has the larger context window (128k vs 32k tokens), so it handles longer documents and codebases in a single prompt.