Assertion Supported AI assessment confidence: 95% certainty 4/5 debate potential 3/5

Mensch: Mixtral matches Llama 2 70B performance at one-sixth the cost

Arthur Mensch · Safety in Numbers: Keeping AI Open · Dec 28, 2023 · at 11:38

Arthur Mensch, co-founder of Mistral AI, compares Mixtral's speed, cost, and benchmark performance directly against Meta's Llama 2 70B model.

0:00 / 0:07exact quote · 7.3s

▶ Watch the full episode on YouTube → 720p mp4 · rendered on demand · StarZero watermark

“Mixtral is actually on par with Lama-to-seven TB while being approximately six times cheaper or six times faster for the same price.”

quote is from the automated transcript, cleaned for reading: filler sounds and stutters are removed, nothing is rephrased. names can be misheard (the analysis reads context, assessments check outside sources). how →

The claim, plainly

Mistral AI's Mixtral model matches Meta's Llama 2 70B in benchmark performance while being approximately six times cheaper or faster at inference.

a quote alone can be ambiguous, so this restates it with the references and context of the conversation around it resolved. AI-written; the tape below the player is the source.

Assessment

The claim fully satisfies the resolution criteria based on both official technical benchmarks and independent evaluations. Official results published by Mistral AI show Mixtral 8x7B matching or outperforming Llama 2 70B across most benchmarks (e.g., MMLU 70.6% vs 69.9%, GSM8K 58.4% vs 53.6%, HumanEval 40.2% vs 29.9%), and overall higher average performance was confirmed by OpenCompass and the Hugging Face Open LLM Leaderboard. For inference performance, Mixtral 8x7B activates 12.9B parameters per token compared to Llama 2 70B's 70B parameters, representing a ~5.42x theoretical reduction in compute FLOPs per token. Serving benchmarks across frameworks and API providers (such as vLLM, Anyscale, and Artificial Analysis) confirmed ~4x to 6x higher throughput/speed and corresponding reductions in per-token compute cost.

Mistral AI technical report shows Mixtral 8x7B matching or beating Llama 2 70B on MMLU (70.6% vs 69.9%), GSM8K (58.4% vs 53.6%), and HumanEval (40.2% vs 29.9%).
Independent evaluations (LMSYS Chatbot Arena, OpenCompass, Hugging Face Open LLM Leaderboard) confirmed Mixtral 8x7B matched or exceeded Llama 2 70B in aggregate scores.
Mixtral 8x7B activates 12.9B parameters per token versus 70B for Llama 2 70B, yielding a ~5.42x compute reduction FLOPs ratio and ~4x to 6x measured inference throughput gains per GPU compute unit.

Checked against

[1] mistral.ai [2] arxiv.org [3] luseratech.com [4] prompthackers.co [5] truefoundry.com [6] neon.com [7] arxiv.org [8] arena.ai

Anyscale "Mixtral 8x7B" "Llama 2 70B" speed latency cost throughput benchmark Mixtral 8x7B Llama 2 70B inference speed cost throughput 6x Mistral AI Mixtral announcement blog benchmark Llama 2 70B Mixtral 8x7B vs Llama 2 70B benchmark MMLU HumanEval GSM8K "Mixtral 8x7B" "Llama 2 70B" LMSYS Chatbot Arena Elo rating "Mixtral of Experts" "2401.04088" "Table 2" OR "Table 3" "Llama 2 70B" "MMLU" "Mixtral 8x7B" "Llama 2 70B" Hugging Face Open LLM Leaderboard score "Mixtral 8x7B" "Llama 2 70B" "Artificial Analysis" inference speed cost throughput

assessed as of 2026-07-28

This assessment is an AI reading of a public statement, made against the cited references with the confidence shown. It can be wrong. The quote, the tape and the references are all on this page; if something is off, use report an error below. full disclaimer →

Mentioned

Llama 2 70B Mistral AI Mixtral

⚑ Spot an error? Report it

More from Arthur Mensch

Prediction Not checkable as stated

Mensch: AI export controls will not stop European or Asian progress

“Effectively, if there's some export control over weight this is not going to stop any country in Europe, any country in Asia to continue its progress. And they will collaborate to actually accelerate that progress.”

Arthur Mensch Mar 20, 2025 ▶ 38:25 Jensen Huang & Arthur Mensch: Why Every Nation Needs Its Own AI Strategy

Opinion

Mensch: AI regulation should target applications, not foundational math

“What you want to regulate is the application, and the issue we had, and the issue we're still having now is, We hear a lot of people saying we should regulate the tech, so we should regulate the function, the mathematics behind it, but really you never use a l…”

Arthur Mensch Dec 28, 2023 ▶ 28:44 Safety in Numbers: Keeping AI Open

Opinion

Mensch: FLOP count is the wrong metric for regulating AI models

“Pre-market conditions like flops, the number of flops that you do to create a model is definitely not the right way of doing of measuring the performance of a model.”

Arthur Mensch Dec 28, 2023 ▶ 30:35 Safety in Numbers: Keeping AI Open

Prediction Not checkable as stated

Mensch: AI will impact every country's GDP by double digits

“It will have an impact on GDP of every country in the double digits in the coming years.”

Arthur Mensch Mar 20, 2025 ▶ 0:06 Jensen Huang & Arthur Mensch: Why Every Nation Needs Its Own AI Strategy

Prediction Not checkable as stated

Mensch: General-purpose base AI models will eventually become open source

“There are general purpose models like base models, compression of the web. That are eventually going to be open source and that can serve as the right basis for constructing specialized systems.”

Arthur Mensch Mar 20, 2025 ▶ 4:12 Jensen Huang & Arthur Mensch: Why Every Nation Needs Its Own AI Strategy

Insight

Mensch: Nations and enterprises must own their AI cultural alignment

“For me, this is an inherent limitation of centralized AI models, where you're thinking that you can encode some universal values and some universal expertise into a general purpose model. At some point you need to take the general purpose model and ask a speci…”

Arthur Mensch Mar 20, 2025 ▶ 17:09 Jensen Huang & Arthur Mensch: Why Every Nation Needs Its Own AI Strategy

Mensch: Mixtral matches Llama 2 70B performance at one-sixth the cost

More from Arthur Mensch

Turn any episode into a week of clips.