Mistral Large 3 vs Llama 4: Price, Speed & Accuracy Compared
Quick answer: If you want the strongest general-purpose open-weight model with a permissive license, start with Mistral Large 3. If you need a very long context window or a model small enough to run on one GPU, Llama 4 Scout is the better fit. Llama 4 Maverick sits in between and is the choice when you want a cheap hosted multimodal model.
The catch is that these are not same-generation rivals. Llama 4 launched in April 2025; Mistral Large 3 arrived in December 2025. This comparison is about which one to deploy today, not which one "won."
At a glance
| Mistral Large 3 | Llama 4 Maverick | Llama 4 Scout | |
|---|---|---|---|
| Developer | Mistral AI | Meta | Meta |
| Architecture | Mixture-of-experts, ~675B total params | MoE, 400B total / 17B active, 128 experts | MoE, 109B total / 17B active, 16 experts |
| Context window | ~256K tokens | 1M tokens | Up to 10M tokens (advertised) |
| Input | Text + image | Text + image | Text + image |
| License | Apache 2.0 | Llama 4 Community License | Llama 4 Community License |
| Typical hosted price (per 1M tokens, in/out) | ~$0.50 / $1.50 (Mistral list price) | ~$0.27 input at some hosts | ~$0.10 / $0.30 at some hosts |
| Self-host footprint | Multi-GPU node | ~4x H100 80GB (INT4) | 1x H100 80GB (INT4) |
Specs from Mistral's documentation and Meta's Llama 4 launch materials; hosted prices vary by provider and change often. Check the sources linked below before you budget.
How we approached the comparison
Most "X vs Y" posts quote a leaderboard and stop. That tells you very little about your workload, so this article separates published facts from things you must measure yourself.
Price: what you actually pay
Per-token list price is only the starting point.
Mistral Large 3 is priced per Mistral's API at roughly $0.50 per million input tokens and $1.50 per million output tokens, with cached input far cheaper and a 50% discount on batch requests. Note that some third-party trackers still show an older $2/$5 rate for the "Large" name, which belongs to a previous generation. Always read Mistral's own page.
Llama 4 has no single price, because Meta releases weights rather than selling an API. Hosted rates depend on the provider and run lower than Mistral's for Scout and Maverick, which is the main reason teams pick them for high-volume, cost-sensitive work.
Self-hosting changes the math. One 2026 comparison estimated Scout at roughly $0.36 per million tokens on a rented H100 at about 800 tokens per second, but that assumes constant utilization. A half-idle GPU costs twice as much per token. Maverick needs about four H100s, which raises the break-even point.
Rule of thumb: below roughly a few million tokens a day, use a hosted API. Self-hosting starts to pay off only when the GPU stays busy.
Speed: what to expect and how to measure it
Latency depends on the provider, batch size, quantization and prompt length far more than on the model name. Be skeptical of any article that gives one "tokens per second" number for an open-weight model.
What the architecture tells you:
- Scout activates only 17B parameters per token and fits on one GPU, so it is the natural low-latency option.
- Maverick also activates 17B parameters but must keep 400B in memory, so it needs more hardware to serve at the same speed.
- Mistral Large 3 is the largest of the three by total parameters, so expect it to need more serving capacity than Scout.
Accuracy: read benchmarks carefully
Two cautions before you trust any score.
First, Meta's launch showed Maverick near 1,400 Elo on LMArena. Meta later acknowledged the version submitted there was an experimental chat-optimized variant, not the released model. Treat that number as marketing context, not a measure of the downloadable weights.
Second, third-party benchmark tables for these models disagree with each other, sometimes by several points on the same test (one tracker lists Maverick HumanEval at 82.4% while some Meta sources report 86.4%). Differences that small say more about prompt format than model quality.
What holds up across sources:
- Llama 4 Maverick is stronger than Scout on reasoning and coding.
- Llama 4 has a strong fine-tuning ecosystem and mature tool-calling support in many serving stacks.
- Mistral Large 3, as the newer and larger model, is generally positioned as the more capable general assistant, but verify that on your own prompts.
- Long-context claims degrade in practice. Scout's 10M window is real, but retrieval quality drops well before the limit, so test with your documents.
Licensing: the decision that is easy to miss
This is often the deciding factor.
- Mistral Large 3 uses Apache 2.0, a standard open-source license with no usage caps.
- Llama 4 uses Meta's Community License, which allows commercial use but requires a separate license if your company exceeds 700 million monthly active users, and carries attribution and acceptable-use terms. Meta calls it open, but it is not an OSI-approved open-source license.
For most startups the Llama cap never applies. For legal teams that want zero ambiguity, Apache 2.0 is simpler to approve.
Which should you choose?
| Your situation | Pick |
|---|---|
| Need clean legal terms and a strong all-round model | Mistral Large 3 |
| Processing huge documents or codebases in one pass | Llama 4 Scout (validate long-context quality first) |
| One GPU, on-prem, tight budget | Llama 4 Scout |
| Cheap hosted multimodal at scale | Llama 4 Maverick |
| Heavy fine-tuning on your own data | Llama 4 (larger tooling ecosystem) |
| Highest quality, cost secondary | Test Mistral Large 3 against a closed frontier model too |
Common mistakes when comparing open-weight models
- Comparing list prices across different hosts. Quantization and context limits change from provider to provider.
- Filling the context window. A larger window costs more and often reduces accuracy. Retrieve what you need instead.
- Ignoring output length. Output tokens cost several times more than input tokens.
- Skipping your own evaluation. Thirty well-chosen prompts beat any leaderboard for your use case.
Frequently asked questions
Is Mistral Large 3 better than Llama 4?
Not universally. Mistral Large 3 is newer and larger and ships under Apache 2.0, so it is the safer default for general-purpose work. Llama 4 Scout wins on context length and single-GPU deployment, and Maverick is attractive on hosted price. Run a small test on your own tasks before committing.
Which is cheaper, Mistral Large 3 or Llama 4?
Hosted Llama 4 Scout and Maverick are typically cheaper per token than Mistral Large 3's list price. Self-hosting can be cheaper still, but only if your GPUs stay highly utilized.
Can I use Llama 4 commercially?
Yes, under the Llama 4 Community License. Companies with more than 700 million monthly active users need a separate license from Meta. Read the license text for attribution and acceptable-use terms.
Does Llama 4 Scout really support 10 million tokens?
It is designed for that length, but practical retrieval quality declines well before the limit. Test with your own long documents rather than relying on the headline number.
RESULT
Mistral Large 3 and Llama 4 solve different problems. Choose Mistral Large 3 when licensing clarity and general capability matter most, and choose Llama 4 Scout or Maverick when context length, hardware limits or hosted price drive the decision. The reliable way to decide is to run your own 30-prompt test and publish the results, because leaderboards and vendor claims will not reflect your workload.
Last updated October 28, 2026. Pricing and specifications change frequently; verify with the official sources below.