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There is no evidence-backed overall winner between Mistral and Llama 3 as APIs. Mistral offers a documented hosted inference catalog with model-specific prices; Llama 3 is a family of models, and the API experience, price, and service terms depend on who hosts the model. Choose a named model and a specific endpoint, then compare them on your workload—not just on the publisher names.
First, distinguish the model from the API
“Mistral vs Llama 3” mixes two different kinds of choices. Mistral AI documents a hosted inference service and its catalog. Meta’s Llama catalog describes model families and variants that can be deployed through a hosting provider or in your own environment. An API call to a hosted Llama model is therefore not automatically a Meta-hosted API call: the provider operating the endpoint sets its service terms and may set its own price.
Use exact model names in any comparison. Mistral’s current catalog includes models such as Mistral Large 3, Medium 3.5, and Small 4. Meta’s catalog groups Llama 3.1, 3.2, and 3.3 variants. “Llama 3” alone does not identify a model size, capability, or endpoint.
What the published prices do—and do not—show
Mistral AI’s pricing documentation, accessed October 7, 2026, lists the following hosted inference rates per million tokens. These are model-specific list prices, not a promise that every request will cost the same or that prices will remain unchanged.
| Named model | Input tokens, per million | Output tokens, per million | Example token charge: 1M input + 1M output |
|---|---|---|---|
| Mistral Large 3 | $0.50 | $1.50 | $2.00 |
| Mistral Medium 3.5 | $1.50 | $7.50 | $9.00 |
| Mistral Small 4 | $0.15 | $0.60 | $0.75 |
The example column is arithmetic using the listed input and output rates; it is not a measured workload or a quote for a complete deployment. Your bill depends on the actual input/output mix and the selected model. Mistral’s pricing FAQ says batch processing can reduce listed prices by 50%, and cached input can reduce input cost by up to 90% for repeated prompts. Those savings apply only when the relevant feature and workload qualify.
Meta’s official Llama model page displayed $0.10 input and $0.40 output per million tokens beside Llama 3.3 70B when accessed October 7, 2026. The page content reviewed does not identify the provider behind those figures or establish comparable endpoint terms. The arithmetic would total $0.50 for one million input and one million output tokens at those rates, but that figure is not a confirmed Meta-hosted API quote and should not be treated as a like-for-like price against Mistral’s documented rates.
How to make a fair model comparison
A model name and a price card are not enough to decide which service will work better. Evaluate the exact endpoint and model versions you could deploy, using the same tasks and constraints for each candidate.
- Choose the actual candidates. Record the full model/version name and, for Llama, the company or service hosting the API. Do not label a third-party endpoint as Meta’s API.
- Use representative prompts and outputs. Include the kinds of requests your application handles, realistic context lengths, and the output limit you intend to use.
- Score quality against your requirements. Apply the same rubric to both candidates—for example, correctness, instruction following, format validity, and performance on your domain tasks. A publisher’s benchmark panel is not a neutral head-to-head test of your workload.
- Measure latency on the intended route. Record the endpoint, region, request conditions, and sample size. Latency can depend on the hosting service and deployment, not only on the model.
- Calculate cost from your token mix. Estimate input, output, and—where applicable—cached input separately. Include batch pricing only if your workload can use batch processing and you have confirmed the applicable terms.
- Check production terms with the host. Confirm availability in your required region, rate limits, uptime commitments, support, data handling, and privacy terms in that provider’s current documentation.
Meta’s Llama page includes publisher-reported benchmark comparisons. Treat those figures as results under the methodology listed on that page, not as independent evidence that a Llama model will outperform a Mistral model on your own tasks.
Match model capabilities to the job
The model family matters as much as the API brand. Meta’s catalog describes Llama 3.3 as a text-only 70B instruction-tuned model; Llama 3.2 includes lightweight 1B and 3B variants as well as 11B and 90B vision-enabled models; and Llama 3.1 includes 8B, 70B, and 405B instruction-tuned versions. Those differences affect which tasks and deployment constraints are relevant, but the catalog alone does not establish which model will perform best for a particular application.
Mistral’s catalog spans general-purpose and specialized models. Select based on the task and the exact model’s documented capabilities. If a requirement includes image input, for instance, compare a vision-capable candidate with an endpoint that actually supports the needed input—not a text-only model merely because it shares a family name.
Hosted API or self-hosted weights?
A hosted API and self-hosting are different operating choices. With a hosted endpoint, the provider runs inference and you evaluate its API price and service terms. With self-hosting, access to model weights can give you deployment control, but the API bill is replaced by the costs and responsibilities of running the infrastructure and service. The available evidence does not establish a hardware configuration or total-cost figure for either model family, so a self-hosting cost comparison requires your own deployment design and estimates.
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Mistral’s “Introducing Mistral 3” announcement describes self-hosting support paths for that family. Meta describes Llama models as deployable in different environments. Neither statement, by itself, establishes that self-hosting will be cheaper or simpler for your workload.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Check the license for the exact model
Do not assume every model in either family has identical licensing terms, or use “open source” as a substitute for checking them. Mistral says most of its open models use Apache 2.0, while some models have different terms. Its Mistral 3 announcement describes that specific family as Apache 2.0; that statement should not be generalized to every Mistral model.
Mistral Help Center guidance dated August 12, 2026 describes a modified MIT exception for certain models. Under that guidance, a company above $20 million in monthly revenue must obtain a commercial license or use Mistral Studio. This threshold and condition apply to the models covered by that guidance, not automatically to all Mistral models. Meta’s catalog uses the phrase “open-source AI models,” but the applicable license and conditions still need to be checked for the specific Llama version you plan to use.
Which should you choose?
- Choose a Mistral hosted model when you want to start from Mistral’s documented hosted inference catalog and can select a model whose published rates and capabilities fit your workload.
- Choose a hosted Llama endpoint when a specific provider offers the Llama version you need and its price, regions, data terms, rate limits, and reliability meet your requirements. Compare that provider’s endpoint—not an assumed Meta API.
- Consider self-hosting when deployment control is important and you can validate the model-specific license and account for infrastructure, operations, and security work.
- Run a workload test before committing when quality, latency, or total cost could change the decision. The published product pages and prices do not settle those questions for your application.
As of October 2026, Mistral’s published rates provide a direct starting point for a hosted API cost estimate. The Llama 3.3 70B price panel does not establish an equivalent Meta-hosted offer, so it cannot support a reliable provider-versus-provider verdict on its own.
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