AI model comparison

Mistral Large 3 vs Llama 3.1 70B

Comparing a managed European-hosted flagship with a popular open-weight model for teams balancing capability, data-residency, and deployment flexibility.

Pricing & context

All figures below are list prices pulled directly from the Harpd pricing registry (last verified 2026-08-18). Prices change often — open each model’s source link to confirm before budgeting.

MetricMistral Large 3Llama 3.1 70B
ProviderMistralMeta · Together
Input / 1M tokens$2$0.88
Output / 1M tokens$6$0.88
Cached input / 1M tokensNot publishedNot published
Batch discount50% offNot published
Context window128,000 tokens128,000 tokens
SourceMistral pricing ↗Meta · Together pricing ↗
Mistral Large 3

Capabilities

Mistral Large 3 is a managed flagship with strong multilingual and reasoning performance, a 128k context window, and published batch pricing.

Llama 3.1 70B

Capabilities

Llama 3.1 70B is an open-weight model available via resale hosting with a 128k context window, favoring self-host or custom deployment control.

Recommendation

Both are capable at the 70B-class level, but they serve different operating models: Mistral Large 3 for managed, multilingual, EU-residency-friendly hosting, and Llama 3.1 70B for open-weight flexibility. Neither is strictly better across the board, so validate on your real tasks to decide which you can safely adopt.

Updated 2026-08-18.

Answer

Price tells you what a model costs. It does not tell you whether it can replace your current model on your real tasks.

Run your real tasks
Evidence
  • Benchmarks measured on Harpd are planned — see /benchmarks/.

Explore related tools

Model Compare

Side-by-side pricing and capability explorer for every model in the registry.

LLM Pricing

Full list-price table across providers, updated from official sources.

Cost per Successful Task

Why raw token price hides the real cost of failed or retried calls.

ModelSwitch

Find the cheapest model that passes your real tasks, measured on your workload.

Frequently asked questions

How do I know if a cheaper model is safe to use?
Price only tells you cost, not whether a model preserves your quality bar on real work. The safe path is to run a representative sample of your actual tasks through both models and compare outputs against your acceptance criteria. Harpd’s ModelSwitch does exactly this — it switches only once a cheaper model passes your tasks, not on price alone.
Is price a good proxy for quality?
No. List price reflects positioning and context length, not how a model performs on your specific workload. A cheaper model can be better for narrow tasks and worse for others. Treat price as one input to a cost-vs-capability decision, and validate capability with real-task testing rather than assuming a ranking.
What is cost per successful task?
Cost per successful task divides your total spend by the tasks that actually succeeded, folding in retries and failures that still cost tokens. A model with a lower per-token price can end up more expensive per successful task if it fails or retries more often. See /cost-per-successful-task/ for the full method.
How does ModelSwitch test a replacement model?
ModelSwitch runs your real tasks against a candidate model and compares results to your current one using your defined acceptance checks. It switches only when the cheaper model passes, and reports the measured savings. It does not rely on published benchmarks or price as a proxy for quality.