// head_to_head
Llama 3.3 70B Instruct vs Mistral Large 4
Published LiveBench scores across all seven categories, live list pricing, context windows, and the measured cost of a point of capability — for both models, side by side.
Llama 3.3 70B Instruct is the cheaper of the two; neither can be ranked on quality here.
Llama 3.3 70B Instruct does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.
meta-llama
Llama 3.3 70B Instruct
- Blended / 1M
- $0.640
- Context
- 131K
- Released
- Dec 6, 2024
- Overall score
- Not evaluated
mistralai
Mistral Large 4
- Blended / 1M
- $1.05
- Context
- 1.0M
- Released
- Oct 6, 2026
- Overall score
- 43.9
Specs and pricing
| Metric | Llama 3.3 70B Instruct | Mistral Large 4 |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | — | 43.9 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | $0.0162 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.640win | $1.05 |
| Input price / 1M | $0.590win | $0.700 |
| Output price / 1M | $0.790win | $2.10 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.295 | $0.070win |
| Context window | 131K | 1.0Mwin |
| Max output tokens | 33K | 262Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Llama 3.3 70B Instruct on top, Mistral Large 4 below, both out of 100.
What each one costs to run
Per-token prices are hard to feel. These are monthly list costs for both models across five workload shapes, using each provider's published cached-input rate where there is one.
| Workload | Llama 3.3 70B Instruct | Mistral Large 4 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $183.56/mo | $290.64/mo |
| RAG assistant 8K in / 600 out × 100K requests | $401.40/mo | $434.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $370.00/mo | $375.20/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $634.50/mo | $826.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $1,407/mo | $1,645/mo |
Which should you pick?
You need to fit large documents in one call
Mistral Large 4
Wider context window — 1.0M against 131K.
You are cost-constrained
Llama 3.3 70B Instruct
Cheaper on blended list price at $0.640 per million tokens.
Llama 3.3 70B Instruct vs Mistral Large 4 FAQ
Which is better, Llama 3.3 70B Instruct or Mistral Large 4?
Llama 3.3 70B Instruct is the cheaper of the two; neither can be ranked on quality here. Llama 3.3 70B Instruct does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.
Is Llama 3.3 70B Instruct cheaper than Mistral Large 4?
Llama 3.3 70B Instruct is cheaper. On a 3:1 input:output blend, Llama 3.3 70B Instruct lists at $0.640 per million tokens and Mistral Large 4 at $1.05 — Llama 3.3 70B Instruct is 1.6× cheaper. Input and output are priced separately — Llama 3.3 70B Instruct charges $0.590 in and $0.790 out, Mistral Large 4 charges $0.700 and $2.10 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does Llama 3.3 70B Instruct or Mistral Large 4 have a bigger context window?
Mistral Large 4 has the larger context window: 131K for Llama 3.3 70B Instruct against 1.0M for Mistral Large 4. Note that a window you can fill is not a window you should fill — retrieval quality usually degrades well before the limit, and you pay for every token you put in it.
Do Llama 3.3 70B Instruct and Mistral Large 4 support prompt caching?
Both publish a cached-input rate: $0.295 per million for Llama 3.3 70B Instruct and $0.070 for Mistral Large 4, against full input rates of $0.590 and $0.700. On a workload with a long stable prefix — a system prompt, a tool schema, a retrieved corpus — that changes the economics more than the headline price does.
Related comparisons
How these numbers are produced
- Price — provider list price from OpenRouter, refreshed every 15 minutes. “Blended” is a 3:1 input:output mix.
- Scores — LiveBench release 2026-10-25, using their own category map. Each model shows its strongest published run. A blank means “not evaluated”, never “bad”.
- Cost per point — the measured dollars LiveBench spent on the run, divided by the score it earned.
- “Win” — awarded only past a threshold: one full point on a benchmark score, 10% on a price, 25% on a context window. Anything tighter reports as a tie, because effort settings alone move a LiveBench score by more than that.
Published benchmarks rank models on someone else's tasks. Before committing, see LLM & agent evaluation for building an eval on your own.