// head_to_head
Mistral Large 4 vs Kimi K2 0711
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.
Mistral Large 4 and Kimi K2 0711 are priced within ~10% of each other.
Kimi K2 0711 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.
mistralai
Mistral Large 4
- Blended / 1M
- $1.05
- Context
- 1.0M
- Released
- Oct 6, 2026
- Overall score
- 43.9
moonshotai
Kimi K2 0711
- Blended / 1M
- $1.00
- Context
- 131K
- Released
- Jul 11, 2025
- Overall score
- Not evaluated
Specs and pricing
| Metric | Mistral Large 4 | Kimi K2 0711 |
|---|---|---|
| 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. | $1.05 | $1.00 |
| Input price / 1M | $0.700 | $0.570win |
| Output price / 1M | $2.10 | $2.30 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.070 | — |
| Context window | 1.0Mwin | 131K |
| Max output tokens | 262Kwin | 98K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Mistral Large 4 on top, Kimi K2 0711 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 | Mistral Large 4 | Kimi K2 0711 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $290.64/mo | $320.80/mo |
| RAG assistant 8K in / 600 out × 100K requests | $434.00/mo | $594.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $375.20/mo | $640.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $826.00/mo | $742.50/mo |
| Bulk classification 500 in / 20 out × 5M requests | $1,645/mo | $1,655/mo |
Which should you pick?
You need to fit large documents in one call
Mistral Large 4
Wider context window — 1.0M against 131K.
Mistral Large 4 vs Kimi K2 0711 FAQ
Which is better, Mistral Large 4 or Kimi K2 0711?
Mistral Large 4 and Kimi K2 0711 are priced within ~10% of each other. Kimi K2 0711 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 Mistral Large 4 cheaper than Kimi K2 0711?
They cost about the same. Both land near $1.05 per million tokens on a 3:1 input:output blend, so price is unlikely to be the deciding factor between them.
Does Mistral Large 4 or Kimi K2 0711 have a bigger context window?
Mistral Large 4 has the larger context window: 1.0M for Mistral Large 4 against 131K for Kimi K2 0711. 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 Mistral Large 4 and Kimi K2 0711 support prompt caching?
Mistral Large 4 publishes a cached-input rate of $0.070 per million tokens against a full input rate of $0.700. The catalogue lists no separate cached rate for Kimi K2 0711, which means the provider does not price it separately here — not that caching is unavailable.
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.