// head_to_head
Mistral Large 2407 vs Kimi K3.1 Pro
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 2407 is the cheaper of the two; neither can be ranked on quality here.
Mistral Large 2407 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 2407
- Blended / 1M
- $3.00
- Context
- 131K
- Released
- Nov 19, 2024
- Overall score
- Not evaluated
moonshotai
Kimi K3.1 Pro
- Blended / 1M
- $4.00
- Context
- 1.0M
- Released
- Oct 23, 2026
- Overall score
- 71.3
Specs and pricing
| Metric | Mistral Large 2407 | Kimi K3.1 Pro |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | — | 71.3 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | $0.0104 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $3.00win | $4.00 |
| Input price / 1M | $2.00 | $2.00 |
| Output price / 1M | $6.00win | $10.00 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.200 | $0.200 |
| Context window | 131K | 1.0Mwin |
| Max output tokens | 105K | 944Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Mistral Large 2407 on top, Kimi K3.1 Pro 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 2407 | Kimi K3.1 Pro |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $830.40/mo | $1,150/mo |
| RAG assistant 8K in / 600 out × 100K requests | $1,240/mo | $1,480/mo |
| Coding agent 40K in / 4K out × 20K requests | $1,072/mo | $1,392/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $2,360/mo | $2,660/mo |
| Bulk classification 500 in / 20 out × 5M requests | $4,700/mo | $5,100/mo |
Which should you pick?
You need to fit large documents in one call
Kimi K3.1 Pro
Wider context window — 1.0M against 131K.
You are cost-constrained
Mistral Large 2407
Cheaper on blended list price at $3.00 per million tokens.
Mistral Large 2407 vs Kimi K3.1 Pro FAQ
Which is better, Mistral Large 2407 or Kimi K3.1 Pro?
Mistral Large 2407 is the cheaper of the two; neither can be ranked on quality here. Mistral Large 2407 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 2407 cheaper than Kimi K3.1 Pro?
Mistral Large 2407 is cheaper. On a 3:1 input:output blend, Mistral Large 2407 lists at $3.00 per million tokens and Kimi K3.1 Pro at $4.00 — Mistral Large 2407 is 33% cheaper. Input and output are priced separately — Mistral Large 2407 charges $2.00 in and $6.00 out, Kimi K3.1 Pro charges $2.00 and $10.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does Mistral Large 2407 or Kimi K3.1 Pro have a bigger context window?
Kimi K3.1 Pro has the larger context window: 131K for Mistral Large 2407 against 1.0M for Kimi K3.1 Pro. 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 2407 and Kimi K3.1 Pro support prompt caching?
Both publish a cached-input rate: $0.200 per million for Mistral Large 2407 and $0.200 for Kimi K3.1 Pro, against full input rates of $2.00 and $2.00. 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.