// head_to_head
Mi:dm K 3.0 Pro vs Qwen3.8 27B
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.
Mi:dm K 3.0 Pro is the cheaper of the two; neither can be ranked on quality here.
Mi:dm K 3.0 Pro 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.
kt
Mi:dm K 3.0 Pro
- Blended / 1M
- $0.350
- Context
- 262K
- Released
- Nov 5, 2026
- Overall score
- Not evaluated
qwen
Qwen3.8 27B
- Blended / 1M
- $0.581
- Context
- 1.0M
- Released
- Aug 14, 2026
- Overall score
- 54.0
Specs and pricing
| Metric | Mi:dm K 3.0 Pro | Qwen3.8 27B |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | — | 54.0 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | $0.0106 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.350win | $0.581 |
| Input price / 1M | $0.200 | $0.150win |
| Output price / 1M | $0.800win | $1.88 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.040 | $0.037 |
| Context window | 262K | 1.0Mwin |
| Max output tokens | 236K | 236K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Mi:dm K 3.0 Pro on top, Qwen3.8 27B 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 | Mi:dm K 3.0 Pro | Qwen3.8 27B |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $100.48/mo | $177.90/mo |
| RAG assistant 8K in / 600 out × 100K requests | $144.00/mo | $187.50/mo |
| Coding agent 40K in / 4K out × 20K requests | $134.40/mo | $207.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $252.00/mo | $285.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $500.00/mo | $506.25/mo |
Which should you pick?
You need to fit large documents in one call
Qwen3.8 27B
Wider context window — 1.0M against 262K.
You are cost-constrained
Mi:dm K 3.0 Pro
Cheaper on blended list price at $0.350 per million tokens.
Mi:dm K 3.0 Pro vs Qwen3.8 27B FAQ
Which is better, Mi:dm K 3.0 Pro or Qwen3.8 27B?
Mi:dm K 3.0 Pro is the cheaper of the two; neither can be ranked on quality here. Mi:dm K 3.0 Pro 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 Mi:dm K 3.0 Pro cheaper than Qwen3.8 27B?
Mi:dm K 3.0 Pro is cheaper. On a 3:1 input:output blend, Mi:dm K 3.0 Pro lists at $0.350 per million tokens and Qwen3.8 27B at $0.581 — Mi:dm K 3.0 Pro is 1.7× cheaper. Input and output are priced separately — Mi:dm K 3.0 Pro charges $0.200 in and $0.800 out, Qwen3.8 27B charges $0.150 and $1.88 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does Mi:dm K 3.0 Pro or Qwen3.8 27B have a bigger context window?
Qwen3.8 27B has the larger context window: 262K for Mi:dm K 3.0 Pro against 1.0M for Qwen3.8 27B. 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 Mi:dm K 3.0 Pro and Qwen3.8 27B support prompt caching?
Both publish a cached-input rate: $0.040 per million for Mi:dm K 3.0 Pro and $0.037 for Qwen3.8 27B, against full input rates of $0.200 and $0.150. 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 2026-11-06, 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.