// head_to_head
MiMo-V2.6-Pro vs GLM 5.3
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.
MiMo-V2.6-Pro is the cheaper of the two; neither can be ranked on quality here.
MiMo-V2.6-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.
xiaomi
MiMo-V2.6-Pro
- Blended / 1M
- $0.544
- Context
- 1.0M
- Released
- Sep 21, 2026
- Overall score
- Not evaluated
z-ai
GLM 5.3
- Blended / 1M
- $1.29
- Context
- 1.3M
- Released
- Aug 18, 2026
- Overall score
- 76.1
Specs and pricing
| Metric | MiMo-V2.6-Pro | GLM 5.3 |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | — | 76.1 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | $0.2460 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.544win | $1.29 |
| Input price / 1M | $0.435win | $0.840 |
| Output price / 1M | $0.870win | $2.64 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.0036win | $0.156 |
| Context window | 1.0M | 1.3M |
| Max output tokens | 131K | 131K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — MiMo-V2.6-Pro on top, GLM 5.3 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 | MiMo-V2.6-Pro | GLM 5.3 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $142.94/mo | $363.55/mo |
| RAG assistant 8K in / 600 out × 100K requests | $227.64/mo | $556.80/mo |
| Coding agent 40K in / 4K out × 20K requests | $176.02/mo | $500.16/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $478.68/mo | $1,004/mo |
| Bulk classification 500 in / 20 out × 5M requests | $958.80/mo | $2,022/mo |
Which should you pick?
You are cost-constrained
MiMo-V2.6-Pro
Cheaper on blended list price at $0.544 per million tokens.
MiMo-V2.6-Pro vs GLM 5.3 FAQ
Which is better, MiMo-V2.6-Pro or GLM 5.3?
MiMo-V2.6-Pro is the cheaper of the two; neither can be ranked on quality here. MiMo-V2.6-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 MiMo-V2.6-Pro cheaper than GLM 5.3?
MiMo-V2.6-Pro is cheaper. On a 3:1 input:output blend, MiMo-V2.6-Pro lists at $0.544 per million tokens and GLM 5.3 at $1.29 — MiMo-V2.6-Pro is 2.4× cheaper. Input and output are priced separately — MiMo-V2.6-Pro charges $0.435 in and $0.870 out, GLM 5.3 charges $0.840 and $2.64 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does MiMo-V2.6-Pro or GLM 5.3 have a bigger context window?
They are effectively the same — 1.0M for MiMo-V2.6-Pro and 1.3M for GLM 5.3.
Do MiMo-V2.6-Pro and GLM 5.3 support prompt caching?
Both publish a cached-input rate: $0.0036 per million for MiMo-V2.6-Pro and $0.156 for GLM 5.3, against full input rates of $0.435 and $0.840. 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 2026-06-25, 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.