// head_to_head
SpaceXAI: Grok 4.6 vs MiMo-V2.6-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.
Effectively the same quality — MiMo-V2.6-Pro is the cheaper way to get it.
The two are within 0.2 points overall, which is inside the range that effort settings alone move a LiveBench score, so treat them as quality-equivalent. MiMo-V2.6-Pro lists 5.5× cheaper per blended million tokens. When quality ties, cost is the whole decision. The two cost measures disagree here, which is worth knowing: MiMo-V2.6-Pro has the lower sticker price, but SpaceXAI: Grok 4.6 earns each point of capability for less — $0.8427 against $0.9976 — because per-token rates do not predict how many tokens a model actually spends on a task.
x-ai
SpaceXAI: Grok 4.6
- Blended / 1M
- $3.00
- Context
- 500K
- Released
- Aug 12, 2026
- Overall score
- 74.6
xiaomi
MiMo-V2.6-Pro
- Blended / 1M
- $0.544
- Context
- 1.1M
- Released
- Sep 21, 2026
- Overall score
- 74.9
Specs and pricing
| Metric | SpaceXAI: Grok 4.6 | MiMo-V2.6-Pro |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 74.6 | 74.9 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.8427win | $0.9976 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $3.00 | $0.544win |
| Input price / 1M | $2.00 | $0.435win |
| Output price / 1M | $6.00 | $0.870win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.500 | $0.0036win |
| Context window | 500K | 1.1Mwin |
| Max output tokens | 450Kwin | 131K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — SpaceXAI: Grok 4.6 on top, MiMo-V2.6-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 | SpaceXAI: Grok 4.6 | MiMo-V2.6-Pro |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $852.00/mo | $142.94/mo |
| RAG assistant 8K in / 600 out × 100K requests | $1,360/mo | $227.64/mo |
| Coding agent 40K in / 4K out × 20K requests | $1,240/mo | $176.02/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $2,375/mo | $478.68/mo |
| Bulk classification 500 in / 20 out × 5M requests | $4,850/mo | $958.80/mo |
Which should you pick?
You are running this at volume
SpaceXAI: Grok 4.6
Lowest measured cost per point of capability at $0.8427 per point — the gap compounds with every request.
The workload is coding or agentic work
SpaceXAI: Grok 4.6
Leads on coding — 89.1 against 81.5.
You need to fit large documents in one call
MiMo-V2.6-Pro
Wider context window — 1.1M against 500K.
SpaceXAI: Grok 4.6 vs MiMo-V2.6-Pro FAQ
Which is better, SpaceXAI: Grok 4.6 or MiMo-V2.6-Pro?
Effectively the same quality — MiMo-V2.6-Pro is the cheaper way to get it. The two are within 0.2 points overall, which is inside the range that effort settings alone move a LiveBench score, so treat them as quality-equivalent. MiMo-V2.6-Pro lists 5.5× cheaper per blended million tokens. When quality ties, cost is the whole decision. The two cost measures disagree here, which is worth knowing: MiMo-V2.6-Pro has the lower sticker price, but SpaceXAI: Grok 4.6 earns each point of capability for less — $0.8427 against $0.9976 — because per-token rates do not predict how many tokens a model actually spends on a task.
Is SpaceXAI: Grok 4.6 cheaper than MiMo-V2.6-Pro?
MiMo-V2.6-Pro is cheaper. On a 3:1 input:output blend, SpaceXAI: Grok 4.6 lists at $3.00 per million tokens and MiMo-V2.6-Pro at $0.544 — MiMo-V2.6-Pro is 5.5× cheaper. Input and output are priced separately — SpaceXAI: Grok 4.6 charges $2.00 in and $6.00 out, MiMo-V2.6-Pro charges $0.435 and $0.870 — so the model that looks cheaper flips depending on how output-heavy your workload is.
SpaceXAI: Grok 4.6 vs MiMo-V2.6-Pro: which scores higher on benchmarks?
SpaceXAI: Grok 4.6 scores 74.6 and MiMo-V2.6-Pro scores 74.9 overall on LiveBench, the mean of its seven categories. That gap is inside the range that effort settings alone move a score, so treat them as equivalent on published quality. Category scores differ from the overall figure — a model can lead on reasoning and trail on coding, which the per-category table above breaks out.
Which gives better value for money, SpaceXAI: Grok 4.6 or MiMo-V2.6-Pro?
SpaceXAI: Grok 4.6. Cost per point divides the measured dollars LiveBench spent running the benchmark by the score it earned, so it captures something token pricing misses: a reasoning model can emit many times more tokens than its per-token rate suggests. SpaceXAI: Grok 4.6 works out at $0.8427 per point and MiMo-V2.6-Pro at $0.9976.
Does SpaceXAI: Grok 4.6 or MiMo-V2.6-Pro have a bigger context window?
MiMo-V2.6-Pro has the larger context window: 500K for SpaceXAI: Grok 4.6 against 1.1M for MiMo-V2.6-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 SpaceXAI: Grok 4.6 and MiMo-V2.6-Pro support prompt caching?
Both publish a cached-input rate: $0.500 per million for SpaceXAI: Grok 4.6 and $0.0036 for MiMo-V2.6-Pro, against full input rates of $2.00 and $0.435. 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 2026-09-02, 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.