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SpaceXAI: Grok 4.7 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.

OpenRouter + LiveBenchAll comparisonsFull leaderboard

Effectively the same quality — MiMo-V2.6-Pro is the cheaper way to get it.

The two are within 0.8 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. MiMo-V2.6-Pro also leads on measured cost per point of capability, at $0.9976 per point.

x-ai

SpaceXAI: Grok 4.7

Blended / 1M
$3.00
Context
500K
Released
Sep 21, 2026
Overall score
74.1
reasoningtool callingimage inputfile inputprompt caching

xiaomi

MiMo-V2.6-Pro

Blended / 1M
$0.544
Context
1.1M
Released
Sep 21, 2026
Overall score
74.9
reasoningtool callingimage inputvideo inputaudio inputprompt caching

Specs and pricing

MetricSpaceXAI: Grok 4.7MiMo-V2.6-Pro
LiveBench overall

Mean of the seven LiveBench category scores, 0–100. Higher is better.

74.174.9
Cost per point

Measured benchmark spend divided by overall score — dollars per point of capability.

$1.83$0.9976win
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 window500K1.1Mwin
Max output tokens450Kwin131K

Benchmarks by category

An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — SpaceXAI: Grok 4.7 on top, MiMo-V2.6-Pro below, both out of 100.

Coding
84.5
81.5
Reasoning
82.7
79.6
Data analysis
76.9
72.3

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.

WorkloadSpaceXAI: Grok 4.7MiMo-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
Run these two through the cost calculator

Which should you pick?

You are running this at volume

MiMo-V2.6-Pro

Lowest measured cost per point of capability at $0.9976 per point — the gap compounds with every request.

The workload is coding or agentic work

SpaceXAI: Grok 4.7

Leads on coding — 84.5 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 4.7 vs MiMo-V2.6-Pro FAQ

Which is better, SpaceXAI: Grok 4.7 or MiMo-V2.6-Pro?

Effectively the same quality — MiMo-V2.6-Pro is the cheaper way to get it. The two are within 0.8 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. MiMo-V2.6-Pro also leads on measured cost per point of capability, at $0.9976 per point.

Is SpaceXAI: Grok 4.7 cheaper than MiMo-V2.6-Pro?

MiMo-V2.6-Pro is cheaper. On a 3:1 input:output blend, SpaceXAI: Grok 4.7 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 4.7 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 4.7 vs MiMo-V2.6-Pro: which scores higher on benchmarks?

SpaceXAI: Grok 4.7 scores 74.1 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 4.7 or MiMo-V2.6-Pro?

MiMo-V2.6-Pro. 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 4.7 works out at $1.83 per point and MiMo-V2.6-Pro at $0.9976.

Does SpaceXAI: Grok 4.7 or MiMo-V2.6-Pro have a bigger context window?

MiMo-V2.6-Pro has the larger context window: 500K for SpaceXAI: Grok 4.7 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 4.7 and MiMo-V2.6-Pro support prompt caching?

Both publish a cached-input rate: $0.500 per million for SpaceXAI: Grok 4.7 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.