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Muse Spark 1.3 vs MiniMax M3.1 Flash

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

MiniMax M3.1 Flash wins outright — it scores higher and costs less.

MiniMax M3.1 Flash leads by 3.9 points overall while listing 7.6× cheaper per blended million tokens. There is no trade-off to reason about here; the only reason to pick the other is a constraint this table does not show, like an existing contract, a region, or a provider you are already on. MiniMax M3.1 Flash also leads on measured cost per point of capability, at $0.0015 per point.

meta

Muse Spark 1.3

Blended / 1M
$2.00
Context
1.0M
Released
Sep 2, 2026
Overall score
63.0
reasoningtool callingimage inputvideo inputfile inputprompt caching

minimax

MiniMax M3.1 Flash

Blended / 1M
$0.262
Context
1.0M
Released
Oct 19, 2026
Overall score
66.9
reasoningtool callingimage inputvideo inputprompt caching

Specs and pricing

MetricMuse Spark 1.3MiniMax M3.1 Flash
LiveBench overall

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

63.066.9win
Cost per point

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

$0.0137$0.0015win
Blended price / 1M

3:1 input:output mix, the usual shape of production traffic.

$2.00$0.262win
Input price / 1M$1.25$0.150win
Output price / 1M$4.25$0.600win
Cached input / 1M

Price of an input token served from the prompt cache, where the provider publishes one.

$0.150$0.015win
Context window1.0M1.0M
Max output tokens944Kwin524K

Benchmarks by category

An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Muse Spark 1.3 on top, MiniMax M3.1 Flash below, both out of 100.

Long-horizon agentic
25.5
28.8
Coding
90.2
83.9
Reasoning
89.7
82.9
Browser
30.9
80.6
Data analysis
79.6
71.4
Research
68.6
72.7
Documents & designtoo close to call
74.8
74.0

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.

WorkloadMuse Spark 1.3MiniMax M3.1 Flash
Support chatbot

1.2K in / 400 out × 200K requests

$560.80/mo$74.28/mo
RAG assistant

8K in / 600 out × 100K requests

$815.00/mo$102.00/mo
Coding agent

40K in / 4K out × 20K requests

$724.00/mo$92.40/mo
Document extraction

20K in / 1.5K out × 50K requests

$1,514/mo$188.25/mo
Bulk classification

500 in / 20 out × 5M requests

$3,000/mo$367.50/mo
Run these two through the cost calculator

Which should you pick?

You are running this at volume

MiniMax M3.1 Flash

Lowest measured cost per point of capability at $0.0015 per point — the gap compounds with every request.

Quality matters more than the bill

MiniMax M3.1 Flash

Highest overall LiveBench score of the two at 66.9.

The workload is coding or agentic work

MiniMax M3.1 Flash

Leads on long-horizon agentic — 28.8 against 25.5.

Muse Spark 1.3 vs MiniMax M3.1 Flash FAQ

Which is better, Muse Spark 1.3 or MiniMax M3.1 Flash?

MiniMax M3.1 Flash wins outright — it scores higher and costs less. MiniMax M3.1 Flash leads by 3.9 points overall while listing 7.6× cheaper per blended million tokens. There is no trade-off to reason about here; the only reason to pick the other is a constraint this table does not show, like an existing contract, a region, or a provider you are already on. MiniMax M3.1 Flash also leads on measured cost per point of capability, at $0.0015 per point.

Is Muse Spark 1.3 cheaper than MiniMax M3.1 Flash?

MiniMax M3.1 Flash is cheaper. On a 3:1 input:output blend, Muse Spark 1.3 lists at $2.00 per million tokens and MiniMax M3.1 Flash at $0.262 — MiniMax M3.1 Flash is 7.6× cheaper. Input and output are priced separately — Muse Spark 1.3 charges $1.25 in and $4.25 out, MiniMax M3.1 Flash charges $0.150 and $0.600 — so the model that looks cheaper flips depending on how output-heavy your workload is.

Muse Spark 1.3 vs MiniMax M3.1 Flash: which scores higher on benchmarks?

Muse Spark 1.3 scores 63.0 and MiniMax M3.1 Flash scores 66.9 overall on LiveBench, the weighted mean of its seven categories. That is a 3.9-point lead for MiniMax M3.1 Flash. 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, Muse Spark 1.3 or MiniMax M3.1 Flash?

MiniMax M3.1 Flash. 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. Muse Spark 1.3 works out at $0.0137 per point and MiniMax M3.1 Flash at $0.0015.

Does Muse Spark 1.3 or MiniMax M3.1 Flash have a bigger context window?

They are effectively the same — 1.0M for Muse Spark 1.3 and 1.0M for MiniMax M3.1 Flash.

Do Muse Spark 1.3 and MiniMax M3.1 Flash support prompt caching?

Both publish a cached-input rate: $0.150 per million for Muse Spark 1.3 and $0.015 for MiniMax M3.1 Flash, against full input rates of $1.25 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 2026-10-25, 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.