text model comparison
MiniMax M3.1 Flash vs Muse Spark Open
MiniMax M3.1 Flash and Muse Spark Open are both current text models, from MiniMax and Meta. On Arena WebDev, MiniMax M3.1 Flash scores 1582 Elo and Muse Spark Open 1526 Elo (Arena, measured October 28, 2026). Both accept 1,000,000 tokens of context.
- Higher Arena WebDev
- MiniMax M3.1 Flash1582 Elo against 1526 Elo
- Newer release
- MiniMax M3.1 FlashOct 2026 against Oct 2026
Data verified November 3, 2026 · Dataset 2026-11-03
Side by side
MiniMax M3.1 Flash and Muse Spark Open, line by line
Prices in effect today, compared at the same tier wherever both list one. The better value in each row is highlighted.
| Fact | MiniMax M3.1 Flash | Muse Spark Open |
|---|---|---|
| Price | ||
| Price per 1M input tokens | $0.15 | Not listed |
| Price per 1M output tokens | $0.60 | Not listed |
| Independent benchmarks | ||
| Arena WebDev | 1582 Elo (#26 of 45) (better) | 1526 Elo (#35 of 45) |
| FrontierMath Tiers 1-3 (v2) | Not measured | 69.8% (#24 of 42) |
| GPQA Diamond | Not measured | 88.6% (#34 of 41) |
| SimpleQA Verified | Not measured | 56.6% (#16 of 38) |
| Specs | ||
| Provider | MiniMax | Meta |
| Release date | October 19, 2026 | October 15, 2026 |
| Access | API and open weights | open weights |
| Context window | 1,000,000 tokens | 1,000,000 tokens |
| Max output | Not reported | Not reported |
| Input | text, image, video | text, image, video, audio |
| Output | text | text |
Charts
Against every current text model
Where MiniMax M3.1 Flash and Muse Spark Open land among the current models of their task, on every independent benchmark, on price and on context.
Where each stands
On Arena WebDev, MiniMax M3.1 Flash ranks #26 and Muse Spark Open #35 of 45 current models. Each bar is the share of current models it beats: on score, price and context.
Independent benchmarks
Arena WebDev
MiniMax M3.1 Flash:#26of 45
1,582 Elo · ahead of 41%
Muse Spark Open:#35of 45
1,526 Elo · ahead of 23%
FrontierMath Tiers 1-3 (v2)
MiniMax M3.1 Flash: Not scored
Muse Spark Open:#24of 42
69.8% · ahead of 44%
GPQA Diamond
MiniMax M3.1 Flash: Not scored
Muse Spark Open:#34of 41
88.6% · ahead of 18%
SimpleQA Verified
MiniMax M3.1 Flash: Not scored
Muse Spark Open:#16of 38
56.6% · ahead of 59%
Price and context
Price per 1M input tokens
MiniMax M3.1 Flash:#5of 52
$0.15 · cheaper than 90%
Muse Spark Open: Not listed
Price per 1M output tokens
MiniMax M3.1 Flash:#6of 52
$0.60 · cheaper than 90%
Muse Spark Open: Not listed
Context window
MiniMax M3.1 Flash:#26of 64
1M tokens · longer than 22%
Muse Spark Open:#26of 64
1M tokens · longer than 22%
Cost
What each costs
Example workloads at each provider's listed rates, then every other rate either one lists. The cheaper figure in each row is highlighted.
| Workload or rate | MiniMax M3.1 Flash | Muse Spark Open |
|---|---|---|
| Other listed rates | ||
| 1M input tokens, cached | $0.015 | Not listed |
Your own bill will differ. These figures use list prices and example workloads. The AI Model Cost Audit reviews your real usage, shortlists models that score as well for each task for less, and estimates the savings.
Launch price $100, regular price $150
Verdict
When to choose each
MiniMax M3.1 Flash
Where it leads
- Higher Arena WebDev score, 1582 Elo vs 1526 Elo
- A cached-input rate, $0.015 per 1M tokens
Best for
- Low-cost coding and agent loops
- Long-context multimodal work on a budget
Watch out for
- Thinking cannot be turned off; effort runs from low to max, and max is the default
- MiniMax gives two parameter counts (about 428B in the model card, 220B in the file metadata)
Muse Spark Open
Where it leads
- Accepts audio input
Best for
- Self-hosted coding and agent work on owned infrastructure
- Long-context multimodal analysis where data must stay in-house
Watch out for
- The Muse Model License limits commercial use; it is not an open-source licence
- Meta does not serve it on its own Model API, so hosted prices come from third parties
- Self-hosting needs at least 700GB of GPU memory in NVFP4 or 2TB in BF16
From benchmark to production
Building on MiniMax M3.1 Flash or Muse Spark Open?
Public scores rarely hold on your own data. We test both models on your real tasks, then build the agent or pipeline around the one that wins.
AI Model Cost AuditLaunch price $100, regular price $150What the audit includes
FAQ
MiniMax M3.1 Flash vs Muse Spark Open FAQ
Do MiniMax M3.1 Flash and Muse Spark Open discount cached input?
MiniMax M3.1 Flash lists a cached-input rate of $0.015 per 1M tokens, 90% below its standard input rate. No cached-input rate is listed for Muse Spark Open.
Which scores higher on independent benchmarks, MiniMax M3.1 Flash or Muse Spark Open?
MiniMax M3.1 Flash leads on Arena WebDev, 1582 Elo to 1526 Elo. Scores are independent evaluations, not either vendor's launch figures.
Which has the larger context window, MiniMax M3.1 Flash or Muse Spark Open?
Both accept 1,000,000 tokens of context.
How do MiniMax M3.1 Flash and Muse Spark Open differ in features?
MiniMax M3.1 Flash takes text, image and video input; Muse Spark Open takes text, image, video and audio.
Can I run MiniMax M3.1 Flash or Muse Spark Open myself?
MiniMax M3.1 Flash is released as open weights you can run yourself; Muse Spark Open is available through its provider's API only.
Further reading
More on MiniMax M3.1 Flash and Muse Spark Open
Related comparisons
- Beam 1.0 vs MiniMax M3.1 Flash
- Muse Spark 1.3 vs Muse Spark Open
- Claude Haiku 5.5 vs MiniMax M3.1 Flash
- Muse Spark Open vs Muse Wonder
- GLM-5.3-Flash vs MiniMax M3.1 Flash
- Beam 1.0 vs Muse Spark Open
- GPT-6 Luna vs MiniMax M3.1 Flash
- Muse Spark Open vs Nemotron 3.5 Super VL
- MiMo-V2.6-Flash vs MiniMax M3.1 Flash
- MiniMax M3.1 Flash vs Nemotron 3.5 Super VL
- GPT-5.6 Luna vs MiniMax M3.1 Flash
- Mercury 2.5 vs MiniMax M3.1 Flash
Production evaluation
Choosing between MiniMax M3.1 Flash and Muse Spark Open?
Public benchmarks narrow the shortlist. We run both on your own tasks, data and cost limits, then build the routing and review controls to run the winner in production.
- A head-to-head evaluation on your real workload
- Routing between models by cost and difficulty