MiniMax M3.1 Flash Open vs PEPerceptron Mk1.5 Paid
MiniMax M3.1 Flash vs Perceptron Mk1.5 compared — price per token, context window, multimodality, openness and which to choose. Can the open-source model replace the paid one? Full 2026 breakdown.
Perceptron Mk1.5 — full profile ›
Prices & specs refreshed from live data · olud.ai
Open-model prices = cheapest provider via OpenRouter; official maker rates may be higher.
$0.6 /M outputNo per-token fees if you self-host
$1.5 /M outputManaged API (no infra to run)
MiniMax M3.1 Flash vs Perceptron Mk1.5 specs
| Spec | MiniMax M3.1 Flash | Perceptron Mk1.5 | Winner |
|---|---|---|---|
| Maker | MiniMax | Perceptron | – |
| Type | Open-weight | Proprietary | |
| Context window | 1M tokens | 37K tokens | |
| Input price | $0.15/M · free self-host | $0.15/M | = Tie |
| Output price | $0.6/M · free self-host | $1.5/M | |
| Vision / multimodal | Yes | Yes | = Tie |
| Tool / function calling | Yes | Yes | = Tie |
| Self-hostable | Yes | No (API only) | |
| License | MIT | Proprietary |
Price gap & when to choose each
MiniMax M3.1 Flash is ~2.5× cheaper than Perceptron Mk1.5 on output tokens ($0.6 vs $1.5 per M tokens).
- You want to self-host or run on your cloud
- You need the longer 1M context window
- You prioritize data privacy & control
- You want the lowest operating costs
- You are building open or reproducible AI
- You want frontier performance through a managed API
- You value reliability & ecosystem
- You don't want to manage infrastructure
Feature comparison
| Capability | MiniMax M3.1 Flash | Perceptron Mk1.5 |
|---|---|---|
| Open weights (downloadable) | ✓ | ✗ |
| Self-hostable | ✓ | ✗ |
| Runs fully offline | ✓ | ✗ |
| Vision / multimodal | ✓ | ✓ |
| Tool / function calling | ✓ | ✓ |
| 1M+ context window | ✓ | ✗ |
Benchmarks: MiniMax M3.1 Flash vs Perceptron Mk1.5
Independent benchmark scores measured by Artificial Analysis. Higher is better (except latency).
Benchmark data by Artificial Analysis.
How MiniMax M3.1 Flash and Perceptron Mk1.5 score
| Criterion | MiniMax M3.1 Flash | Perceptron Mk1.5 |
|---|---|---|
| Cost-efficiency | 5.0 | 4.5 |
| Context window | 5.0 | 2.5 |
| Openness | 5.0 | 1.5 |
| Self-hosting | 5.0 | 1.0 |
| Multimodality | 5.0 | 5.0 |
Scores come from live data — output price (cost), context length, open vs closed weights (openness & self-hosting) and vision/tool support (multimodality). Raw task quality isn't scored here; it depends on your benchmark — see the verdict.
What each model is
MiniMax M3.1 Flash Open
MiniMax-M3.1-Flash is a multimodal model from MiniMax. Compared with M3, it delivers significant improvements across multiple benchmarks, particularly in complex agentic tasks, coding, and cybersecurity. It is built for complex,...
Perceptron Mk1.5 Paid
Perceptron Mk1.5 is Perceptron's embodied reasoning model for physical agents. It accepts text, image, video, and audio input, and answers with text plus optional structured annotations: points, boxes, polygons, tracks,...
Other models in these families
These variants are tracked but not compared here — one page per family keeps the comparison readable.
Frequently asked questions
Is MiniMax M3.1 Flash as good as Perceptron Mk1.5?
MiniMax M3.1 Flash is open-weight and competitive on many tasks, but Perceptron Mk1.5 may still lead on the hardest reasoning and agentic work. The gap keeps narrowing — benchmark both on your actual use case before deciding.
Can I run MiniMax M3.1 Flash locally?
Yes. MiniMax M3.1 Flash has open weights, so you can self-host it on your own GPUs or run it via a low-cost API. Perceptron Mk1.5 is API-only and cannot be self-hosted.
How much cheaper is MiniMax M3.1 Flash?
MiniMax M3.1 Flash costs $0.6/M output vs $1.5/M for Perceptron Mk1.5 — roughly 3x cheaper via API, and free if you self-host.
MiniMax M3.1 Flash vs Perceptron Mk1.5 — which should I pick in 2026?
Choose MiniMax M3.1 Flash if you want to self-host, keep your data private and skip per-token fees — it's open-weight and runs on your own hardware. Choose Perceptron Mk1.5 if you want frontier capability through a managed API with zero infrastructure to run.
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