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  5. MiniMax M1vsTernary Bonsai 2 27B
Updated Sep 2026

MiniMax M1vsTernary Bonsai 2 27B

Ternary Bonsai 2 27B is cheaper than MiniMax M1 at $0.075/M vs $0.3/M input tokens.

MiniMax M1 and Ternary Bonsai 2 27B compared across 3 shared prompts
SpecMiniMax M1Ternary Bonsai 2 27B
Input price$0.3/M tokens$0.075/M tokens
Output price$1.65/M tokens$0.5/M tokens
Context window1.0M tokens262K tokens
WeightsOpen—
Free API (OpenRouter)NoNo
ReleasedJun 2025Sep 2026
Side by side

Same prompt, both answers

Opening lines from 2 of the 3 prompts both answered. Formatting stripped, words untouched.

Simple 7-Ingredient Recipe

Create a simple recipe for a delicious dish (sweet or savory) using only 7 common pantry ingredients. Include a creative name for the dish…

MiniMax M1672 words
Here's a delicious, fast, and flexible recipe using 7 common pantry staples: Dish Name: Sun-Drenched Chickpea Scramble (Inspired by chickpea "scrambles" or shakshuka variations, using pantry essentials) Flavor Profile: Savory, earthy, bright, slightly tangy, and warmly spiced.
Read the full answer
Ternary Bonsai 2 27B358 words
🍫 Velvet Hour Skillet Cakes A one-pan, one-pan-pour, one-lick-the-spoon kind of dessert. Fluffy, deeply chocolate, and done before your coffee goes cold. Serves: 2–3 Prep: 10 min Cook: 12–15 min Total: ~25 min The 7 Ingredients # Ingredient Amount 1 All-purpose flour ¾ cup 2 Granulated sugar ⅓ cup 3 Unsweetened cocoa…
Read the full answer

Explain Like I'm a Specific Expert

Explain how a large language model (like GPT or Claude) learns and generates text to each of these three audiences: 1. **An experienced…

MiniMax M1763 words
For an Experienced Software Engineer Imagine you’re designing a distributed system where every API request is a snippet of text, and your system’s job is to predict the next word in the sequence. That’s essentially what a large language model (LLM) does.
Read the full answer
Ternary Bonsai 2 27B1,636 words
For the Distributed-Systems Engineer Think of the model as a single, enormous state object—roughly 150 billion floating-point parameters—that has been trained offline over months on a cluster of thousands of GPUs.
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Our Verdict
MiniMax M1
MiniMax M1
Ternary Bonsai 2 27B
Ternary Bonsai 2 27BRunner-up

Not enough votes to call it. On the specs, MiniMax M1 has the edge: bigger model tier, bigger context window.

Ternary Bonsai 2 27B costs 3.3x less per token.

Too close to call
API pricing

Cost per 1M tokens

MiniMax M1
Input
$0.30
Output
$1.65
Ternary Bonsai 2 27B
Input
$0.07
4.0× cheaper
Output
$0.50
3.3× cheaper

Ternary Bonsai 2 27B is cheaper on both: 4.0× input, 3.3× output.

Where to run it

3 hosts, cheapest first

MiniMax M12 hosts
HostInOutContextUptime
MiniMax$0.40 in·$2.20 out·1M·100% upNNovitabf16$0.55 in·$2.20 out·1M·100% up
Ternary Bonsai 2 27B1 host
HostInOutContextUptime
DDarkbloomint4$0.07 in·$0.50 out·262k·100% up

Per million tokens. Prices and uptime via OpenRouter, checked 22 Sep 2026.

Research

What we learned reading every model

FAQ

Common questions

MiniMax M1 is developed by MiniMax while Ternary Bonsai 2 27B is developed by PrismML. MiniMax M1 has a 1.0M token context window vs Ternary Bonsai 2 27B's 262K. You can compare their actual outputs across 3 challenges on Rival to see how they differ in practice.

It depends on your use case. MiniMax M1 and Ternary Bonsai 2 27B each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 3 challenges so you can judge which fits your needs best.

MiniMax M1 costs $0.3/M input tokens and Ternary Bonsai 2 27B costs $0.075/M input tokens. Ternary Bonsai 2 27B is $0.22/M cheaper per input. Check their side-by-side outputs on Rival to see if the price difference is justified by quality.

This page shows a side-by-side comparison of MiniMax M1 and Ternary Bonsai 2 27B across shared challenges. You can vote on which model produced the better output in a blind duel. Browsing and voting are free. No account is needed to look; signing in only saves your votes and likes.

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