Standard Compute
Model comparison 2026

DeepSeek R1 (0528) vs MiniMax M2.1

DeepSeek R1 (0528)
DeepSeek · Balanced · Open weights
$0.99/1M · 164K ctx
MiniMax M2.1
MiniMax · Balanced · Open weights
$0.57/1M · 205K ctx

Community vote

Pick a winner in each dimension. You can change your vote any time.

Output Quality

Correctness and depth of what it produces

Agentic Ability

Tool calls, instruction-following, and multi-step tasks

Speed

Tokens per second and time-to-first-token

Value for Money

How much capability you get per dollar

Reliability

Consistent results: fewer refusals, loops and format breaks

Specs and pricing side by side

Spec
DeepSeek R1
MiniMax M2.1
Maker
DeepSeek
MiniMax
Blended price / 1M
$0.99
$0.57
Input / output
$0.50 in · $2.15 out / 1M tokens
$0.30 in · $1.20 out / 1M tokens
Context window
164K
205K
Open weights
Yes
Yes
Tool use
Yes
Yes
Reasoning
Yes
Yes
Output Quality
4.0
5.1
Agentic Ability
4.4
4.7
Speed
5.1
6.9
Value for Money
4.7
7.2
Reliability
8.4
8.3

Pricing and capabilities synced from the OpenRouter catalogue. Scores are editorial (0–10).

Verdict: DeepSeek R1 or MiniMax M2.1?

Updated 2026-10-17

Choose DeepSeek R1 (0528) if you want research and comparisons that need the original R1 reasoning model. Choose MiniMax M2.1 if you want short, tool-heavy agent steps on a tight budget.

In our editorial scoring, MiniMax M2.1 leads in 4 of five dimensions (output quality, agentic ability, speed and value for money), while DeepSeek R1 (0528) leads in 1. On price, DeepSeek R1 (0528) runs about $0.99 per 1M tokens (blended) and is open-weight; MiniMax M2.1 is about $0.57 and open-weight.

Where DeepSeek R1 falls short
  • Slow (about 28 output tokens/s, per Artificial Analysis)
  • 164K context, smaller than 27.9% of the coding-agent requests we measured
Full DeepSeek R1 breakdown →
Where MiniMax M2.1 falls short
  • 204K context, smaller than 19.7% of the coding-agent requests we measured
  • Superseded by MiniMax M3
Full MiniMax M2.1 breakdown →

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