Compare · ModelsLive · 2 picked · head to head
MiniMax M2.5 vs Grok 4
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
Grok 4 wins on 3/4 benchmarks
Grok 4 wins 3 of 4 shared benchmarks. Leads in agentic · reasoning.
Category leads
agentic·Grok 4reasoning·Grok 4coding·MiniMax M2.5
Hype vs Reality
Attention vs performance
MiniMax M2.5
#71 by perf·no signal
Grok 4
#73 by perf·no signal
Best value
MiniMax M2.5
13.9x better value than Grok 4
MiniMax M2.5
84.8 pts/$
$0.65/M
Grok 4
6.1 pts/$
$9.00/M
Vendor risk
Mixed exposure
One or more vendors flagged
MiniMax
$4.0B·Tier 1
xAI
$250.0B·Tier 1
Head to head
4 benchmarks · 2 models
MiniMax M2.5Grok 4
APEX-Agents
Grok 4 leads by +9.0
APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments.
MiniMax M2.5
6.2
Grok 4
15.2
ARC-AGI
Grok 4 leads by +3.0
ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization.
MiniMax M2.5
63.7
Grok 4
66.7
ARC-AGI-2
Grok 4 leads by +11.1
ARC-AGI-2 · the second iteration of the Abstraction and Reasoning Corpus, testing novel pattern recognition and abstract reasoning without prior training data.
MiniMax M2.5
4.9
Grok 4
16.0
Terminal Bench
MiniMax M2.5 leads by +15.0
Terminal-Bench 2.0 · evaluates AI agents on real terminal-based coding tasks · writing scripts, debugging, running tests, and managing projects entirely through command-line interaction. Tests both code quality and terminal fluency. Claude Opus 4.7 scores 69.4%, demonstrating significant agentic terminal competence.
MiniMax M2.5
42.2
Grok 4
27.2
Full benchmark table
| Benchmark | MiniMax M2.5 | Grok 4 |
|---|---|---|
APEX-Agents APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments. | 6.2 | 15.2 |
ARC-AGI ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization. | 63.7 | 66.7 |
ARC-AGI-2 ARC-AGI-2 · the second iteration of the Abstraction and Reasoning Corpus, testing novel pattern recognition and abstract reasoning without prior training data. | 4.9 | 16.0 |
Terminal Bench Terminal-Bench 2.0 · evaluates AI agents on real terminal-based coding tasks · writing scripts, debugging, running tests, and managing projects entirely through command-line interaction. Tests both code quality and terminal fluency. Claude Opus 4.7 scores 69.4%, demonstrating significant agentic terminal competence. | 42.2 | 27.2 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $0.15 | $1.15 | 197K tokens (~98 books) | $4.00 | |
| $3.00 | $15.00 | 256K tokens (~128 books) | $60.00 |