Compare · ModelsLive · 2 picked · head to head
DeepSeek V3.2 vs MiniMax M2.5
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
DeepSeek V3.2 wins on 12/23 benchmarks
DeepSeek V3.2 wins 12 of 23 shared benchmarks. Leads in agentic · general · knowledge.
Category leads
agentic·DeepSeek V3.2reasoning·MiniMax M2.5arena·MiniMax M2.5general·DeepSeek V3.2coding·MiniMax M2.5language·MiniMax M2.5math·MiniMax M2.5knowledge·DeepSeek V3.2
Hype vs Reality
Attention vs performance
DeepSeek V3.2
#142 by perf·no signal
MiniMax M2.5
#131 by perf·#11 by attention
Best value
DeepSeek V3.2
1.9x better value than MiniMax M2.5
DeepSeek V3.2
136.3 pts/$
$0.35/M
MiniMax M2.5
72.0 pts/$
$0.68/M
Vendor risk
Mixed exposure
One or more vendors flagged
DeepSeek
$3.4B·Tier 1
MiniMax
$4.0B·Tier 1
Head to head
23 benchmarks · 2 models
DeepSeek V3.2MiniMax M2.5
APEX-Agents
DeepSeek V3.2 leads by +0.8
APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments.
DeepSeek V3.2
7.0
MiniMax M2.5
6.2
ARC-AGI
MiniMax M2.5 leads by +6.7
ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization.
DeepSeek V3.2
57.0
MiniMax M2.5
63.7
ARC-AGI-2
MiniMax M2.5 leads by +0.8
ARC-AGI-2 · the second iteration of the Abstraction and Reasoning Corpus, testing novel pattern recognition and abstract reasoning without prior training data.
DeepSeek V3.2
4.0
MiniMax M2.5
4.9
Chatbot Arena Elo · Coding
MiniMax M2.5 leads by +61.4
DeepSeek V3.2
1324.8
MiniMax M2.5
1386.3
Chatbot Arena Elo · Overall
DeepSeek V3.2 leads by +33.9
DeepSeek V3.2
1424.8
MiniMax M2.5
1390.8
Cl Bench
DeepSeek V3.2 leads by +1.0
DeepSeek V3.2
12.4
MiniMax M2.5
11.4
Cl Bench Life
DeepSeek V3.2 leads by +1.1
DeepSeek V3.2
7.4
MiniMax M2.5
6.3
LiveBench · Agentic Coding
MiniMax M2.5 leads by +5.0
DeepSeek V3.2
46.7
MiniMax M2.5
51.7
LiveBench · Coding
DeepSeek V3.2 leads by +5.0
DeepSeek V3.2
75.7
MiniMax M2.5
70.7
LiveBench · Data Analysis
MiniMax M2.5 leads by +4.6
DeepSeek V3.2
45.0
MiniMax M2.5
49.6
LiveBench · If
MiniMax M2.5 leads by +34.2
DeepSeek V3.2
23.1
MiniMax M2.5
57.2
LiveBench · Language
DeepSeek V3.2 leads by +9.1
DeepSeek V3.2
64.2
MiniMax M2.5
55.1
LiveBench · Mathematics
MiniMax M2.5 leads by +13.5
DeepSeek V3.2
64.0
MiniMax M2.5
77.4
LiveBench · Overall
MiniMax M2.5 leads by +8.3
DeepSeek V3.2
51.8
MiniMax M2.5
60.1
LiveBench · Reasoning
MiniMax M2.5 leads by +15.0
DeepSeek V3.2
44.3
MiniMax M2.5
59.3
OpenCompass · AIME2025
DeepSeek V3.2 leads by +6.8
DeepSeek V3.2
93.0
MiniMax M2.5
86.2
OpenCompass · GPQA-Diamond
DeepSeek V3.2
84.6
MiniMax M2.5
84.6
OpenCompass · HLE
DeepSeek V3.2 leads by +1.0
DeepSeek V3.2
23.2
MiniMax M2.5
22.2
OpenCompass · IFEval
MiniMax M2.5 leads by +1.4
DeepSeek V3.2
89.7
MiniMax M2.5
91.1
OpenCompass · LiveCodeBenchV6
DeepSeek V3.2 leads by +1.8
DeepSeek V3.2
75.4
MiniMax M2.5
73.6
OpenCompass · MMLU-Pro
DeepSeek V3.2 leads by +4.1
DeepSeek V3.2
85.8
MiniMax M2.5
81.7
Proofbench
DeepSeek V3.2 leads by +4.0
DeepSeek V3.2
8.0
MiniMax M2.5
4.0
Terminal Bench
MiniMax M2.5 leads by +3.2
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.
DeepSeek V3.2
39.5
MiniMax M2.5
42.7
Full benchmark table
| Benchmark | DeepSeek V3.2 | MiniMax M2.5 |
|---|---|---|
APEX-Agents APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments. | 7.0 | 6.2 |
ARC-AGI ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization. | 57.0 | 63.7 |
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. | 4.0 | 4.9 |
Chatbot Arena Elo · Coding | 1324.8 | 1386.3 |
Chatbot Arena Elo · Overall | 1424.8 | 1390.8 |
Cl Bench | 12.4 | 11.4 |
Cl Bench Life | 7.4 | 6.3 |
LiveBench · Agentic Coding | 46.7 | 51.7 |
LiveBench · Coding | 75.7 | 70.7 |
LiveBench · Data Analysis | 45.0 | 49.6 |
LiveBench · If | 23.1 | 57.2 |
LiveBench · Language | 64.2 | 55.1 |
LiveBench · Mathematics | 64.0 | 77.4 |
LiveBench · Overall | 51.8 | 60.1 |
LiveBench · Reasoning | 44.3 | 59.3 |
OpenCompass · AIME2025 | 93.0 | 86.2 |
OpenCompass · GPQA-Diamond | 84.6 | 84.6 |
OpenCompass · HLE | 23.2 | 22.2 |
OpenCompass · IFEval | 89.7 | 91.1 |
OpenCompass · LiveCodeBenchV6 | 75.4 | 73.6 |
OpenCompass · MMLU-Pro | 85.8 | 81.7 |
Proofbench | 8.0 | 4.0 |
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. | 39.5 | 42.7 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $0.28 | $0.42 | 164K tokens (~82 books) | $3.15 | |
| $0.27 | $1.08 | 205K tokens (~102 books) | $4.72 |
People also compared