Compare · ModelsLive · 2 picked · head to head
DeepSeek V3.2 vs MiniMax M2.7
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
MiniMax M2.7 wins on 10/15 benchmarks
MiniMax M2.7 wins 10 of 15 shared benchmarks. Leads in arena · coding · reasoning.
Category leads
speed·DeepSeek V3.2arena·MiniMax M2.7coding·MiniMax M2.7reasoning·MiniMax M2.7language·MiniMax M2.7math·MiniMax M2.7knowledge·MiniMax M2.7general·DeepSeek V3.2
Hype vs Reality
Attention vs performance
DeepSeek V3.2
#142 by perf·no signal
MiniMax M2.7
#120 by perf·#11 by attention
Best value
DeepSeek V3.2
1.4x better value than MiniMax M2.7
DeepSeek V3.2
136.3 pts/$
$0.35/M
MiniMax M2.7
96.2 pts/$
$0.53/M
Vendor risk
Mixed exposure
One or more vendors flagged
DeepSeek
$3.4B·Tier 1
MiniMax
$4.0B·Tier 1
Head to head
15 benchmarks · 2 models
DeepSeek V3.2MiniMax M2.7
Artificial Analysis · Agentic Index
DeepSeek V3.2 leads by +27.3
Artificial Analysis Agentic Index · a composite score measuring how well a model performs in agentic workflows · multi-step tool use, planning, error recovery, and autonomous task completion. Aggregates results from multiple agentic benchmarks including SWE-bench, tool-use tests, and planning evaluations. The canonical single-number metric for "how good is this model as an agent?"
DeepSeek V3.2
52.9
MiniMax M2.7
25.6
Artificial Analysis · Coding Index
MiniMax M2.7 leads by +15.9
Artificial Analysis Coding Index · a composite score that aggregates performance across multiple coding benchmarks into a single index. Tracks code generation quality, debugging ability, multi-language competence, and real-world software engineering tasks. Used by Artificial Analysis to rank model coding capability in a normalized, comparable format. Useful for developers choosing between models for coding-heavy workloads.
DeepSeek V3.2
36.7
MiniMax M2.7
52.6
Artificial Analysis · Quality Index
DeepSeek V3.2 leads by +3.6
DeepSeek V3.2
41.7
MiniMax M2.7
38.1
Chatbot Arena Elo · Coding
MiniMax M2.7 leads by +72.1
DeepSeek V3.2
1324.8
MiniMax M2.7
1397.0
Chatbot Arena Elo · Overall
DeepSeek V3.2 leads by +9.8
DeepSeek V3.2
1424.8
MiniMax M2.7
1415.0
LiveBench · Agentic Coding
MiniMax M2.7 leads by +3.3
DeepSeek V3.2
46.7
MiniMax M2.7
50.0
LiveBench · Coding
DeepSeek V3.2 leads by +20.8
DeepSeek V3.2
75.7
MiniMax M2.7
54.9
LiveBench · Data Analysis
MiniMax M2.7 leads by +11.3
DeepSeek V3.2
45.0
MiniMax M2.7
56.3
LiveBench · If
MiniMax M2.7 leads by +38.1
DeepSeek V3.2
23.1
MiniMax M2.7
61.1
LiveBench · Language
MiniMax M2.7 leads by +2.5
DeepSeek V3.2
64.2
MiniMax M2.7
66.8
LiveBench · Mathematics
MiniMax M2.7 leads by +16.6
DeepSeek V3.2
64.0
MiniMax M2.7
80.5
LiveBench · Overall
MiniMax M2.7 leads by +11.7
DeepSeek V3.2
51.8
MiniMax M2.7
63.5
LiveBench · Reasoning
MiniMax M2.7 leads by +30.5
DeepSeek V3.2
44.3
MiniMax M2.7
74.8
Proofbench
DeepSeek V3.2 leads by +5.0
DeepSeek V3.2
8.0
MiniMax M2.7
3.0
Terminal Bench
MiniMax M2.7 leads by +5.5
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 M2.7
45.1
Full benchmark table
| Benchmark | DeepSeek V3.2 | MiniMax M2.7 |
|---|---|---|
Artificial Analysis · Agentic Index Artificial Analysis Agentic Index · a composite score measuring how well a model performs in agentic workflows · multi-step tool use, planning, error recovery, and autonomous task completion. Aggregates results from multiple agentic benchmarks including SWE-bench, tool-use tests, and planning evaluations. The canonical single-number metric for "how good is this model as an agent?" | 52.9 | 25.6 |
Artificial Analysis · Coding Index Artificial Analysis Coding Index · a composite score that aggregates performance across multiple coding benchmarks into a single index. Tracks code generation quality, debugging ability, multi-language competence, and real-world software engineering tasks. Used by Artificial Analysis to rank model coding capability in a normalized, comparable format. Useful for developers choosing between models for coding-heavy workloads. | 36.7 | 52.6 |
Artificial Analysis · Quality Index | 41.7 | 38.1 |
Chatbot Arena Elo · Coding | 1324.8 | 1397.0 |
Chatbot Arena Elo · Overall | 1424.8 | 1415.0 |
LiveBench · Agentic Coding | 46.7 | 50.0 |
LiveBench · Coding | 75.7 | 54.9 |
LiveBench · Data Analysis | 45.0 | 56.3 |
LiveBench · If | 23.1 | 61.1 |
LiveBench · Language | 64.2 | 66.8 |
LiveBench · Mathematics | 64.0 | 80.5 |
LiveBench · Overall | 51.8 | 63.5 |
LiveBench · Reasoning | 44.3 | 74.8 |
Proofbench | 8.0 | 3.0 |
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 | 45.1 |
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 | |
| $0.21 | $0.84 | 205K tokens (~102 books) | $3.67 |