Compare · ModelsLive · 2 picked · head to head
DeepSeek V3.2 Speciale vs MiniMax M2.7
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
MiniMax M2.7 wins on 3/3 benchmarks
MiniMax M2.7 wins 3 of 3 shared benchmarks. Leads in speed.
Category leads
speed·MiniMax M2.7
Hype vs Reality
Attention vs performance
DeepSeek V3.2 Speciale
#6 by perf·no signal
MiniMax M2.7
#120 by perf·#11 by attention
Best value
DeepSeek V3.2 Speciale
1.0x better value than MiniMax M2.7
DeepSeek V3.2 Speciale
97.8 pts/$
$0.80/M
MiniMax M2.7
96.2 pts/$
$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
3 benchmarks · 2 models
DeepSeek V3.2 SpecialeMiniMax M2.7
Artificial Analysis · Agentic Index
MiniMax M2.7 leads by +25.6
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 Speciale
0.0
MiniMax M2.7
25.6
Artificial Analysis · Coding Index
MiniMax M2.7 leads by +14.7
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 Speciale
37.9
MiniMax M2.7
52.6
Artificial Analysis · Quality Index
MiniMax M2.7 leads by +8.7
DeepSeek V3.2 Speciale
29.4
MiniMax M2.7
38.1
Full benchmark table
| Benchmark | DeepSeek V3.2 Speciale | 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?" | 0.0 | 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. | 37.9 | 52.6 |
Artificial Analysis · Quality Index | 29.4 | 38.1 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $0.40 | $1.20 | 164K tokens (~82 books) | $6.00 | |
| $0.21 | $0.84 | 205K tokens (~102 books) | $3.67 |
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