Compare · ModelsLive · 2 picked · head to head
MiniMax M3 vs o3
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
MiniMax M3 wins on 12/19 benchmarks
MiniMax M3 wins 12 of 19 shared benchmarks. Leads in speed · agentic.
Category leads
speed·MiniMax M3agentic·MiniMax M3knowledge·o3general·o3math·o3reasoning·o3
Hype vs Reality
Attention vs performance
MiniMax M3
#124 by perf·#11 by attention
o3
#111 by perf·no signal
Best value
MiniMax M3
6.5x better value than o3
MiniMax M3
66.5 pts/$
$0.75/M
o3
10.2 pts/$
$5.00/M
Vendor risk
Mixed exposure
One or more vendors flagged
MiniMax
$4.0B·Tier 1
OpenAI
$840.0B·Tier 1
Head to head
19 benchmarks · 2 models
MiniMax M3o3
Artificial Analysis · Agentic Index
o3 leads by +0.7
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?"
MiniMax M3
35.4
o3
36.1
Artificial Analysis · Coding Index
MiniMax M3 leads by +20.2
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.
MiniMax M3
58.6
o3
38.4
Artificial Analysis · CritPt
MiniMax M3 leads by +2.6
MiniMax M3
3.7
o3
1.1
Artificial Analysis · GPQA Diamond
MiniMax M3 leads by +10.2
MiniMax M3
92.9
o3
82.7
Artificial Analysis · Humanity's Last Exam
MiniMax M3 leads by +18.9
MiniMax M3
39.0
o3
20.1
Artificial Analysis · IFBench
MiniMax M3 leads by +11.5
MiniMax M3
82.9
o3
71.4
Artificial Analysis · Long Context Reasoning
MiniMax M3 leads by +8.3
MiniMax M3
83.0
o3
74.7
Artificial Analysis · MMMU Pro
MiniMax M3 leads by +8.5
MiniMax M3
78.6
o3
70.1
Artificial Analysis · Quality Index
MiniMax M3 leads by +9.0
MiniMax M3
29.2
o3
20.2
Artificial Analysis · tau2-Bench Telecom
MiniMax M3 leads by +8.2
MiniMax M3
88.9
o3
80.7
Artificial Analysis · Terminal-Bench Hard
MiniMax M3 leads by +5.3
MiniMax M3
42.4
o3
37.1
APEX-Agents
MiniMax M3 leads by +20.5
APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments.
MiniMax M3
37.7
o3
17.2
Chess Puzzles
o3 leads by +25.3
Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities.
MiniMax M3
9.5
o3
34.8
Dtbench
o3 leads by +9.8
MiniMax M3
64.9
o3
74.7
GPQA diamond
MiniMax M3 leads by +12.1
Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs.
MiniMax M3
87.9
o3
75.8
Lmca
o3 leads by +7.1
MiniMax M3
39.6
o3
46.7
Mystery Game Puzzles
o3 leads by +21.8
MiniMax M3
0.0
o3
21.8
OTIS Mock AIME 2024-2025
o3 leads by +13.4
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
MiniMax M3
71.1
o3
84.4
SimpleBench
o3 leads by +8.8
SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking.
MiniMax M3
35.0
o3
43.7
Full benchmark table
| Benchmark | MiniMax M3 | o3 |
|---|---|---|
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?" | 35.4 | 36.1 |
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. | 58.6 | 38.4 |
Artificial Analysis · CritPt | 3.7 | 1.1 |
Artificial Analysis · GPQA Diamond | 92.9 | 82.7 |
Artificial Analysis · Humanity's Last Exam | 39.0 | 20.1 |
Artificial Analysis · IFBench | 82.9 | 71.4 |
Artificial Analysis · Long Context Reasoning | 83.0 | 74.7 |
Artificial Analysis · MMMU Pro | 78.6 | 70.1 |
Artificial Analysis · Quality Index | 29.2 | 20.2 |
Artificial Analysis · tau2-Bench Telecom | 88.9 | 80.7 |
Artificial Analysis · Terminal-Bench Hard | 42.4 | 37.1 |
APEX-Agents APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments. | 37.7 | 17.2 |
Chess Puzzles Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities. | 9.5 | 34.8 |
Dtbench | 64.9 | 74.7 |
GPQA diamond Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs. | 87.9 | 75.8 |
Lmca | 39.6 | 46.7 |
Mystery Game Puzzles | 0.0 | 21.8 |
OTIS Mock AIME 2024-2025 OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills. | 71.1 | 84.4 |
SimpleBench SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking. | 35.0 | 43.7 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $0.30 | $1.20 | 1.0M tokens (~524 books) | $5.25 | |
| $2.00 | $8.00 | 200K tokens (~100 books) | $35.00 |