MiniMaxproprietary
Released: 2026-06-25MiniMax M3
MiniMax frontier model with MiniMax Sparse Attention (MSA), 1M context, and top SWE-Bench Pro agentic completion.
Intelligence Index
89.5/ 100
Calibrated multi-domain compositeThroughput Speed
88tok/sec
Output streaming throughputTime To First Token
340ms
Initial chunk server latencyLiveBench Contam-Free
76.8%
September 2026 suite scoreSourced Benchmark Evaluations (2)
Verified performance across authoritative benchmarks with provenance tracking.| Benchmark | Status | Score | Trust | Date | Source Type | Provenance |
|---|---|---|---|---|---|---|
| ARC-AGI-2 | Nearing Saturation | 68.6% | 75 | 2026-07-23 | independent | Source ↗ |
| SWE-bench Verified | Saturated | 60.8% | 25 | 2026-07-04 | independent | Source ↗ |
OpenAI Compatible API ExecutionAPI Ready
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="minimax-m3",
messages=[{"role": "user", "content": "Evaluate multi-step logic problem"}],
temperature=0.2
)
print(response.choices[0].message.content)Evaluation & Sourcing Notes
Scores listed for MiniMax M3 represent verified evaluations extracted from official research papers, independent evaluation suites (HELM, LMSYS, OpenCompass, LiveBench), and verified audit reports.
All benchmarks marked as saturated or deprecated reflect historical performance where the benchmark no longer provides active discriminative power.