SI Index Data as of Oct 9, 2026

MiniMax-M2.1: Price, Context, Benchmarks, and Release Details

minimax provisional listing open weights · first seen Oct 8, 2026 · score computed Oct 9, 2026, 01:50 UTC
50.6
SI Score (method si-v3-retained-evidence-2)
13% confidence 13 percent, Low confidence — 1 of 3 expected sources in
Coverage 10% of expected source weight · 100% confidence at 80% coverage

Pillar breakdown

Coding (weight 40 percent) 55.4
Math (weight 15 percent) —
Preference (weight 15 percent) —
Reasoning (weight 30 percent) —

Pillar weights: reasoning 30% · math 15% · coding 40% · preference 15%. Benchmark results use fixed 0–100 scales before averaging; incomplete evidence is shrunk toward 50.

Facts

Input price / 1M
$0.30MiniMax API pricingOfficial MiniMax global on-demand API; lowest short-context tier and displayed permanent promotional discount; excludes high-context, fast tier and subscriptions Retrieved Oct 9, 2026 · factual citation
Open source ↗
Output price / 1M
$1.20MiniMax API pricingOfficial MiniMax global on-demand API; lowest short-context tier and displayed permanent promotional discount; excludes high-context, fast tier and subscriptions Retrieved Oct 9, 2026 · factual citation
Open source ↗
Context window
205Kmodels.devPublished source fact Retrieved Oct 9, 2026 · MIT
Open source ↗
Max output
131Kmodels.devPublished source fact Retrieved Oct 9, 2026 · MIT
Open source ↗
Released
Dec 23, 2025models.devPublished source fact Retrieved Oct 9, 2026 · MIT
Open source ↗
Open weights
Yesmodels.devPublished source fact Retrieved Oct 9, 2026 · MIT
Open source ↗
License
not yet reported
Input modalities
textmodels.devPublished source fact Retrieved Oct 9, 2026 · MIT
Open source ↗

Benchmark results

2 results
Benchmark Raw result Normalized (0–100) Pillar
swe bench pro public 36.8%Official model cards via models.devLab-reported; metric resolve rate; transcribed by MIT models.dev catalog; not independently evaluated [variant] public Retrieved Oct 9, 2026 · factual citation; MIT transcription
Open source ↗ 		36.8 coding
swe bench verified 74%Official model cards via models.devLab-reported; metric resolved; transcribed by MIT models.dev catalog; not independently evaluated [variant] Retrieved Oct 9, 2026 · factual citation; MIT transcription
Open source ↗ 		74.0 coding

Normalization uses fixed absolute 0–100 scales for each unit, independently of other models that have a result on each benchmark. Coverage and evidence breadth still affect the composite; compare the evaluation conditions before reading a small score gap as decisive. “lab-reported” marks the provider's own published figure.

Sources: in and pending

in Reported (1)

  • Official model cards via models.dev · arrived Oct 8, 2026

pending Awaiting (2)

  • LiveBench · carries 30% of expected weight
  • LMArena / Arena · carries 60% of expected weight

The confidence % rises as pending sources publish. Some sources never cover some models — that is why 100% confidence arrives at 80% of expected weight, not at full coverage.