MiniMax, released Mar 18, 2026

MiniMax-M2.7price, context, benchmarks and release details

Provisional: not enough results to rank yet Open weights 88% confidence 88 percent, High confidence, 2 of 3 expected sources in
57.9
SI Score
#70 of 142 ranked models
Input, per 1M tokens
$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, per 1M tokens
$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
Mar 18, 2026models.devPublished source fact Retrieved Oct 9, 2026 · MIT
Open source ↗

How the score breaks down

Coding (weight 40 percent) 62.4
Math (weight 15 percent) —
Preference (weight 15 percent) 73.6
Reasoning (weight 30 percent) —

Weights: reasoning 30%, math 15%, coding 40%, preference 15%. Results use fixed 0–100 scales before averaging, and thin evidence is pulled toward 50. Method si-v3-retained-evidence-2, computed Oct 9, 2026, 06:15 UTC.

Around it on the leaderboard

  1. 68 Gemini 3 Pro Preview 58.6
  2. 69 GPT-5.5 Instant 58.3
  3. 70 MiniMax-M2.7 57.9
  4. 71 Fugu Ultra 57.9
  5. 72 MiniMax-M2.5 57.8

Full leaderboard

Benchmark results

4 benchmarks, 4 results

Each row shows the best published result. Where a model was tested at several settings, such as reasoning effort, open the row to see each one. Hover or tap a value for its source.

SWE-bench Procoding 56.2%Official model cards via models.devLab-reported; metric resolve rate; transcribed by MIT models.dev catalog; not independently evaluated [variant] Claude CodePublished Jun 1, 2026 Retrieved Oct 9, 2026 · factual citation; MIT transcription
Open source ↗
56.2
SWE-bench Verifiedcoding 79.9%Official model cards via models.devLab-reported; metric resolved; transcribed by MIT models.dev catalog; not independently evaluated [variant] Claude CodePublished Jun 1, 2026 Retrieved Oct 9, 2026 · factual citation; MIT transcription
Open source ↗
79.9
Terminal-Bench 2.1coding 51.1%Official model cards via models.devLab-reported; metric success rate; transcribed by MIT models.dev catalog; not independently evaluated [variant] 2.1Published Jun 1, 2026 Retrieved Oct 9, 2026 · factual citation; MIT transcription
Open source ↗
51.1
LMArena Textpreference 1404.6 eloLMArena / ArenaPublished source fact [variant] text / overallPublished Oct 8, 2026 Retrieved Oct 9, 2026 · CC-BY-4.0
Open source ↗
73.6

Normalization uses a fixed 0–100 scale for each unit, independent of other models. Compare evaluation conditions before reading a small gap as decisive. “Lab-reported” marks the provider's own published figure.

Details and sources

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 ↗
First seen by SuperIndex
Oct 8, 2026
Coverage
70% of expected source weight

Reported (2)

  • Official model cards via models.devOct 8, 2026
  • LMArena / ArenaOct 8, 2026

Awaiting (1)

  • LiveBench30% of weight

Confidence rises as pending sources publish. Some sources never cover some models, so confidence reaches 100% at 80% of expected weight.

What changed

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Alerts on this device

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