MiniMax, released Feb 12, 2026

MiniMax-M2.5price, context, benchmarks and release details

Provisional: not enough results to rank yet Open weights 100% confidence 100 percent, Full confidence, 4 of 5 expected sources in
57.8
SI Score
#72 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
Feb 12, 2026models.devPublished source fact Retrieved Oct 9, 2026 · MIT
Open source ↗

How the score breaks down

Coding (weight 40 percent) 75.8
Math (weight 15 percent) —
Preference (weight 15 percent) 68.9
Reasoning (weight 30 percent) 33.3

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, 05:13 UTC.

Around it on the leaderboard

  1. 70 MiniMax-M2.7 57.9
  2. 71 Fugu Ultra 57.9
  3. 72 MiniMax-M2.5 57.8
  4. 73 Grok 4.20 (Reasoning) 57.7
  5. 74 MiMo-V2.6-Pro 57.4

Full leaderboard

Benchmark results

6 benchmarks, 7 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.

ARC-AGI-1 (public eval)reasoning 59.1%ARC PrizeARC Prize steward-published result; exact edition/subset; publication time is HTTP Last-Modified of the aggregate export, not the evaluation date. [variant] minimax-m2.5Published Oct 6, 2026 Retrieved Oct 9, 2026 · factual citation
Open source ↗
59.1
ARC-AGI-1 (semi-private)reasoning 63.7%ARC PrizeARC Prize steward-published result; exact edition/subset; publication time is HTTP Last-Modified of the aggregate export, not the evaluation date. [variant] minimax-m2.5Published Oct 6, 2026 Retrieved Oct 9, 2026 · factual citation
Open source ↗
63.7
ARC-AGI-2 (public eval)reasoning 5.4%ARC PrizeARC Prize steward-published result; exact edition/subset; publication time is HTTP Last-Modified of the aggregate export, not the evaluation date. [variant] minimax-m2.5Published Oct 6, 2026 Retrieved Oct 9, 2026 · factual citation
Open source ↗
5.4
ARC-AGI-2 (semi-private)reasoning 4.9%ARC PrizeARC Prize steward-published result; exact edition/subset; publication time is HTTP Last-Modified of the aggregate export, not the evaluation date. [variant] minimax-m2.5Published Oct 6, 2026 Retrieved Oct 9, 2026 · factual citation
Open source ↗
4.9
SWE-bench Verifiedcoding 75.8%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 ↗
75.8 2 settings
  • Setting 1 75.8%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 ↗
  • Setting 2 75.8%SWE-bench VerifiedSWE-bench published model plus agent result; harness retained, not a base model evaluation [variant] mini-SWE-agent; high; 2.0.0Published Feb 17, 2026 Retrieved Oct 9, 2026 · factual citation
    Open source ↗
About SWE-bench Verified
LMArena Textpreference 1359.4 eloLMArena / ArenaPublished source fact [variant] text / overallPublished Oct 8, 2026 Retrieved Oct 9, 2026 · CC-BY-4.0
Open source ↗
68.9

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
81% of expected source weight

Reported (4)

  • ARC PrizeOct 8, 2026
  • Official model cards via models.devOct 8, 2026
  • LMArena / ArenaOct 8, 2026
  • SWE-bench VerifiedOct 8, 2026

Awaiting (1)

  • LiveBench19% 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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