MiniMax, released Feb 12, 2026
MiniMax-M2.5price, context, benchmarks and release details
- 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
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
- 70 MiniMax-M2.7 57.9
- 71 Fugu Ultra 57.9
- 72 MiniMax-M2.5 57.8
- 73 Grok 4.20 (Reasoning) 57.7
- 74 MiMo-V2.6-Pro 57.4
Benchmark results
6 benchmarks, 7 resultsEach 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.
Open source ↗ 59.1
Open source ↗ 63.7
Open source ↗ 5.4
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 ↗
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.