Sakana AI, released Jun 15, 2026

Fugu Ultraprice, context, benchmarks and release details

Provisional: not enough results to rank yet 100% confidence 100 percent, Full confidence, 1 of 1 expected sources in
57.9
SI Score
#71 of 142 ranked models
Input, per 1M tokens
not yet reported
Output, per 1M tokens
not yet reported
Context window
1Mmodels.devPublished source fact Retrieved Oct 9, 2026 · MIT
Open source ↗
Max output
not yet reported
Released
Jun 15, 2026models.devPublished source fact Retrieved Oct 9, 2026 · MIT
Open source ↗

How the score breaks down

Coding (weight 40 percent) 77.9
Math (weight 15 percent) —
Preference (weight 15 percent) —
Reasoning (weight 30 percent) 65.2

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. 69 GPT-5.5 Instant 58.3
  2. 70 MiniMax-M2.7 57.9
  3. 71 Fugu Ultra 57.9
  4. 72 MiniMax-M2.5 57.8
  5. 73 Grok 4.20 (Reasoning) 57.7

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.

GPQA Diamondreasoning 95.5%Official model cards via models.devLab-reported; metric percent score; transcribed by MIT models.dev catalog; not independently evaluated [variant] Retrieved Oct 9, 2026 · factual citation; MIT transcription
Open source ↗
95.5
Humanity's Last Examreasoning 50%Official model cards via models.devLab-reported; metric percent score; transcribed by MIT models.dev catalog; not independently evaluated [variant] Retrieved Oct 9, 2026 · factual citation; MIT transcription
Open source ↗
50.0
SWE-bench Procoding 73.7%Official model cards via models.devLab-reported; metric percent score; transcribed by MIT models.dev catalog; not independently evaluated [variant] Retrieved Oct 9, 2026 · factual citation; MIT transcription
Open source ↗
73.7
Terminal-Benchcoding 82.1%Official model cards via models.devLab-reported; metric percent score; transcribed by MIT models.dev catalog; not independently evaluated [variant] Retrieved Oct 9, 2026 · factual citation; MIT transcription
Open source ↗
82.1

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
Nomodels.devPublished source fact Retrieved Oct 9, 2026 · MIT
Open source ↗
License
not yet reported
Input modalities
text, imagemodels.devPublished source fact Retrieved Oct 9, 2026 · MIT
Open source ↗
First seen by SuperIndex
Oct 8, 2026
Coverage
100% of expected source weight

Reported (1)

  • Official model cards via models.devOct 8, 2026

Awaiting (0)

Every expected source has reported for this model.

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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