Google, released May 7, 2026
Gemini 3.1 Flash Liteprice, context, benchmarks and release details
Provisional: not enough results to rank yet 32% confidence 32 percent, Low confidence, 1 of 7 expected sources in
53.2
SI Score
Not ranked yet
- Input, per 1M tokens
- $0.25Google Gemini pricingOfficial paid Standard text rate, lowest short-context tier; current promotional price if dated; excludes free/Batch/Flex/audio
Retrieved Oct 9, 2026 · CC-BY-4.0 factual citation
Open source ↗ - Output, per 1M tokens
- $1.50Google Gemini pricingOfficial paid Standard text rate, lowest short-context tier; current promotional price if dated; excludes free/Batch/Flex/audio
Retrieved Oct 9, 2026 · CC-BY-4.0 factual citation
Open source ↗ - Context window
- 1Mmodels.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗ - Max output
- 65.5Kmodels.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗ - Released
- May 7, 2026Google Gemini release notesPublished source fact
Retrieved Oct 9, 2026 · CC-BY-4.0 factual citation
Open source ↗
How the score breaks down
Coding (weight 40 percent) —
Math (weight 15 percent) 33.9
Preference (weight 15 percent) —
Reasoning (weight 30 percent) 76.6
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 Claude Fable 5.1 80.2
- 2 Claude Opus 5.5 78.4
- 3 GPT-6 Astra 77.5
- 4 Claude Fable 5 76.8
- 5 Claude Opus 5 74.9
Benchmark results
3 benchmarks, 9 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.
GPQA Diamondreasoning 81.8%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] highPublished Aug 6, 2026
Retrieved Oct 9, 2026 · CC-BY
Open source ↗
81.8 3 settings
- minimal effort 73.7%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] minimalPublished Aug 6, 2026
Retrieved Oct 9, 2026 · CC-BY
Open source ↗ - low effort 74.2%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] lowPublished Aug 6, 2026
Retrieved Oct 9, 2026 · CC-BY
Open source ↗ - high effort 81.8%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] highPublished Aug 6, 2026
Retrieved Oct 9, 2026 · CC-BY
Open source ↗
FrontierMath Tiers 1–3 (v2)math 27.7%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] highPublished Aug 30, 2026
Retrieved Oct 9, 2026 · CC-BY
Open source ↗
27.7 3 settings
- minimal effort 21.4%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] minimalPublished Aug 28, 2026
Retrieved Oct 9, 2026 · CC-BY
Open source ↗ - low effort 22.5%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] lowPublished Aug 30, 2026
Retrieved Oct 9, 2026 · CC-BY
Open source ↗ - high effort 27.7%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] highPublished Aug 30, 2026
Retrieved Oct 9, 2026 · CC-BY
Open source ↗
OTIS Mock AIME 2024–2025math 80%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] highPublished Aug 6, 2026
Retrieved Oct 9, 2026 · CC-BY
Open source ↗
80.0 3 settings
- minimal effort 37.8%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] minimalPublished Aug 6, 2026
Retrieved Oct 9, 2026 · CC-BY
Open source ↗ - low effort 44.4%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] lowPublished Aug 6, 2026
Retrieved Oct 9, 2026 · CC-BY
Open source ↗ - high effort 80%Epoch AI BenchmarkingEpoch-owned evaluation mean score; scores only, no benchmark questions [variant] highPublished Aug 6, 2026
Retrieved Oct 9, 2026 · CC-BY
Open source ↗
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, image, video, audio, pdfmodels.devPublished source fact
Retrieved Oct 9, 2026 · MIT
Open source ↗ - First seen by SuperIndex
- Oct 8, 2026
- Coverage
- 25% of expected source weight
Reported (1)
- Epoch AI BenchmarkingOct 8, 2026
Awaiting (6)
- ARC Prize13% of weight
- Humanity’s Last Exam13% of weight
- Official model cards via models.dev4% of weight
- LiveBench13% of weight
- LMArena / Arena25% of weight
- Terminal-Bench6% of weight
Confidence rises as pending sources publish. Some sources never cover some models, so confidence reaches 100% at 80% of expected weight.