Model comparison
Gemini 3.1 Pro vs Sakana Fugu-Ultra
Head-to-head evidence from 4 shared benchmark results across 3 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Gemini 3.1 Pro #83 (Estimated); Sakana Fugu-Ultra unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Gemini 3.1 Pro and Sakana Fugu-Ultra share 4 comparable benchmark results. 2 of 8 categories are comparable. 43 results are unique to Gemini 3.1 Pro; 7 to Sakana Fugu-Ultra.
Updated July 23, 2026- Shared results
- 4
- Gemini 3.1 Pro only
- 43
- Sakana Fugu-Ultra only
- 7
- Comparable categories
- 2 / 8
Treat this as a split decision. Gemini 3.1 Pro makes more sense if you would rather avoid the extra latency and token burn of a reasoning model; Sakana Fugu-Ultra is the better fit if reasoning is the priority or you want the stronger reasoning-first profile.
Confidence note. This is a partial-evidence comparison with 4 shared benchmark results across 3 evidence categories; 2 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Gemini 3.1 Pro and Sakana Fugu-Ultra finish on the same BenchAlign overall score, so this is less about a single winner and more about where the edge shows up. The BenchAlign headline says tie; the benchmark table is where the real choice happens.
Sakana Fugu-Ultra is the reasoning model in the pair, while Gemini 3.1 Pro is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | Gemini 3.1 Pro | Δ | Sakana Fugu-Ultra |
|---|---|---|---|
| Reasoning | Gemini 3.1 Pro77.1 | Margin→ 16.5 | Sakana Fugu-Ultra93.6 |
| Multimodal | Gemini 3.1 Pro82.6 | Margin→ 4.0 | Sakana Fugu-Ultra86.6 |
| Agentic | Gemini 3.1 ProNot measured | MarginNo overlap | Sakana Fugu-Ultra82.1 |
| Coding | Gemini 3.1 ProNot measured | MarginNo overlap | Sakana Fugu-Ultra64.5 |
| Knowledge | Gemini 3.1 ProNot measured | MarginNo overlap | Sakana Fugu-Ultra95.5 |
| Math | Gemini 3.1 Pro31.8 | MarginNo overlap | Sakana Fugu-UltraNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
CharXiv
MultimodalA 80.2%B 86.6%Winner: Sakana Fugu-UltraΔ 6.4CharXiv: Gemini 3.1 Pro scored 80.2%; Sakana Fugu-Ultra scored 86.6%. Sakana Fugu-Ultra wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Gemini 3.1 Pro | Sakana Fugu-Ultra | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Gemini 3.1 Pro$2 input / $12 output | Sakana Fugu-UltraNot available | A complete price comparison is not available. |
| Generation speedtokens per second | Gemini 3.1 Pro109 tok/s | Sakana Fugu-UltraNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Gemini 3.1 Pro29.71 s | Sakana Fugu-UltraNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Gemini 3.1 Pro1M | Sakana Fugu-Ultra1M | Listed context windows are equal. |
Benchmark Deep Dive
Agentic17 benchmarks
| Benchmark | Gemini 3.1 Pro | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| Claw-EvalSource | 57.8% | — | Not comparable |
| DeepSearchQASource | 69.7% | — | Not comparable |
| τ²-bench resultsSource | 95.6% | — | Not comparable |
| AA Agentic IndexSource | 21.4% | — | Not comparable |
| APEX-Agents-AASource | 32.0% | — | Not comparable |
| GDPval-AASource | 23.3% | — | Not comparable |
| GDPval-AASource | 965 | — | Not comparable |
| Gert LabsSource | 56.87% | — | Not comparable |
| ResearchClawBenchSource | 13.3% | — | Not comparable |
| AA AutomationBenchSource | 37.5% | — | Not comparable |
| AA EnterpriseOps-GymSource | 42.2% | — | Not comparable |
| AA Harvey LABSource | 58.9% | — | Not comparable |
| AA ITBenchSource | 30.3% | — | Not comparable |
| AA Tau3 BankingSource | 16.5% | — | Not comparable |
| terminalBenchHardSource | 53.8% | — | Not comparable |
| aaTerminalBench21Source | 73.8% | — | Not comparable |
| Terminal-Bench 2.0Source | — | 82.1% | Not comparable |
Coding9 benchmarks
| Benchmark | Gemini 3.1 Pro | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| LiveCodeBench ProSource | 82.9% | 90.8% | Sakana Fugu-Ultra leads |
| React Native EvalsSource | 78.9% | — | Not comparable |
| Vibe Code BenchSource | 32.03% | — | Not comparable |
| AA Coding IndexSource | 68.8% | — | Not comparable |
| AA-SciCodeSource | 58.9% | — | Not comparable |
| SWE-bench ProSource | — | 73.7% | Not comparable |
| Terminal-Bench 2.0Source | — | 82.1% | Not comparable |
| LiveCodeBench v6Source | — | 93.2% | Not comparable |
| SciCodeSource | — | 58.7% | Not comparable |
ReasoningSakana Fugu-Ultra wins4 benchmarks
Knowledge11 benchmarks
| Benchmark | Gemini 3.1 Pro | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| GPQA-DSource | 94.3% | 95.5% | Sakana Fugu-Ultra leads |
| HLE w/o toolsSource | 45.4% | 50% | Sakana Fugu-Ultra leads |
| HealthBench HardSource | 20.6% | — | Not comparable |
| MedXpertQA (Text)Source | 71.5% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 46.5% | — | Not comparable |
| AA-GPQA DiamondSource | 94.1% | — | Not comparable |
| AA-HLESource | 44.7% | — | Not comparable |
| AA-Omniscience IndexSource | 32.9% | — | Not comparable |
| AA-Omniscience AccuracySource | 55.3% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 49.9% | — | Not comparable |
| GPQASource | — | 95.5% | Not comparable |
Math2 benchmarks
Multilingual1 benchmarks
| Benchmark | Gemini 3.1 Pro | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| AA Global-MMLU-LiteSource | 93.2% | — | Not comparable |
MultimodalSakana Fugu-Ultra wins9 benchmarks
| Benchmark | Gemini 3.1 Pro | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| MMMU-ProSource | 83.9% | — | Not comparable |
| CharXivSource | 80.2% | 86.6% | Sakana Fugu-Ultra leads |
| ERQASource | 69.4% | — | Not comparable |
| SimpleVQASource | 72.4% | — | Not comparable |
| ScreenSpot ProSource | 84.4% | — | Not comparable |
| ZeroBenchSource | 29.0% | — | Not comparable |
| MedXpertQA (MM)Source | 81.3% | — | Not comparable |
| AA-MMMU-ProSource | 82.4% | — | Not comparable |
| Design Arena WebsiteSource | 1281 | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | Gemini 3.1 Pro | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| AA-IFBenchSource | 77.1% | — | Not comparable |
Frequently Asked Questions (3)
Which is better, Gemini 3.1 Pro or Sakana Fugu-Ultra?
Gemini 3.1 Pro and Sakana Fugu-Ultra are tied on the BenchAlign overall score, so the right pick depends on which category matters most for your use case.
Which is better for reasoning, Gemini 3.1 Pro or Sakana Fugu-Ultra?
Sakana Fugu-Ultra has the edge for reasoning in this comparison, averaging 93.6 versus 77.1. Gemini 3.1 Pro stays close enough that the answer can still flip depending on your workload.
Which is better for multimodal and grounded tasks, Gemini 3.1 Pro or Sakana Fugu-Ultra?
Sakana Fugu-Ultra has the edge for multimodal and grounded tasks in this comparison, averaging 86.6 versus 82.6. Inside this category, CharXiv is the benchmark that creates the most daylight between them.
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