Model comparison
Gemini 3 Pro vs Sakana Fugu-Ultra
Head-to-head evidence from 1 shared benchmark result across 1 category. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Gemini 3 Pro #19 (Supported); Sakana Fugu-Ultra unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Gemini 3 Pro and Sakana Fugu-Ultra share 1 comparable benchmark result. 2 of 8 categories are comparable. 26 results are unique to Gemini 3 Pro; 10 to Sakana Fugu-Ultra.
Updated July 23, 2026- Shared results
- 1
- Gemini 3 Pro only
- 26
- Sakana Fugu-Ultra only
- 10
- Comparable categories
- 2 / 8
Treat this as a split decision. Gemini 3 Pro makes more sense if you need the larger 2M context window or 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 1 shared benchmark result across 1 evidence category; 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 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 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. Gemini 3 Pro gives you the larger context window at 2M, compared with 1M for Sakana Fugu-Ultra.
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 Pro | Δ | Sakana Fugu-Ultra |
|---|---|---|---|
| Reasoning | Gemini 3 Pro31.1 | Margin→ 62.5 | Sakana Fugu-Ultra93.6 |
| Multimodal | Gemini 3 Pro81.1 | Margin→ 5.5 | Sakana Fugu-Ultra86.6 |
| Agentic | Gemini 3 ProNot measured | MarginNo overlap | Sakana Fugu-Ultra82.1 |
| Coding | Gemini 3 ProNot measured | MarginNo overlap | Sakana Fugu-Ultra64.5 |
| Knowledge | Gemini 3 ProNot measured | MarginNo overlap | Sakana Fugu-Ultra95.5 |
| Math | Gemini 3 Pro32.9 | 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 81.4%B 86.6%Winner: Sakana Fugu-UltraΔ 5.2CharXiv: Gemini 3 Pro scored 81.4%; 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 Pro | Sakana Fugu-Ultra | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Gemini 3 Pro$2 input / $12 output | Sakana Fugu-UltraNot available | A complete price comparison is not available. |
| Generation speedtokens per second | Gemini 3 Pro109 tok/s | Sakana Fugu-UltraNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Gemini 3 Pro32.65 s | Sakana Fugu-UltraNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Gemini 3 Pro2M | Sakana Fugu-Ultra1M | Gemini 3 Pro lists the larger context window. |
Benchmark Deep Dive
Agentic4 benchmarks
Coding8 benchmarks
| Benchmark | Gemini 3 Pro | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| Vibe Code BenchSource | 14.30% | — | Not comparable |
| AA-SciCodeSource | 56.1% | — | Not comparable |
| AA LiveCodeBenchSource | 91.7% | — | Not comparable |
| SWE-bench ProSource | — | 73.7% | Not comparable |
| Terminal-Bench 2.0Source | — | 82.1% | Not comparable |
| LiveCodeBench v6Source | — | 93.2% | Not comparable |
| LiveCodeBench ProSource | — | 90.8% | Not comparable |
| SciCodeSource | — | 58.7% | Not comparable |
ReasoningSakana Fugu-Ultra wins4 benchmarks
Knowledge10 benchmarks
| Benchmark | Gemini 3 Pro | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 39.5% | — | Not comparable |
| AA-GPQA DiamondSource | 90.8% | — | Not comparable |
| AA-HLESource | 37.2% | — | Not comparable |
| AA-Omniscience IndexSource | 15.8% | — | Not comparable |
| AA-Omniscience AccuracySource | 55.9% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 90.9% | — | Not comparable |
| AA MMLU-ProSource | 89.8% | — | Not comparable |
| GPQASource | — | 95.5% | Not comparable |
| GPQA-DSource | — | 95.5% | Not comparable |
| HLE w/o toolsSource | — | 50% | Not comparable |
Math2 benchmarks
Multilingual1 benchmarks
| Benchmark | Gemini 3 Pro | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| AA Global-MMLU-LiteSource | 92.2% | — | Not comparable |
MultimodalSakana Fugu-Ultra wins7 benchmarks
| Benchmark | Gemini 3 Pro | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| MMMU-ProSource | 81% | — | Not comparable |
| MathVisionSource | 86.6% | — | Not comparable |
| VideoMMMUSource | 87.6% | — | Not comparable |
| ScreenSpot ProSource | 72.7% | — | Not comparable |
| CharXivSource | 81.4% | 86.6% | Sakana Fugu-Ultra leads |
| V*Source | 88.0% | — | Not comparable |
| AA-MMMU-ProSource | 80.2% | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | Gemini 3 Pro | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| AA-IFBenchSource | 70.4% | — | Not comparable |
Frequently Asked Questions (3)
Which is better, Gemini 3 Pro or Sakana Fugu-Ultra?
Gemini 3 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 Pro or Sakana Fugu-Ultra?
Sakana Fugu-Ultra has the edge for reasoning in this comparison, averaging 93.6 versus 31.1. Gemini 3 Pro stays close enough that the answer can still flip depending on your workload.
Which is better for multimodal and grounded tasks, Gemini 3 Pro or Sakana Fugu-Ultra?
Sakana Fugu-Ultra has the edge for multimodal and grounded tasks in this comparison, averaging 86.6 versus 81.1. Inside this category, CharXiv is the benchmark that creates the most daylight between them.
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