Model comparison
Gemini 2.5 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 2.5 Pro #70 (Supported); Sakana Fugu-Ultra unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Gemini 2.5 Pro and Sakana Fugu-Ultra share 1 comparable benchmark result. 2 of 8 categories are comparable. 23 results are unique to Gemini 2.5 Pro; 10 to Sakana Fugu-Ultra.
Updated July 23, 2026- Shared results
- 1
- Gemini 2.5 Pro only
- 23
- Sakana Fugu-Ultra only
- 10
- Comparable categories
- 2 / 8
Treat this as a split decision. Gemini 2.5 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 knowledge 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 2.5 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 2.5 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 2.5 Pro | Δ | Sakana Fugu-Ultra |
|---|---|---|---|
| Knowledge | Gemini 2.5 Pro27.4 | Margin→ 68.1 | Sakana Fugu-Ultra95.5 |
| Coding | Gemini 2.5 Pro63.8 | Margin→ 0.7 | Sakana Fugu-Ultra64.5 |
| Agentic | Gemini 2.5 ProNot measured | MarginNo overlap | Sakana Fugu-Ultra82.1 |
| Reasoning | Gemini 2.5 ProNot measured | MarginNo overlap | Sakana Fugu-Ultra93.6 |
| Math | Gemini 2.5 Pro11.6 | MarginNo overlap | Sakana Fugu-UltraNot measured |
| Multimodal | Gemini 2.5 ProNot measured | MarginNo overlap | Sakana Fugu-Ultra86.6 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
GPQA
KnowledgeA 83%B 95.5%Winner: Sakana Fugu-UltraΔ 12.5GPQA: Gemini 2.5 Pro scored 83%; Sakana Fugu-Ultra scored 95.5%. 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 2.5 Pro | Sakana Fugu-Ultra | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Gemini 2.5 Pro$1.25 input / $10 output | Sakana Fugu-UltraNot available | A complete price comparison is not available. |
| Generation speedtokens per second | Gemini 2.5 Pro117 tok/s | Sakana Fugu-UltraNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Gemini 2.5 Pro21.19 s | Sakana Fugu-UltraNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Gemini 2.5 Pro1M | Sakana Fugu-Ultra1M | Listed context windows are equal. |
Benchmark Deep Dive
Agentic6 benchmarks
CodingSakana Fugu-Ultra wins9 benchmarks
| Benchmark | Gemini 2.5 Pro | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 63.8% | — | Not comparable |
| Vibe Code BenchSource | 0.40% | — | Not comparable |
| AA Coding IndexSource | 33.3% | — | Not comparable |
| AA-SciCodeSource | 42.8% | — | 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 |
Reasoning3 benchmarks
KnowledgeSakana Fugu-Ultra wins10 benchmarks
| Benchmark | Gemini 2.5 Pro | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| GPQASource | 83% | 95.5% | Sakana Fugu-Ultra leads |
| HLESource | 18.8% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 25.8% | — | Not comparable |
| AA-GPQA DiamondSource | 84.4% | — | Not comparable |
| AA-HLESource | 21.1% | — | Not comparable |
| AA-Omniscience IndexSource | -14.3% | — | Not comparable |
| AA-Omniscience AccuracySource | 39.0% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 87.4% | — | Not comparable |
| GPQA-DSource | — | 95.5% | Not comparable |
| HLE w/o toolsSource | — | 50% | Not comparable |
Math2 benchmarks
Multimodal3 benchmarks
Inst. Following1 benchmarks
| Benchmark | Gemini 2.5 Pro | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| AA-IFBenchSource | 48.7% | — | Not comparable |
Frequently Asked Questions (3)
Which is better, Gemini 2.5 Pro or Sakana Fugu-Ultra?
Gemini 2.5 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 knowledge tasks, Gemini 2.5 Pro or Sakana Fugu-Ultra?
Sakana Fugu-Ultra has the edge for knowledge tasks in this comparison, averaging 95.5 versus 27.4. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Which is better for coding, Gemini 2.5 Pro or Sakana Fugu-Ultra?
Sakana Fugu-Ultra has the edge for coding in this comparison, averaging 64.5 versus 63.8. Gemini 2.5 Pro stays close enough that the answer can still flip depending on your workload.
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