Model comparison
Claude Opus 4.6 vs Sakana Fugu-Ultra
Head-to-head evidence from 6 shared benchmark results across 3 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Claude Opus 4.6 #16 (Supported); Sakana Fugu-Ultra unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Claude Opus 4.6 and Sakana Fugu-Ultra share 6 comparable benchmark results. 4 of 8 categories are comparable. 40 results are unique to Claude Opus 4.6; 5 to Sakana Fugu-Ultra.
Updated July 23, 2026- Shared results
- 6
- Claude Opus 4.6 only
- 40
- Sakana Fugu-Ultra only
- 5
- Comparable categories
- 4 / 8
Treat this as a split decision. Claude Opus 4.6 makes more sense if coding is the priority or 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 6 shared benchmark results across 3 evidence categories; 4 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Claude Opus 4.6 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 Claude Opus 4.6 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 | Claude Opus 4.6 | Δ | Sakana Fugu-Ultra |
|---|---|---|---|
| Knowledge | Claude Opus 4.669.1 | Margin→ 26.4 | Sakana Fugu-Ultra95.5 |
| Multimodal | Claude Opus 4.677.3 | Margin→ 9.3 | Sakana Fugu-Ultra86.6 |
| Agentic | Claude Opus 4.673.0 | Margin→ 9.1 | Sakana Fugu-Ultra82.1 |
| Coding | Claude Opus 4.668.1 | Margin← 3.6 | Sakana Fugu-Ultra64.5 |
| Reasoning | Claude Opus 4.6Not measured | MarginNo overlap | Sakana Fugu-Ultra93.6 |
| Math | Claude Opus 4.636.3 | MarginNo overlap | Sakana Fugu-UltraNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
SWE-bench Pro
CodingA 53.4%B 73.7%Winner: Sakana Fugu-UltraΔ 20.3SWE-bench Pro: Claude Opus 4.6 scored 53.4%; Sakana Fugu-Ultra scored 73.7%. Sakana Fugu-Ultra wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 65.4%B 82.1%Winner: Sakana Fugu-UltraΔ 16.7Terminal-Bench 2.0: Claude Opus 4.6 scored 65.4%; Sakana Fugu-Ultra scored 82.1%. Sakana Fugu-Ultra wins this benchmark. - Source ↗
GPQA
KnowledgeA 91.3%B 95.5%Winner: Sakana Fugu-UltraΔ 4.2GPQA: Claude Opus 4.6 scored 91.3%; 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 | Claude Opus 4.6 | Sakana Fugu-Ultra | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Claude Opus 4.6$5 input / $25 output | Sakana Fugu-UltraNot available | A complete price comparison is not available. |
| Generation speedtokens per second | Claude Opus 4.640 tok/s | Sakana Fugu-UltraNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Claude Opus 4.61.78 s | Sakana Fugu-UltraNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Claude Opus 4.61M | Sakana Fugu-Ultra1M | Listed context windows are equal. |
Benchmark Deep Dive
AgenticSakana Fugu-Ultra wins10 benchmarks
| Benchmark | Claude Opus 4.6 | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 65.4% | 82.1% | Sakana Fugu-Ultra leads |
| BrowseCompSource | 83.7% | — | Not comparable |
| OSWorld-VerifiedSource | 72.7% | — | Not comparable |
| τ²-bench resultsSource | 84.8% | — | Not comparable |
| Claw-EvalSource | 70.4% | — | Not comparable |
| DeepSearchQASource | 73.7% | — | Not comparable |
| CyberGymSource | 66.6% | — | Not comparable |
| Gert LabsSource | 61.85% | — | Not comparable |
| ResearchClawBenchSource | 19.9% | — | Not comparable |
| JobBenchSource | 36.7% | — | Not comparable |
CodingClaude Opus 4.6 wins12 benchmarks
| Benchmark | Claude Opus 4.6 | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 80.8% | — | Not comparable |
| SWE-bench Verified*Source | 75.6% | — | Not comparable |
| LiveCodeBench ProSource | 70.7% | 90.8% | Sakana Fugu-Ultra leads |
| SWE-bench ProSource | 53.4% | 73.7% | Sakana Fugu-Ultra leads |
| SWE-RebenchSource | 65.3% | — | Not comparable |
| React Native EvalsSource | 84.1% | — | Not comparable |
| Vibe Code BenchSource | 57.57% | — | Not comparable |
| AA-SciCodeSource | 45.7% | — | Not comparable |
| FrontierCode 1.1 MainSource | 26.9% | — | Not comparable |
| Terminal-Bench 2.0Source | — | 82.1% | Not comparable |
| LiveCodeBench v6Source | — | 93.2% | Not comparable |
| SciCodeSource | — | 58.7% | Not comparable |
Reasoning3 benchmarks
KnowledgeSakana Fugu-Ultra wins15 benchmarks
| Benchmark | Claude Opus 4.6 | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| GPQASource | 91.3% | 95.5% | Sakana Fugu-Ultra leads |
| GPQA-DSource | 89.2% | 95.5% | Sakana Fugu-Ultra leads |
| SuperGPQASource | 95% | — | Not comparable |
| MMLU-ProSource | 82% | — | Not comparable |
| MMLU-Pro (Arcee)Source | 89.1% | — | Not comparable |
| HLESource | 53% | — | Not comparable |
| HLE w/o toolsSource | 40% | 50% | Sakana Fugu-Ultra leads |
| HealthBench HardSource | 14.8% | — | Not comparable |
| MedXpertQA (Text)Source | 52.1% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 37.8% | — | Not comparable |
| AA-GPQA DiamondSource | 84.0% | — | Not comparable |
| AA-HLESource | 18.6% | — | Not comparable |
| AA-Omniscience IndexSource | 3.5% | — | Not comparable |
| AA-Omniscience AccuracySource | 45.2% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 76.0% | — | Not comparable |
Math3 benchmarks
MultimodalSakana Fugu-Ultra wins7 benchmarks
| Benchmark | Claude Opus 4.6 | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| MMMU-ProSource | 77.3% | — | Not comparable |
| ERQASource | 51.6% | — | Not comparable |
| ScreenSpot ProSource | 83.1% | — | Not comparable |
| MedXpertQA (MM)Source | 64.8% | — | Not comparable |
| AA-MMMU-ProSource | 72.5% | — | Not comparable |
| Design Arena WebsiteSource | 1325 | — | Not comparable |
| CharXivSource | — | 86.6% | Not comparable |
Inst. Following1 benchmarks
| Benchmark | Claude Opus 4.6 | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| AA-IFBenchSource | 44.6% | — | Not comparable |
Frequently Asked Questions (5)
Which is better, Claude Opus 4.6 or Sakana Fugu-Ultra?
Claude Opus 4.6 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, Claude Opus 4.6 or Sakana Fugu-Ultra?
Sakana Fugu-Ultra has the edge for knowledge tasks in this comparison, averaging 95.5 versus 69.1. Inside this category, HLE w/o tools is the benchmark that creates the most daylight between them.
Which is better for coding, Claude Opus 4.6 or Sakana Fugu-Ultra?
Claude Opus 4.6 has the edge for coding in this comparison, averaging 68.1 versus 64.5. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, Claude Opus 4.6 or Sakana Fugu-Ultra?
Sakana Fugu-Ultra has the edge for agentic tasks in this comparison, averaging 82.1 versus 73. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Which is better for multimodal and grounded tasks, Claude Opus 4.6 or Sakana Fugu-Ultra?
Sakana Fugu-Ultra has the edge for multimodal and grounded tasks in this comparison, averaging 86.6 versus 77.3. Claude Opus 4.6 stays close enough that the answer can still flip depending on your workload.
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