Model comparison
Claude Sonnet 4.6 vs Sakana Fugu-Ultra
Head-to-head evidence from 3 shared benchmark results across 3 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Claude Sonnet 4.6 #32 (Supported); Sakana Fugu-Ultra unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Claude Sonnet 4.6 and Sakana Fugu-Ultra share 3 comparable benchmark results. 4 of 8 categories are comparable. 30 results are unique to Claude Sonnet 4.6; 8 to Sakana Fugu-Ultra.
Updated July 23, 2026- Shared results
- 3
- Claude Sonnet 4.6 only
- 30
- Sakana Fugu-Ultra only
- 8
- Comparable categories
- 4 / 8
Treat this as a split decision. Claude Sonnet 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 need the larger 1M context window.
Confidence note. This is a partial-evidence comparison with 3 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 Sonnet 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 Sonnet 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. Sakana Fugu-Ultra gives you the larger context window at 1M, compared with 200K for Claude Sonnet 4.6.
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 Sonnet 4.6 | Δ | Sakana Fugu-Ultra |
|---|---|---|---|
| Knowledge | Claude Sonnet 4.666.0 | Margin→ 29.5 | Sakana Fugu-Ultra95.5 |
| Agentic | Claude Sonnet 4.665.2 | Margin→ 16.9 | Sakana Fugu-Ultra82.1 |
| Multimodal | Claude Sonnet 4.677.4 | Margin→ 9.2 | Sakana Fugu-Ultra86.6 |
| Coding | Claude Sonnet 4.669.1 | Margin← 4.6 | Sakana Fugu-Ultra64.5 |
| Reasoning | Claude Sonnet 4.6Not measured | MarginNo overlap | Sakana Fugu-Ultra93.6 |
| Math | Claude Sonnet 4.626.4 | MarginNo overlap | Sakana Fugu-UltraNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 59.1%B 82.1%Winner: Sakana Fugu-UltraΔ 23Terminal-Bench 2.0: Claude Sonnet 4.6 scored 59.1%; Sakana Fugu-Ultra scored 82.1%. Sakana Fugu-Ultra wins this benchmark. - Source ↗
CharXiv
MultimodalA 77.4%B 86.6%Winner: Sakana Fugu-UltraΔ 9.2CharXiv: Claude Sonnet 4.6 scored 77.4%; Sakana Fugu-Ultra scored 86.6%. Sakana Fugu-Ultra wins this benchmark. - Source ↗
GPQA
KnowledgeA 89.9%B 95.5%Winner: Sakana Fugu-UltraΔ 5.6GPQA: Claude Sonnet 4.6 scored 89.9%; 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 Sonnet 4.6 | Sakana Fugu-Ultra | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Claude Sonnet 4.6$3 input / $15 output | Sakana Fugu-UltraNot available | A complete price comparison is not available. |
| Generation speedtokens per second | Claude Sonnet 4.644 tok/s | Sakana Fugu-UltraNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Claude Sonnet 4.61.48 s | Sakana Fugu-UltraNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Claude Sonnet 4.6200K | Sakana Fugu-Ultra1M | Sakana Fugu-Ultra lists the larger context window. |
Benchmark Deep Dive
AgenticSakana Fugu-Ultra wins8 benchmarks
| Benchmark | Claude Sonnet 4.6 | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 59.1% | 82.1% | Sakana Fugu-Ultra leads |
| OSWorld-VerifiedSource | 72.1% | — | Not comparable |
| Claw-EvalSource | 67.8% | — | Not comparable |
| CyberGymSource | 65.2% | — | Not comparable |
| τ²-bench resultsSource | 79.5% | — | Not comparable |
| Gert LabsSource | 62.92% | — | Not comparable |
| OSWorld 2.0Source | 8.3% | — | Not comparable |
| JobBenchSource | 36.9% | — | Not comparable |
CodingClaude Sonnet 4.6 wins12 benchmarks
| Benchmark | Claude Sonnet 4.6 | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 79.6% | — | Not comparable |
| SWE-RebenchSource | 60.7% | — | Not comparable |
| React Native EvalsSource | 80.6% | — | Not comparable |
| Vibe Code BenchSource | 51.48% | — | Not comparable |
| cursorBench31Source | 48.8% | — | Not comparable |
| AA-SciCodeSource | 46.9% | — | Not comparable |
| FrontierCode 1.1 MainSource | 24.3% | — | 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 wins12 benchmarks
| Benchmark | Claude Sonnet 4.6 | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| GPQASource | 89.9% | 95.5% | Sakana Fugu-Ultra leads |
| SuperGPQASource | 95% | — | Not comparable |
| MMLU-ProSource | 79.2% | — | Not comparable |
| HLESource | 49% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 35.9% | — | Not comparable |
| AA-GPQA DiamondSource | 79.9% | — | Not comparable |
| AA-HLESource | 13.2% | — | Not comparable |
| AA-Omniscience IndexSource | -2.9% | — | Not comparable |
| AA-Omniscience AccuracySource | 38.0% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 65.9% | — | Not comparable |
| GPQA-DSource | — | 95.5% | Not comparable |
| HLE w/o toolsSource | — | 50% | Not comparable |
Math2 benchmarks
MultimodalSakana Fugu-Ultra wins3 benchmarks
Inst. Following1 benchmarks
| Benchmark | Claude Sonnet 4.6 | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| AA-IFBenchSource | 41.2% | — | Not comparable |
Frequently Asked Questions (5)
Which is better, Claude Sonnet 4.6 or Sakana Fugu-Ultra?
Claude Sonnet 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 Sonnet 4.6 or Sakana Fugu-Ultra?
Sakana Fugu-Ultra has the edge for knowledge tasks in this comparison, averaging 95.5 versus 66. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Which is better for coding, Claude Sonnet 4.6 or Sakana Fugu-Ultra?
Claude Sonnet 4.6 has the edge for coding in this comparison, averaging 69.1 versus 64.5. Sakana Fugu-Ultra stays close enough that the answer can still flip depending on your workload.
Which is better for agentic tasks, Claude Sonnet 4.6 or Sakana Fugu-Ultra?
Sakana Fugu-Ultra has the edge for agentic tasks in this comparison, averaging 82.1 versus 65.2. 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 Sonnet 4.6 or Sakana Fugu-Ultra?
Sakana Fugu-Ultra has the edge for multimodal and grounded tasks in this comparison, averaging 86.6 versus 77.4. Inside this category, CharXiv is the benchmark that creates the most daylight between them.
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