Intelligence
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General reasoning and benchmark headroom.
UnavailableSakana: Fugu Ultra is a mid-range-priced multimodal generalist from Sakana with partial runtime data, large context posture, and the clearest fit around long-context research / multimodal.
Benchmark blend
Dev workflow signal
Large
Mid-range tier
Sakana: Fugu Ultra currently reads as a mid-range multimodal option with large context and a partially published runtime profile.
Decision Strip
Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.
General reasoning and benchmark headroom.
UnavailableLatency data is partial.
UnavailableHow much prompt and task state can stay in view.
Above average$30.00 output / 1M
CompetitiveEditorial Profile
Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.
Fugu Ultra is the higher-performance model in Sakana AI's Fugu family. Rather than a single monolithic model, Fugu is a learned multi-agent orchestration system: a language model trained to route...
Sakana multimodal profile
Long-context research / Multimodal with large context and partially published runtime.
Balanced spend profile. Easier to justify in mixed production and exploration workloads.
Large context headroom supports repo-wide prompts and long research sessions.
Vision-capable routing opens up multimodal review and extraction workflows.
Costs look manageable, but still deserve attention in always-on agents or batch jobs.
Latency data is incomplete, so interactive responsiveness is harder to rank confidently.
Image-grounded review, multimodal extraction, and UI audit workflows.
Long-context summarization, repo analysis, and policy or document review.
Benchmarks
Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.
Specs & Pricing
Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.
This model sits in a balanced spend range. It is easier to justify across both production and exploratory workflows.
Metadata
Verification details remain available, but the page no longer forces them ahead of the editorial read.