Model comparison
LFM2.5-230M vs Sakana Fugu-Ultra
Head-to-head evidence from 2 shared benchmark results across 1 category. Overall scores shown here use the public BenchAlign v5 ranking lane.
Evidence parity. LFM2.5-230M and Sakana Fugu-Ultra share 2 comparable benchmark results. 1 of 8 categories are comparable. 4 results are unique to LFM2.5-230M; 9 to Sakana Fugu-Ultra.
Updated July 23, 2026- Shared results
- 2
- LFM2.5-230M only
- 4
- Sakana Fugu-Ultra only
- 9
- Comparable categories
- 1 / 8
Treat this as a split decision. LFM2.5-230M 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 need the larger 1M context window.
Confidence note. This is a partial-evidence comparison with 2 shared benchmark results across 1 evidence category; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
LFM2.5-230M 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 LFM2.5-230M 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 32K for LFM2.5-230M.
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 | LFM2.5-230M | Δ | Sakana Fugu-Ultra |
|---|---|---|---|
| Knowledge | LFM2.5-230M21.2 | Margin→ 74.3 | Sakana Fugu-Ultra95.5 |
| Agentic | LFM2.5-230MNot measured | MarginNo overlap | Sakana Fugu-Ultra82.1 |
| Coding | LFM2.5-230MNot measured | MarginNo overlap | Sakana Fugu-Ultra64.5 |
| Reasoning | LFM2.5-230MNot measured | MarginNo overlap | Sakana Fugu-Ultra93.6 |
| Multimodal | LFM2.5-230MNot measured | MarginNo overlap | Sakana Fugu-Ultra86.6 |
| Inst. Following | LFM2.5-230M50.1 | MarginNo overlap | Sakana Fugu-UltraNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
GPQA
KnowledgeA 25.4%B 95.5%Winner: Sakana Fugu-UltraΔ 70.1GPQA: LFM2.5-230M scored 25.4%; 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 | LFM2.5-230M | Sakana Fugu-Ultra | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | LFM2.5-230M$0 input / $0 output | Sakana Fugu-UltraNot available | A complete price comparison is not available. |
| Generation speedtokens per second | LFM2.5-230MNot available | Sakana Fugu-UltraNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | LFM2.5-230MNot available | Sakana Fugu-UltraNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | LFM2.5-230M32K | Sakana Fugu-Ultra1M | Sakana Fugu-Ultra lists the larger context window. |
Benchmark Deep Dive
Frequently Asked Questions (2)
Which is better, LFM2.5-230M or Sakana Fugu-Ultra?
LFM2.5-230M 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, LFM2.5-230M or Sakana Fugu-Ultra?
Sakana Fugu-Ultra has the edge for knowledge tasks in this comparison, averaging 95.5 versus 21.2. Inside this category, GPQA is the benchmark that creates the most daylight between them.
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