Model comparison
Gemma 4 E4B vs Sakana Fugu-Ultra v1.1
Head-to-head evidence from 0 shared benchmark results across 0 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Gemma 4 E4B #166 (Estimated); Sakana Fugu-Ultra v1.1 unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Gemma 4 E4B and Sakana Fugu-Ultra v1.1 share 0 comparable benchmark results. 0 of 8 categories are comparable. 18 results are unique to Gemma 4 E4B; 0 to Sakana Fugu-Ultra v1.1.
Updated July 24, 2026- Shared results
- 0
- Gemma 4 E4B only
- 18
- Sakana Fugu-Ultra v1.1 only
- 0
- Comparable categories
- 0 / 8
Benchmark data for Gemma 4 E4B and Sakana Fugu-Ultra v1.1 is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 0 shared benchmark results across 0 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM does not have sourced benchmark coverage for Sakana Fugu-Ultra v1.1 yet. This comparison is currently limited to metadata such as context window, reasoning mode, and pricing where available.
Sakana Fugu-Ultra v1.1 is priced at $5.00 input / $30.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Gemma 4 E4B. Sakana Fugu-Ultra v1.1 has the larger context window at 1M, compared with 128K for Gemma 4 E4B.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Gemma 4 E4B | Sakana Fugu-Ultra v1.1 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Gemma 4 E4B$0 input / $0 output | Sakana Fugu-Ultra v1.1$5 input / $30 output | Gemma 4 E4B has the lower combined listed price. |
| Generation speedtokens per second | Gemma 4 E4BNot available | Sakana Fugu-Ultra v1.1Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Gemma 4 E4BNot available | Sakana Fugu-Ultra v1.1Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Gemma 4 E4B128K | Sakana Fugu-Ultra v1.11M | Sakana Fugu-Ultra v1.1 lists the larger context window. |
Benchmark Deep Dive
Agentic4 benchmarks
Coding2 benchmarks
Reasoning2 benchmarks
Knowledge8 benchmarks
| Benchmark | Gemma 4 E4B | Sakana Fugu-Ultra v1.1 | Result |
|---|---|---|---|
| GPQASource | 58.6% | — | Not comparable |
| MMLU-ProSource | 69.4% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 11.9% | — | Not comparable |
| AA-GPQA DiamondSource | 52.2% | — | Not comparable |
| AA-HLESource | 3.7% | — | Not comparable |
| AA-Omniscience IndexSource | -20.0% | — | Not comparable |
| AA-Omniscience AccuracySource | 8.6% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 31.3% | — | Not comparable |
Multimodal1 benchmarks
| Benchmark | Gemma 4 E4B | Sakana Fugu-Ultra v1.1 | Result |
|---|---|---|---|
| AA-MMMU-ProSource | 51.4% | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | Gemma 4 E4B | Sakana Fugu-Ultra v1.1 | Result |
|---|---|---|---|
| AA-IFBenchSource | 40.6% | — | Not comparable |
Frequently Asked Questions (3)
Can I compare Gemma 4 E4B and Sakana Fugu-Ultra v1.1 on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for Gemma 4 E4B and Sakana Fugu-Ultra v1.1 today?
Gemma 4 E4B: $0.00 input / $0.00 output per 1M tokens Sakana Fugu-Ultra v1.1: $5.00 input / $30.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.