Model comparison
Mistral Small 4 (Reasoning) 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.
Evidence parity. Mistral Small 4 (Reasoning) and Sakana Fugu-Ultra v1.1 share 0 comparable benchmark results. 0 of 8 categories are comparable. 16 results are unique to Mistral Small 4 (Reasoning); 0 to Sakana Fugu-Ultra v1.1.
Updated July 24, 2026- Shared results
- 0
- Mistral Small 4 (Reasoning) only
- 16
- Sakana Fugu-Ultra v1.1 only
- 0
- Comparable categories
- 0 / 8
Benchmark data for Mistral Small 4 (Reasoning) 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 $0.15 input / $0.60 output per 1M tokens for Mistral Small 4 (Reasoning). Sakana Fugu-Ultra v1.1 has the larger context window at 1M, compared with 256K for Mistral Small 4 (Reasoning).
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Mistral Small 4 (Reasoning) | Sakana Fugu-Ultra v1.1 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Mistral Small 4 (Reasoning)$0.15 input / $0.6 output | Sakana Fugu-Ultra v1.1$5 input / $30 output | Mistral Small 4 (Reasoning) has the lower combined listed price. |
| Generation speedtokens per second | Mistral Small 4 (Reasoning)Not available | Sakana Fugu-Ultra v1.1Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Mistral Small 4 (Reasoning)Not available | Sakana Fugu-Ultra v1.1Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Mistral Small 4 (Reasoning)256K | Sakana Fugu-Ultra v1.11M | Sakana Fugu-Ultra v1.1 lists the larger context window. |
Benchmark Deep Dive
Agentic4 benchmarks
Coding2 benchmarks
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | Mistral Small 4 (Reasoning) | Sakana Fugu-Ultra v1.1 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 19.6% | — | Not comparable |
| AA-GPQA DiamondSource | 76.9% | — | Not comparable |
| AA-HLESource | 9.5% | — | Not comparable |
| AA-Omniscience IndexSource | -29.9% | — | Not comparable |
| AA-Omniscience AccuracySource | 22.1% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 66.8% | — | Not comparable |
Multimodal1 benchmarks
| Benchmark | Mistral Small 4 (Reasoning) | Sakana Fugu-Ultra v1.1 | Result |
|---|---|---|---|
| AA-MMMU-ProSource | 56.8% | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | Mistral Small 4 (Reasoning) | Sakana Fugu-Ultra v1.1 | Result |
|---|---|---|---|
| AA-IFBenchSource | 48.2% | — | Not comparable |
Frequently Asked Questions (3)
Can I compare Mistral Small 4 (Reasoning) 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 Mistral Small 4 (Reasoning) and Sakana Fugu-Ultra v1.1 today?
Mistral Small 4 (Reasoning): $0.15 input / $0.60 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.