Model comparison
Kimi K3 vs Sakana Fugu-Ultra
Head-to-head evidence from 5 shared benchmark results across 3 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Kimi K3 #4 (Supported); Sakana Fugu-Ultra unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Kimi K3 and Sakana Fugu-Ultra share 5 comparable benchmark results. 3 of 8 categories are comparable. 52 results are unique to Kimi K3; 6 to Sakana Fugu-Ultra.
Updated July 23, 2026- Shared results
- 5
- Kimi K3 only
- 52
- Sakana Fugu-Ultra only
- 6
- Comparable categories
- 3 / 8
Treat this as a split decision. Kimi K3 makes more sense if agentic is the priority or you need the larger 1.05M context window; Sakana Fugu-Ultra is the better fit if knowledge is the priority.
Confidence note. This is a partial-evidence comparison with 5 shared benchmark results across 3 evidence categories; 3 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Kimi K3 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.
Kimi K3 gives you the larger context window at 1.05M, compared with 1M for Sakana Fugu-Ultra.
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 | Kimi K3 | Δ | Sakana Fugu-Ultra |
|---|---|---|---|
| Knowledge | Kimi K361.0 | Margin→ 34.5 | Sakana Fugu-Ultra95.5 |
| Multimodal | Kimi K378.5 | Margin→ 8.1 | Sakana Fugu-Ultra86.6 |
| Agentic | Kimi K389.5 | Margin← 7.4 | Sakana Fugu-Ultra82.1 |
| Coding | Kimi K3Not measured | MarginNo overlap | Sakana Fugu-Ultra64.5 |
| Reasoning | Kimi K3Not measured | MarginNo overlap | Sakana Fugu-Ultra93.6 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 88.3%B 82.1%Winner: Kimi K3Δ 6.2Terminal-Bench 2.0: Kimi K3 scored 88.3%; Sakana Fugu-Ultra scored 82.1%. Kimi K3 wins this benchmark. - Source ↗
CharXiv
MultimodalA 91.3%B 86.6%Winner: Kimi K3Δ 4.7CharXiv: Kimi K3 scored 91.3%; Sakana Fugu-Ultra scored 86.6%. Kimi K3 wins this benchmark. - Source ↗
GPQA
KnowledgeA 93.5%B 95.5%Winner: Sakana Fugu-UltraΔ 2GPQA: Kimi K3 scored 93.5%; 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 | Kimi K3 | Sakana Fugu-Ultra | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Kimi K3$3 input / $15 output | Sakana Fugu-UltraNot available | A complete price comparison is not available. |
| Generation speedtokens per second | Kimi K3Not available | Sakana Fugu-UltraNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Kimi K3Not available | Sakana Fugu-UltraNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Kimi K31.05M | Sakana Fugu-Ultra1M | Kimi K3 lists the larger context window. |
Benchmark Deep Dive
AgenticKimi K3 wins21 benchmarks
| Benchmark | Kimi K3 | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 88.3% | 82.1% | Kimi K3 leads |
| BrowseCompSource | 91.2% | — | Not comparable |
| DeepSearchQASource | 95.0% | — | Not comparable |
| Toolathlon-VerifiedSource | 73.2% | — | Not comparable |
| MCP AtlasSource | 84.2% | — | Not comparable |
| AutomationBenchSource | 30.8% | — | Not comparable |
| JobBenchSource | 52.9% | — | Not comparable |
| APEX-AgentsSource | 37.6% | — | Not comparable |
| SpreadsheetBench 2Source | 34.8% | — | Not comparable |
| DECK-BenchSource | 73.5% | — | Not comparable |
| AA Agentic IndexSource | 50.1% | — | Not comparable |
| GDPval-AASource | 59.0% | — | Not comparable |
| GDPval-AASource | 1679 | — | Not comparable |
| AA BriefcaseSource | 1543 | — | Not comparable |
| AA AutomationBenchSource | 52.7% | — | Not comparable |
| AA EnterpriseOps-GymSource | 45.3% | — | Not comparable |
| AA Harvey LABSource | 94.6% | — | Not comparable |
| AA Tau3 BankingSource | 33.4% | — | Not comparable |
| aaTerminalBench21Source | 85% | — | Not comparable |
| APEX-Agents-AASource | 41.3% | — | Not comparable |
| AA ITBenchSource | 47.7% | — | Not comparable |
Coding14 benchmarks
| Benchmark | Kimi K3 | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| deepSweSource | 67.5% | — | Not comparable |
| FrontierSWESource | 81.2% | — | Not comparable |
| ProgramBenchSource | 77.8% | — | Not comparable |
| Kimi Code Bench v2Source | 72.9% | — | Not comparable |
| sweMarathonSource | 42% | — | Not comparable |
| PostTrain BenchSource | 36.6% | — | Not comparable |
| MLS-Bench LiteSource | 48.3% | — | Not comparable |
| AA Coding IndexSource | 76.2% | — | Not comparable |
| AA-SciCodeSource | 58.7% | — | 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 wins10 benchmarks
| Benchmark | Kimi K3 | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| GPQASource | 93.5% | 95.5% | Sakana Fugu-Ultra leads |
| GPQA-DSource | 93.5% | 95.5% | Sakana Fugu-Ultra leads |
| HLESource | 56% | — | Not comparable |
| HLE w/o toolsSource | 43.5% | 50% | Sakana Fugu-Ultra leads |
| Artificial Analysis Intelligence IndexSource | 57.1% | — | Not comparable |
| AA-GPQA DiamondSource | 93.5% | — | Not comparable |
| AA-HLESource | 44.3% | — | Not comparable |
| AA-Omniscience IndexSource | 18.4% | — | Not comparable |
| AA-Omniscience AccuracySource | 46.0% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 50.9% | — | Not comparable |
MultimodalSakana Fugu-Ultra wins15 benchmarks
| Benchmark | Kimi K3 | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| OfficeQA ProSource | 63.3% | — | Not comparable |
| MMMU-ProSource | 81.6% | — | Not comparable |
| MMMU-Pro w/ PythonSource | 83.4% | — | Not comparable |
| CharXiv w/o toolsSource | 84.8% | — | Not comparable |
| CharXivSource | 91.3% | 86.6% | Kimi K3 leads |
| MathVisionSource | 94.3% | — | Not comparable |
| MathVision w/ PythonSource | 97.8% | — | Not comparable |
| BabyVision w/ PythonSource | 85.7% | — | Not comparable |
| ZeroBenchSource | 23.0% | — | Not comparable |
| ZeroBench w/ PythonSource | 41.0% | — | Not comparable |
| WorldVQA ForceAnswerSource | 51.0% | — | Not comparable |
| OmniDocBenchSource | 91.1% | — | Not comparable |
| PerceptionBenchSource | 58.5% | — | Not comparable |
| AA-MMMU-ProSource | 80.5% | — | Not comparable |
| Design Arena WebsiteSource | 1386 | — | Not comparable |
Frequently Asked Questions (4)
Which is better, Kimi K3 or Sakana Fugu-Ultra?
Kimi K3 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, Kimi K3 or Sakana Fugu-Ultra?
Sakana Fugu-Ultra has the edge for knowledge tasks in this comparison, averaging 95.5 versus 61. Inside this category, HLE w/o tools is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, Kimi K3 or Sakana Fugu-Ultra?
Kimi K3 has the edge for agentic tasks in this comparison, averaging 89.5 versus 82.1. 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, Kimi K3 or Sakana Fugu-Ultra?
Sakana Fugu-Ultra has the edge for multimodal and grounded tasks in this comparison, averaging 86.6 versus 78.5. Inside this category, CharXiv is the benchmark that creates the most daylight between them.
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