Model comparison
MiniMax M2.7 vs Sakana Fugu-Ultra
Head-to-head evidence from 3 shared benchmark results across 3 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: MiniMax M2.7 #36 (Supported); Sakana Fugu-Ultra unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. MiniMax M2.7 and Sakana Fugu-Ultra share 3 comparable benchmark results. 2 of 8 categories are comparable. 32 results are unique to MiniMax M2.7; 8 to Sakana Fugu-Ultra.
Updated July 23, 2026- Shared results
- 3
- MiniMax M2.7 only
- 32
- Sakana Fugu-Ultra only
- 8
- Comparable categories
- 2 / 8
Treat this as a split decision. MiniMax M2.7 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 agentic is the priority or you need the larger 1M context window.
Confidence note. This is a partial-evidence comparison with 3 shared benchmark results across 3 evidence categories; 2 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
MiniMax M2.7 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 MiniMax M2.7 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 200K for MiniMax M2.7.
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 | MiniMax M2.7 | Δ | Sakana Fugu-Ultra |
|---|---|---|---|
| Agentic | MiniMax M2.757.0 | Margin→ 25.1 | Sakana Fugu-Ultra82.1 |
| Coding | MiniMax M2.753.3 | Margin→ 11.2 | Sakana Fugu-Ultra64.5 |
| Reasoning | MiniMax M2.7Not measured | MarginNo overlap | Sakana Fugu-Ultra93.6 |
| Knowledge | MiniMax M2.7Not measured | MarginNo overlap | Sakana Fugu-Ultra95.5 |
| Multimodal | MiniMax M2.7Not measured | MarginNo overlap | Sakana Fugu-Ultra86.6 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 57%B 82.1%Winner: Sakana Fugu-UltraΔ 25.1Terminal-Bench 2.0: MiniMax M2.7 scored 57%; Sakana Fugu-Ultra scored 82.1%. Sakana Fugu-Ultra wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 56.2%B 73.7%Winner: Sakana Fugu-UltraΔ 17.5SWE-bench Pro: MiniMax M2.7 scored 56.2%; Sakana Fugu-Ultra scored 73.7%. Sakana Fugu-Ultra wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | MiniMax M2.7 | Sakana Fugu-Ultra | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | MiniMax M2.7$0.3 input / $1.2 output | Sakana Fugu-UltraNot available | A complete price comparison is not available. |
| Generation speedtokens per second | MiniMax M2.745 tok/s | Sakana Fugu-UltraNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | MiniMax M2.72.53 s | Sakana Fugu-UltraNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | MiniMax M2.7200K | Sakana Fugu-Ultra1M | Sakana Fugu-Ultra lists the larger context window. |
Benchmark Deep Dive
AgenticSakana Fugu-Ultra wins11 benchmarks
| Benchmark | MiniMax M2.7 | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 57% | 82.1% | Sakana Fugu-Ultra leads |
| τ²-bench resultsSource | 84.8% | — | Not comparable |
| ToolathlonSource | 46.3% | — | Not comparable |
| MLE-Bench LiteSource | 66.6% | — | Not comparable |
| MM-ClawBenchSource | 62.7% | — | Not comparable |
| Claw-EvalSource | 48.7% | — | Not comparable |
| AA Agentic IndexSource | 25.6% | — | Not comparable |
| APEX-Agents-AASource | 10.6% | — | Not comparable |
| GDPval-AASource | 32.9% | — | Not comparable |
| GDPval-AASource | 1158 | — | Not comparable |
| Gert LabsSource | 40.40% | — | Not comparable |
CodingSakana Fugu-Ultra wins15 benchmarks
| Benchmark | MiniMax M2.7 | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| SWE-bench Verified*Source | 75.4% | — | Not comparable |
| SWE-bench ProSource | 56.2% | 73.7% | Sakana Fugu-Ultra leads |
| SWE-RebenchSource | 51.9% | — | Not comparable |
| SWE MultilingualSource | 76.5% | — | Not comparable |
| Multi-SWE BenchSource | 52.7% | — | Not comparable |
| VIBE-ProSource | 55.6% | — | Not comparable |
| NL2RepoSource | 39.8% | — | Not comparable |
| Vibe Code BenchSource | 27.04% | — | Not comparable |
| React Native EvalsSource | 71.4% | — | Not comparable |
| AA Coding IndexSource | 52.6% | — | Not comparable |
| AA-SciCodeSource | 47.0% | — | 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
Knowledge10 benchmarks
| Benchmark | MiniMax M2.7 | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| GPQA-DSource | 87.0% | 95.5% | Sakana Fugu-Ultra leads |
| MMLU-Pro (Arcee)Source | 80.8% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 38.1% | — | Not comparable |
| AA-GPQA DiamondSource | 87.4% | — | Not comparable |
| AA-HLESource | 28.1% | — | Not comparable |
| AA-Omniscience IndexSource | 0.7% | — | Not comparable |
| AA-Omniscience AccuracySource | 26.1% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 34.4% | — | Not comparable |
| GPQASource | — | 95.5% | Not comparable |
| HLE w/o toolsSource | — | 50% | Not comparable |
Math1 benchmarks
| Benchmark | MiniMax M2.7 | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| AIME25 (Arcee)Source | 80.0% | — | Not comparable |
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | MiniMax M2.7 | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| AA-IFBenchSource | 75.7% | — | Not comparable |
Frequently Asked Questions (3)
Which is better, MiniMax M2.7 or Sakana Fugu-Ultra?
MiniMax M2.7 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 coding, MiniMax M2.7 or Sakana Fugu-Ultra?
Sakana Fugu-Ultra has the edge for coding in this comparison, averaging 64.5 versus 53.3. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, MiniMax M2.7 or Sakana Fugu-Ultra?
Sakana Fugu-Ultra has the edge for agentic tasks in this comparison, averaging 82.1 versus 57. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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