Model comparison
MiniMax M2.7 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: MiniMax M2.7 #39 (Supported); Sakana Fugu-Ultra v1.1 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 v1.1 share 0 comparable benchmark results. 0 of 8 categories are comparable. 35 results are unique to MiniMax M2.7; 0 to Sakana Fugu-Ultra v1.1.
Updated July 24, 2026- Shared results
- 0
- MiniMax M2.7 only
- 35
- Sakana Fugu-Ultra v1.1 only
- 0
- Comparable categories
- 0 / 8
Benchmark data for MiniMax M2.7 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 $0.30 input / $1.20 output per 1M tokens for MiniMax M2.7. Sakana Fugu-Ultra v1.1 has the larger context window at 1M, compared with 200K for MiniMax M2.7.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | MiniMax M2.7 | Sakana Fugu-Ultra v1.1 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | MiniMax M2.7$0.3 input / $1.2 output | Sakana Fugu-Ultra v1.1$5 input / $30 output | MiniMax M2.7 has the lower combined listed price. |
| Generation speedtokens per second | MiniMax M2.745 tok/s | Sakana Fugu-Ultra v1.1Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | MiniMax M2.72.53 s | Sakana Fugu-Ultra v1.1Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | MiniMax M2.7200K | Sakana Fugu-Ultra v1.11M | Sakana Fugu-Ultra v1.1 lists the larger context window. |
Benchmark Deep Dive
Agentic11 benchmarks
| Benchmark | MiniMax M2.7 | Sakana Fugu-Ultra v1.1 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 57% | — | Not comparable |
| 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 | 1159 | — | Not comparable |
| Gert LabsSource | 40.40% | — | Not comparable |
Coding11 benchmarks
| Benchmark | MiniMax M2.7 | Sakana Fugu-Ultra v1.1 | Result |
|---|---|---|---|
| SWE-bench Verified*Source | 75.4% | — | Not comparable |
| SWE-bench ProSource | 56.2% | — | Not comparable |
| 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 |
Reasoning2 benchmarks
Knowledge8 benchmarks
| Benchmark | MiniMax M2.7 | Sakana Fugu-Ultra v1.1 | Result |
|---|---|---|---|
| GPQA-DSource | 87.0% | — | Not comparable |
| 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 |
Math1 benchmarks
| Benchmark | MiniMax M2.7 | Sakana Fugu-Ultra v1.1 | Result |
|---|---|---|---|
| AIME25 (Arcee)Source | 80.0% | — | Not comparable |
Multimodal1 benchmarks
| Benchmark | MiniMax M2.7 | Sakana Fugu-Ultra v1.1 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1275 | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | MiniMax M2.7 | Sakana Fugu-Ultra v1.1 | Result |
|---|---|---|---|
| AA-IFBenchSource | 75.7% | — | Not comparable |
Frequently Asked Questions (3)
Can I compare MiniMax M2.7 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 MiniMax M2.7 and Sakana Fugu-Ultra v1.1 today?
MiniMax M2.7: $0.30 input / $1.20 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.