Model comparison
GLM-5 vs Sakana Fugu-Ultra
Head-to-head evidence from 4 shared benchmark results across 3 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5 #28 (Supported); Sakana Fugu-Ultra unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5 and Sakana Fugu-Ultra share 4 comparable benchmark results. 4 of 8 categories are comparable. 45 results are unique to GLM-5; 7 to Sakana Fugu-Ultra.
Updated July 18, 2026- Shared results
- 4
- GLM-5 only
- 45
- Sakana Fugu-Ultra only
- 7
- Comparable categories
- 4 / 8
Treat this as a split decision. GLM-5 makes more sense if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model; Sakana Fugu-Ultra is the better fit if reasoning is the priority or you need the larger 1M context window.
Confidence note. This is a partial-evidence comparison with 4 shared benchmark results across 3 evidence categories; 4 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
GLM-5 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 GLM-5 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 GLM-5.
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 | GLM-5 | Δ | Sakana Fugu-Ultra |
|---|---|---|---|
| Reasoning | GLM-560.8 | Margin→ 32.8 | Sakana Fugu-Ultra93.6 |
| Knowledge | GLM-566.4 | Margin→ 29.1 | Sakana Fugu-Ultra95.5 |
| Agentic | GLM-556.2 | Margin→ 25.9 | Sakana Fugu-Ultra82.1 |
| Coding | GLM-566.3 | Margin← 1.8 | Sakana Fugu-Ultra64.5 |
| Math | GLM-556.3 | MarginNo overlap | Sakana Fugu-UltraNot measured |
| Multilingual | GLM-583.1 | MarginNo overlap | Sakana Fugu-UltraNot measured |
| Multimodal | GLM-5Not measured | MarginNo overlap | Sakana Fugu-Ultra86.6 |
| Inst. Following | GLM-592.6 | MarginNo overlap | Sakana Fugu-UltraNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 56.2%B 82.1%Winner: Sakana Fugu-UltraΔ 25.9Terminal-Bench 2.0: GLM-5 scored 56.2%; Sakana Fugu-Ultra scored 82.1%. Sakana Fugu-Ultra wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 55.1%B 73.7%Winner: Sakana Fugu-UltraΔ 18.6SWE-bench Pro: GLM-5 scored 55.1%; Sakana Fugu-Ultra scored 73.7%. Sakana Fugu-Ultra wins this benchmark. - Source ↗
GPQA
KnowledgeA 86%B 95.5%Winner: Sakana Fugu-UltraΔ 9.5GPQA: GLM-5 scored 86%; 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 | GLM-5 | Sakana Fugu-Ultra | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 output | Sakana Fugu-UltraNot available | A complete price comparison is not available. |
| Generation speedtokens per second | GLM-574 tok/s | Sakana Fugu-UltraNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-51.64 s | Sakana Fugu-UltraNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5200K | Sakana Fugu-Ultra1M | Sakana Fugu-Ultra lists the larger context window. |
Benchmark Deep Dive
AgenticSakana Fugu-Ultra wins13 benchmarks
| Benchmark | GLM-5 | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 56.2% | 82.1% | Sakana Fugu-Ultra leads |
| Claw-EvalSource | 57.7% | — | Not comparable |
| QwenClawBenchSource | 54.1% | — | Not comparable |
| τ³-bench resultsSource | 65.6% | — | Not comparable |
| DeepPlanningSource | 14.6% | — | Not comparable |
| ToolathlonSource | 38% | — | Not comparable |
| MCP AtlasSource | 31.1% | — | Not comparable |
| MCP-TasksSource | 60.8% | — | Not comparable |
| WideResearchSource | 69.8% | — | Not comparable |
| τ²-bench resultsSource | 98.2% | — | Not comparable |
| CyberGymSource | 43.2% | — | Not comparable |
| APEX-Agents-AASource | 14.5% | — | Not comparable |
| Gert LabsSource | 50.99% | — | Not comparable |
CodingGLM-5 wins11 benchmarks
| Benchmark | GLM-5 | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 77.8% | — | Not comparable |
| SWE-bench Verified*Source | 72.8% | — | Not comparable |
| SWE-bench ProSource | 55.1% | 73.7% | Sakana Fugu-Ultra leads |
| SWE MultilingualSource | 73.3% | — | Not comparable |
| SWE-RebenchSource | 62.8% | — | Not comparable |
| React Native EvalsSource | 74.8% | — | Not comparable |
| AA-SciCodeSource | 46.2% | — | 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 |
ReasoningSakana Fugu-Ultra wins5 benchmarks
KnowledgeSakana Fugu-Ultra wins13 benchmarks
| Benchmark | GLM-5 | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| GPQASource | 86% | 95.5% | Sakana Fugu-Ultra leads |
| GPQA-DSource | 86.0% | 95.5% | Sakana Fugu-Ultra leads |
| SuperGPQASource | 66.8% | — | Not comparable |
| MMLU-ProSource | 85.7% | — | Not comparable |
| MMLU-Pro (Arcee)Source | 85.8% | — | Not comparable |
| HLESource | 50.4% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 39.5% | — | Not comparable |
| AA-GPQA DiamondSource | 82.0% | — | Not comparable |
| AA-HLESource | 27.2% | — | Not comparable |
| AA-Omniscience IndexSource | 2.0% | — | Not comparable |
| AA-Omniscience AccuracySource | 26.9% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 34.0% | — | Not comparable |
| HLE w/o toolsSource | — | 50% | Not comparable |
Math8 benchmarks
| Benchmark | GLM-5 | Sakana Fugu-Ultra | Result |
|---|---|---|---|
| AIME26Source | 95.8% | — | Not comparable |
| AIME25 (Arcee)Source | 93.3% | — | Not comparable |
| HMMT Feb 2025Source | 97.5% | — | Not comparable |
| HMMT Nov 2025Source | 96.9% | — | Not comparable |
| HMMT Feb 2026Source | 86.4% | — | Not comparable |
| MMAnswerBenchSource | 82.5% | — | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | 16.434% | — | Not comparable |
| FrontierMath v2 (Tier 4)Source | 2.100% | — | Not comparable |
Multilingual2 benchmarks
Multimodal2 benchmarks
Frequently Asked Questions (5)
Which is better, GLM-5 or Sakana Fugu-Ultra?
GLM-5 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, GLM-5 or Sakana Fugu-Ultra?
Sakana Fugu-Ultra has the edge for knowledge tasks in this comparison, averaging 95.5 versus 66.4. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Which is better for coding, GLM-5 or Sakana Fugu-Ultra?
GLM-5 has the edge for coding in this comparison, averaging 66.3 versus 64.5. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Which is better for reasoning, GLM-5 or Sakana Fugu-Ultra?
Sakana Fugu-Ultra has the edge for reasoning in this comparison, averaging 93.6 versus 60.8. GLM-5 stays close enough that the answer can still flip depending on your workload.
Which is better for agentic tasks, GLM-5 or Sakana Fugu-Ultra?
Sakana Fugu-Ultra has the edge for agentic tasks in this comparison, averaging 82.1 versus 56.2. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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