Claude Opus 5 vs Kolibri-1
New ComparisonScores are fetched live from the current database on every visit — nothing is cached.
Winner
Claude Opus 5
Claude Opus 5 scores higher overall across category strength profiles (91 vs 39 for Kolibri-1); only 2 benchmarks are directly comparable.
Claude Opus 5
90.7
Kolibri-1
39.4
Only 2 of 101 benchmarks are scored by every selected model — the verdict leans on category strength profiles and is indicative.
Benchmark Scores Comparison
Claude Opus 5
Kolibri-1
Claude Opus 5
Score: 40.0
Kolibri-1
78.1B params
Coverage matrix
2 models × 101 benchmarks — hover a benchmark name for its category
Shading = score relative to the best score among the selected models:
Low
Mid
High
Missing (—)
The totals row shows how many of the 101 benchmarks each model has scores for.
| Benchmark | Kolibri-1 Aleph Alpha | Claude Opus 5 Anthropic |
|---|---|---|
| OmniScience Accuracy | 14.8 | — |
| GPQA Diamond | 84.3 | 93.7 |
| IFBench (prompt loose) | 78.1 | — |
| SealQA (12 distractors, <24k) | 61 | — |
| ArXivMath | — | 71.5 |
| LiveCodeBench v6 | 85.9 | — |
| Hy-LifeSearch (Internal) | — | 56.1 |
| Automation-Bench | — | 48.7 |
| SWE-bench Pro | — | 79.2 |
| BFCL-V4 | 61.4 | — |
| BioMysteryBench | — | 72.1 |
| AA-LCR | 68.3 | — |
| AIME 26 | 96 | — |
| Agents' Last Exam | — | 25.1 |
| Toolathlon Verified | — | 76.5 |
| Hy-Backend 2.0 (Internal) | — | 40.3 |
| FRAMES (<24k) | 71.2 | — |
| German Public Sector | 75 | — |
| Hy-SWE Max Verified (Internal) | — | 70.1 |
| OmniScience Non-Hallucination | 44 | — |
| Hy-BrowseComp-Pro2 (Internal) | — | 61.3 |
| Aerospace | 58.9 | — |
| Agentic Wiki QA (DE) | 69.4 | — |
| AIME 2025 (DE) | 87.5 | — |
| Overall (EN) | 75.5 | — |
| OfficeQA Pro | — | 66.9 |
| SWE Atlas - TW | — | 68.7 |
| LOCA-Bench (256k) | — | 53.7 |
| MuSiQue (EN) | 77.3 | — |
| SealQA (no distractors, >24k) | 100 | — |
| SWE Atlas - QnA | — | 57.5 |
| NL2Repo | — | 75.3 |
| PostTrainBench | — | 35 |
| BabyVision | — | 94.1 |
| Apex-Agents | — | 41.8 |
| HumanEval+ | 92.7 | — |
| MMLU-ProX | 75.5 | — |
| WorkSpaceBench | — | 75 |
| BankerToolBench | — | 81.9 |
| E-Bench-Code (Internal) | — | 82.7 |
| HLE (with tools) | — | 60.9 |
| CharXiv (RQ) | — | 83.7 |
| GDP.pdf | — | 21.6 |
| CritPt (no tools) | — | 29.1 |
| BrowseComp | 29.4 | — |
| NL2Repo-Bench | — | 75.3 |
| DRACO | — | 88.6 |
| BFCL v4 (web search) | 62.5 | — |
| RGB Closed-Book | 51 | — |
| Industrial Drive Technology | 60 | — |
| AA-Omniscience Index (public set) | -32.8 | — |
| SWE-bench Multilingual | — | 89.5 |
| Humanity's Last Exam (DE) | 15.9 | — |
| Hy-FinAgentBench (Internal) | — | 82 |
| SWE-bench Verified | 66.4 | — |
| Humanity's Last Exam | 21.5 | 54.9 |
| BFCL v4 (non-live AST) | 79.1 | — |
| BFCL v4 (live) | 78.9 | — |
| BFCL v4 (memory) | 62.8 | — |
| AIME 2026 (DE) | 90 | — |
| SUPERChem | — | 76.7 |
| HorizonMath (pass@4) | — | 5.3 |
| Terminal-Bench 2.1 (Terminus-2) | — | 86.7 |
| Automotive Supplier | 99 | — |
| DeepSWE | — | 68.8 |
| GPQA Diamond (DE) | 81.3 | — |
| BrokenArXiv | — | 77.7 |
| SQuAD (Utility Accuracy) | 83.4 | — |
| Hy-FinmodelBench v2 (Internal) | — | 66 |
| MathArena Apex 2025 | — | 91.4 |
| Terminal Bench 2.1 | 27.7 | — |
| AIME 2025 | 96.9 | — |
| BFCL v4 (multi-turn) | 47.5 | — |
| RGB Fact-Check (Error Correction) | 34 | — |
| JobBench | — | 68 |
| $OneMillion-Bench | — | 68.1 |
| SQuAD (M/A Grounding Score) | 23.4 | — |
| LongBenchPro | 64.5 | — |
| FRAMES (>24k) | 73 | — |
| Semiconductors | 80.4 | — |
| OSWorld 2.0 (Binary) | — | 70.6 |
| Honeypot | 80.8 | — |
| WideSearch | — | 84 |
| SkillsBench Avg5 | — | 63.7 |
| TauBench V3 Banking | 38.1 | — |
| E-Bench (Internal) | — | 77.8 |
| ProgramBench | — | 39.5 |
| MCP-Atlas | — | 85.7 |
| SWE-Marathon | — | 50 |
| Terminal-Bench 4.0 | — | 51.8 |
| SWE Atlas - RF | — | 60 |
| DeepSWE 1.1 | — | 74 |
| Hy-CompanyBench V2 (Internal) | — | 72.7 |
| RGB Negative (Abstention) | 85.6 | — |
| Harbor-Index | — | 56.9 |
| SWE Refactor Bench | — | 31 |
| MMLU Pro COT | 80 | — |
| Overall (DE) | 70.8 | — |
| SealQA (12 distractors, >24k) | 65.1 | — |
| SealQA (no distractors, <24k) | 80.7 | — |
| GDPVal-AA v2 | — | 1831 |
| Covered | 49/101 | 54/101 |
Scorecard
Overall 0–100 score — category strengths (50%), shared-benchmark wins (30%), coverage (20%)
| Model | Overall score | Shared benchmark wins | Coverage | Strongest at | Weakest at |
|---|---|---|---|---|---|
| Claude Opus 5 Winner | 90.7 | 2 of 2 | 54 of 101 (54.0%) | STEM & Reasoning | STEM & Reasoning |
| Kolibri-1 | 39.4 | 0 of 2 | 49 of 101 (48.0%) | STEM & Reasoning | STEM & Reasoning |
Category strengths
Where each model stands, per benchmark category — score relative to the best model, adjusted for benchmark coverage. This also ranks models when they share few benchmarks.
| Category | Claude Opus 5 | Kolibri-1 |
|---|---|---|
| STEM & Reasoning 19 benchmarks | 100 | 59.4 |
Fully comparable benchmarks
Scored by all 2 selected models (2)
| Benchmark | Category | Claude Opus 5 | Kolibri-1 |
|---|---|---|---|
| GPQA Diamond | stem_reasoning | 93.7 | 84.3 |
| Humanity's Last Exam | stem_reasoning | 54.9 | 21.5 |
Partial coverage benchmarks
Scored by at least one but not all selected models (99) — missing scores show as —
| Benchmark | Category | Claude Opus 5 | Kolibri-1 |
|---|---|---|---|
| OmniScience Accuracy 1/2 models | knowledge | — | 14.8 |
| IFBench (prompt loose) 1/2 models | instruction_following | — | 78.1 |
| SealQA (12 distractors, <24k) 1/2 models | agentic | — | 61 |
| ArXivMath 1/2 models | stem_reasoning | 71.5 | — |
| LiveCodeBench v6 1/2 models | stem_reasoning | — | 85.9 |
| Hy-LifeSearch (Internal) 1/2 models | general_agent | 56.1 | — |
| Automation-Bench 1/2 models | general_agent | 48.7 | — |
| SWE-bench Pro 1/2 models | coding_agent | 79.2 | — |
| BFCL-V4 1/2 models | general_agent | — | 61.4 |
| BioMysteryBench 1/2 models | stem_reasoning | 72.1 | — |
| AA-LCR 1/2 models | long_context | — | 68.3 |
| AIME 26 1/2 models | stem_reasoning | — | 96 |
| Agents' Last Exam 1/2 models | general_agent | 25.1 | — |
| Toolathlon Verified 1/2 models | general_agent | 76.5 | — |
| Hy-Backend 2.0 (Internal) 1/2 models | coding_agent | 40.3 | — |
| FRAMES (<24k) 1/2 models | agentic | — | 71.2 |
| German Public Sector 1/2 models | agentic | — | 75 |
| Hy-SWE Max Verified (Internal) 1/2 models | coding_agent | 70.1 | — |
| OmniScience Non-Hallucination 1/2 models | knowledge | — | 44 |
| Hy-BrowseComp-Pro2 (Internal) 1/2 models | general_agent | 61.3 | — |
| Aerospace 1/2 models | agentic | — | 58.9 |
| Agentic Wiki QA (DE) 1/2 models | agentic | — | 69.4 |
| AIME 2025 (DE) 1/2 models | stem_reasoning | — | 87.5 |
| Overall (EN) 1/2 models | stem_reasoning | — | 75.5 |
| OfficeQA Pro 1/2 models | general_agent | 66.9 | — |
| SWE Atlas - TW 1/2 models | coding_agent | 68.7 | — |
| LOCA-Bench (256k) 1/2 models | long_context | 53.7 | — |
| MuSiQue (EN) 1/2 models | agentic | — | 77.3 |
| SealQA (no distractors, >24k) 1/2 models | agentic | — | 100 |
| SWE Atlas - QnA 1/2 models | coding_agent | 57.5 | — |
| NL2Repo 1/2 models | coding_agent | 75.3 | — |
| PostTrainBench 1/2 models | coding_agent | 35 | — |
| BabyVision 1/2 models | vision_language | 94.1 | — |
| Apex-Agents 1/2 models | general_agent | 41.8 | — |
| HumanEval+ 1/2 models | code | — | 92.7 |
| MMLU-ProX 1/2 models | multilingual | — | 75.5 |
| WorkSpaceBench 1/2 models | general_agent | 75 | — |
| BankerToolBench 1/2 models | general_agent | 81.9 | — |
| E-Bench-Code (Internal) 1/2 models | coding_agent | 82.7 | — |
| HLE (with tools) 1/2 models | stem_reasoning | 60.9 | — |
| CharXiv (RQ) 1/2 models | document_understanding | 83.7 | — |
| GDP.pdf 1/2 models | long_context | 21.6 | — |
| CritPt (no tools) 1/2 models | stem_reasoning | 29.1 | — |
| BrowseComp 1/2 models | general_agent | — | 29.4 |
| NL2Repo-Bench 1/2 models | coding_agent | 75.3 | — |
| DRACO 1/2 models | general_agent | 88.6 | — |
| BFCL v4 (web search) 1/2 models | agentic | — | 62.5 |
| RGB Closed-Book 1/2 models | grounding_hallucination | — | 51 |
| Industrial Drive Technology 1/2 models | agentic | — | 60 |
| AA-Omniscience Index (public set) 1/2 models | knowledge | — | -32.8 |
| SWE-bench Multilingual 1/2 models | coding_agent | 89.5 | — |
| Humanity's Last Exam (DE) 1/2 models | stem_reasoning | — | 15.9 |
| Hy-FinAgentBench (Internal) 1/2 models | domain_finance | 82 | — |
| SWE-bench Verified 1/2 models | coding_agent | — | 66.4 |
| BFCL v4 (non-live AST) 1/2 models | agentic | — | 79.1 |
| BFCL v4 (live) 1/2 models | agentic | — | 78.9 |
| BFCL v4 (memory) 1/2 models | agentic | — | 62.8 |
| AIME 2026 (DE) 1/2 models | stem_reasoning | — | 90 |
| SUPERChem 1/2 models | stem_reasoning | 76.7 | — |
| HorizonMath (pass@4) 1/2 models | stem_reasoning | 5.3 | — |
| Terminal-Bench 2.1 (Terminus-2) 1/2 models | coding_agent | 86.7 | — |
| Automotive Supplier 1/2 models | agentic | — | 99 |
| DeepSWE 1/2 models | coding_agent | 68.8 | — |
| GPQA Diamond (DE) 1/2 models | stem_reasoning | — | 81.3 |
| BrokenArXiv 1/2 models | stem_reasoning | 77.7 | — |
| SQuAD (Utility Accuracy) 1/2 models | grounding_hallucination | — | 83.4 |
| Hy-FinmodelBench v2 (Internal) 1/2 models | domain_finance | 66 | — |
| MathArena Apex 2025 1/2 models | stem_reasoning | 91.4 | — |
| Terminal Bench 2.1 1/2 models | coding_agent | — | 27.7 |
| AIME 2025 1/2 models | stem_reasoning | — | 96.9 |
| BFCL v4 (multi-turn) 1/2 models | agentic | — | 47.5 |
| RGB Fact-Check (Error Correction) 1/2 models | grounding_hallucination | — | 34 |
| JobBench 1/2 models | general_agent | 68 | — |
| $OneMillion-Bench 1/2 models | general_capabilities | 68.1 | — |
| SQuAD (M/A Grounding Score) 1/2 models | grounding_hallucination | — | 23.4 |
| LongBenchPro 1/2 models | long_context | — | 64.5 |
| FRAMES (>24k) 1/2 models | agentic | — | 73 |
| Semiconductors 1/2 models | agentic | — | 80.4 |
| OSWorld 2.0 (Binary) 1/2 models | general_agent | 70.6 | — |
| Honeypot 1/2 models | agentic | — | 80.8 |
| WideSearch 1/2 models | general_agent | 84 | — |
| SkillsBench Avg5 1/2 models | coding_agent | 63.7 | — |
| TauBench V3 Banking 1/2 models | general_agent | — | 38.1 |
| E-Bench (Internal) 1/2 models | general_agent | 77.8 | — |
| ProgramBench 1/2 models | coding_agent | 39.5 | — |
| MCP-Atlas 1/2 models | general_agent | 85.7 | — |
| SWE-Marathon 1/2 models | coding_agent | 50 | — |
| Terminal-Bench 4.0 1/2 models | coding_agent | 51.8 | — |
| SWE Atlas - RF 1/2 models | coding_agent | 60 | — |
| DeepSWE 1.1 1/2 models | coding_agent | 74 | — |
| Hy-CompanyBench V2 (Internal) 1/2 models | general_agent | 72.7 | — |
| RGB Negative (Abstention) 1/2 models | grounding_hallucination | — | 85.6 |
| Harbor-Index 1/2 models | coding_agent | 56.9 | — |
| SWE Refactor Bench 1/2 models | coding_agent | 31 | — |
| MMLU Pro COT 1/2 models | knowledge | — | 80 |
| Overall (DE) 1/2 models | stem_reasoning | — | 70.8 |
| SealQA (12 distractors, >24k) 1/2 models | agentic | — | 65.1 |
| SealQA (no distractors, <24k) 1/2 models | agentic | — | 80.7 |
| GDPVal-AA v2 1/2 models | general_agent | 1831 | — |
Comparison FAQ
Which model is better, Claude Opus 5 or Kolibri-1?
Claude Opus 5 scores higher overall across category strength profiles (91 vs 39 for Kolibri-1); only 2 benchmarks are directly comparable.
Which model is better at STEM & Reasoning, Claude Opus 5 or Kolibri-1?
Claude Opus 5 leads the STEM & Reasoning category (strength score 100/100 across 2 comparable benchmarks).
How reliable is this comparison verdict?
Only 2 of 101 benchmarks are scored by every selected model — the verdict leans on category strength profiles and is indicative.