OpenAI o3 vs Kolibri-1
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Benchmark Scores Comparison
OpenAI o3
Kolibri-1
OpenAI o3
Score: 15.0
Kolibri-1
78.1B params
Coverage matrix
2 models × 50 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 50 benchmarks each model has scores for.
| Benchmark | Kolibri-1 Aleph Alpha | OpenAI o3 OpenAI |
|---|---|---|
| OmniScience Accuracy | 14.8 | — |
| GPQA Diamond | 84.3 | — |
| IFBench (prompt loose) | 78.1 | — |
| SealQA (12 distractors, <24k) | 61 | — |
| LiveCodeBench v6 | 85.9 | — |
| BFCL-V4 | 61.4 | — |
| AA-LCR | 68.3 | — |
| AIME 26 | 96 | — |
| FRAMES (<24k) | 71.2 | — |
| German Public Sector | 75 | — |
| OmniScience Non-Hallucination | 44 | — |
| Aerospace | 58.9 | — |
| Agentic Wiki QA (DE) | 69.4 | — |
| AIME 2025 (DE) | 87.5 | — |
| Overall (EN) | 75.5 | — |
| MuSiQue (EN) | 77.3 | — |
| SealQA (no distractors, >24k) | 100 | — |
| MMMLU | — | 88.8 |
| HumanEval+ | 92.7 | — |
| MMLU-ProX | 75.5 | — |
| BrowseComp | 29.4 | — |
| BFCL v4 (web search) | 62.5 | — |
| RGB Closed-Book | 51 | — |
| Industrial Drive Technology | 60 | — |
| AA-Omniscience Index (public set) | -32.8 | — |
| Humanity's Last Exam (DE) | 15.9 | — |
| SWE-bench Verified | 66.4 | — |
| Humanity's Last Exam | 21.5 | — |
| BFCL v4 (non-live AST) | 79.1 | — |
| BFCL v4 (live) | 78.9 | — |
| BFCL v4 (memory) | 62.8 | — |
| AIME 2026 (DE) | 90 | — |
| Automotive Supplier | 99 | — |
| GPQA Diamond (DE) | 81.3 | — |
| SQuAD (Utility Accuracy) | 83.4 | — |
| Terminal Bench 2.1 | 27.7 | — |
| AIME 2025 | 96.9 | — |
| BFCL v4 (multi-turn) | 47.5 | — |
| RGB Fact-Check (Error Correction) | 34 | — |
| SQuAD (M/A Grounding Score) | 23.4 | — |
| LongBenchPro | 64.5 | — |
| FRAMES (>24k) | 73 | — |
| Semiconductors | 80.4 | — |
| Honeypot | 80.8 | — |
| TauBench V3 Banking | 38.1 | — |
| RGB Negative (Abstention) | 85.6 | — |
| MMLU Pro COT | 80 | — |
| Overall (DE) | 70.8 | — |
| SealQA (12 distractors, >24k) | 65.1 | — |
| SealQA (no distractors, <24k) | 80.7 | — |
| Covered | 49/50 | 1/50 |
Scorecard
Overall 0–100 score — category strengths (50%), shared-benchmark wins (30%), coverage (20%)
Partial coverage benchmarks
Scored by at least one but not all selected models (50) — missing scores show as —
| Benchmark | Category | OpenAI o3 | Kolibri-1 |
|---|---|---|---|
| OmniScience Accuracy 1/2 models | knowledge | — | 14.8 |
| GPQA Diamond 1/2 models | stem_reasoning | — | 84.3 |
| IFBench (prompt loose) 1/2 models | instruction_following | — | 78.1 |
| SealQA (12 distractors, <24k) 1/2 models | agentic | — | 61 |
| LiveCodeBench v6 1/2 models | stem_reasoning | — | 85.9 |
| BFCL-V4 1/2 models | general_agent | — | 61.4 |
| AA-LCR 1/2 models | long_context | — | 68.3 |
| AIME 26 1/2 models | stem_reasoning | — | 96 |
| FRAMES (<24k) 1/2 models | agentic | — | 71.2 |
| German Public Sector 1/2 models | agentic | — | 75 |
| OmniScience Non-Hallucination 1/2 models | knowledge | — | 44 |
| 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 |
| MuSiQue (EN) 1/2 models | agentic | — | 77.3 |
| SealQA (no distractors, >24k) 1/2 models | agentic | — | 100 |
| MMMLU 1/2 models | multilingual | 88.8 | — |
| HumanEval+ 1/2 models | code | — | 92.7 |
| MMLU-ProX 1/2 models | multilingual | — | 75.5 |
| BrowseComp 1/2 models | general_agent | — | 29.4 |
| 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 |
| Humanity's Last Exam (DE) 1/2 models | stem_reasoning | — | 15.9 |
| SWE-bench Verified 1/2 models | coding_agent | — | 66.4 |
| Humanity's Last Exam 1/2 models | stem_reasoning | — | 21.5 |
| 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 |
| Automotive Supplier 1/2 models | agentic | — | 99 |
| GPQA Diamond (DE) 1/2 models | stem_reasoning | — | 81.3 |
| SQuAD (Utility Accuracy) 1/2 models | grounding_hallucination | — | 83.4 |
| 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 |
| 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 |
| Honeypot 1/2 models | agentic | — | 80.8 |
| TauBench V3 Banking 1/2 models | general_agent | — | 38.1 |
| RGB Negative (Abstention) 1/2 models | grounding_hallucination | — | 85.6 |
| 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 |