Model comparison
Kimi K3 vs Ornith-1.0-9B
Head-to-head evidence from 1 shared benchmark result across 1 category. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Kimi K3 #4 (Supported); Ornith-1.0-9B unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Kimi K3 and Ornith-1.0-9B share 1 comparable benchmark result. 1 of 8 categories are comparable. 56 results are unique to Kimi K3; 6 to Ornith-1.0-9B.
Updated July 23, 2026- Shared results
- 1
- Kimi K3 only
- 56
- Ornith-1.0-9B only
- 6
- Comparable categories
- 1 / 8
Treat this as a split decision. Kimi K3 makes more sense if agentic is the priority or you need the larger 1.05M context window; Ornith-1.0-9B is the better fit if you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 1 shared benchmark result across 1 evidence category; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Kimi K3 and Ornith-1.0-9B 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.
Kimi K3 is also the more expensive model on tokens at $3.00 input / $15.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Ornith-1.0-9B. That is roughly Infinityx on output cost alone. Kimi K3 gives you the larger context window at 1.05M, compared with 256K for Ornith-1.0-9B.
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 | Kimi K3 | Δ | Ornith-1.0-9B |
|---|---|---|---|
| Agentic | Kimi K389.5 | Margin← 46.4 | Ornith-1.0-9B43.1 |
| Coding | Kimi K3Not measured | MarginNo overlap | Ornith-1.0-9B59.2 |
| Knowledge | Kimi K361.0 | MarginNo overlap | Ornith-1.0-9BNot measured |
| Multimodal | Kimi K378.5 | MarginNo overlap | Ornith-1.0-9BNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 88.3%B 43.1%Winner: Kimi K3Δ 45.2Terminal-Bench 2.0: Kimi K3 scored 88.3%; Ornith-1.0-9B scored 43.1%. Kimi K3 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Kimi K3 | Ornith-1.0-9B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Kimi K3$3 input / $15 output | Ornith-1.0-9B$0 input / $0 output | Ornith-1.0-9B has the lower combined listed price. |
| Generation speedtokens per second | Kimi K3Not available | Ornith-1.0-9BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Kimi K3Not available | Ornith-1.0-9BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Kimi K31.05M | Ornith-1.0-9B256K | Kimi K3 lists the larger context window. |
Benchmark Deep Dive
AgenticKimi K3 wins22 benchmarks
| Benchmark | Kimi K3 | Ornith-1.0-9B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 88.3% | 43.1% | Kimi K3 leads |
| BrowseCompSource | 91.2% | — | Not comparable |
| DeepSearchQASource | 95.0% | — | Not comparable |
| Toolathlon-VerifiedSource | 73.2% | — | Not comparable |
| MCP AtlasSource | 84.2% | — | Not comparable |
| AutomationBenchSource | 30.8% | — | Not comparable |
| JobBenchSource | 52.9% | — | Not comparable |
| APEX-AgentsSource | 37.6% | — | Not comparable |
| SpreadsheetBench 2Source | 34.8% | — | Not comparable |
| DECK-BenchSource | 73.5% | — | Not comparable |
| AA Agentic IndexSource | 50.1% | — | Not comparable |
| GDPval-AASource | 59.0% | — | Not comparable |
| GDPval-AASource | 1679 | — | Not comparable |
| AA BriefcaseSource | 1543 | — | Not comparable |
| AA AutomationBenchSource | 52.7% | — | Not comparable |
| AA EnterpriseOps-GymSource | 45.3% | — | Not comparable |
| AA Harvey LABSource | 94.6% | — | Not comparable |
| AA Tau3 BankingSource | 33.4% | — | Not comparable |
| aaTerminalBench21Source | 85% | — | Not comparable |
| APEX-Agents-AASource | 41.3% | — | Not comparable |
| AA ITBenchSource | 47.7% | — | Not comparable |
| Claw-EvalSource | — | 63.1% | Not comparable |
Coding14 benchmarks
| Benchmark | Kimi K3 | Ornith-1.0-9B | Result |
|---|---|---|---|
| deepSweSource | 67.5% | — | Not comparable |
| FrontierSWESource | 81.2% | — | Not comparable |
| ProgramBenchSource | 77.8% | — | Not comparable |
| Kimi Code Bench v2Source | 72.9% | — | Not comparable |
| sweMarathonSource | 42% | — | Not comparable |
| PostTrain BenchSource | 36.6% | — | Not comparable |
| MLS-Bench LiteSource | 48.3% | — | Not comparable |
| AA Coding IndexSource | 76.2% | — | Not comparable |
| AA-SciCodeSource | 58.7% | — | Not comparable |
| SWE-bench VerifiedSource | — | 69.4% | Not comparable |
| SWE-bench ProSource | — | 42.9% | Not comparable |
| SWE MultilingualSource | — | 52% | Not comparable |
| NL2RepoSource | — | 27.2% | Not comparable |
| Terminal-Bench 2.0Source | — | 43.1% | Not comparable |
Reasoning2 benchmarks
Knowledge10 benchmarks
| Benchmark | Kimi K3 | Ornith-1.0-9B | Result |
|---|---|---|---|
| GPQASource | 93.5% | — | Not comparable |
| GPQA-DSource | 93.5% | — | Not comparable |
| HLESource | 56% | — | Not comparable |
| HLE w/o toolsSource | 43.5% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 57.1% | — | Not comparable |
| AA-GPQA DiamondSource | 93.5% | — | Not comparable |
| AA-HLESource | 44.3% | — | Not comparable |
| AA-Omniscience IndexSource | 18.4% | — | Not comparable |
| AA-Omniscience AccuracySource | 46.0% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 50.9% | — | Not comparable |
Multimodal15 benchmarks
| Benchmark | Kimi K3 | Ornith-1.0-9B | Result |
|---|---|---|---|
| OfficeQA ProSource | 63.3% | — | Not comparable |
| MMMU-ProSource | 81.6% | — | Not comparable |
| MMMU-Pro w/ PythonSource | 83.4% | — | Not comparable |
| CharXiv w/o toolsSource | 84.8% | — | Not comparable |
| CharXivSource | 91.3% | — | Not comparable |
| MathVisionSource | 94.3% | — | Not comparable |
| MathVision w/ PythonSource | 97.8% | — | Not comparable |
| BabyVision w/ PythonSource | 85.7% | — | Not comparable |
| ZeroBenchSource | 23.0% | — | Not comparable |
| ZeroBench w/ PythonSource | 41.0% | — | Not comparable |
| WorldVQA ForceAnswerSource | 51.0% | — | Not comparable |
| OmniDocBenchSource | 91.1% | — | Not comparable |
| PerceptionBenchSource | 58.5% | — | Not comparable |
| AA-MMMU-ProSource | 80.5% | — | Not comparable |
| Design Arena WebsiteSource | 1386 | — | Not comparable |
Frequently Asked Questions (2)
Which is better, Kimi K3 or Ornith-1.0-9B?
Kimi K3 and Ornith-1.0-9B 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 agentic tasks, Kimi K3 or Ornith-1.0-9B?
Kimi K3 has the edge for agentic tasks in this comparison, averaging 89.5 versus 43.1. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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