Model comparison
GLM-5 vs Ornith-1.0-9B
Head-to-head evidence from 5 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5 #26 (Supported); Ornith-1.0-9B unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5 and Ornith-1.0-9B share 5 comparable benchmark results. 2 of 8 categories are comparable. 45 results are unique to GLM-5; 2 to Ornith-1.0-9B.
Updated July 17, 2026- Shared results
- 5
- GLM-5 only
- 45
- Ornith-1.0-9B only
- 2
- Comparable categories
- 2 / 8
Pick GLM-5 if you want the stronger benchmark profile. Ornith-1.0-9B only becomes the better choice if you want the cheaper token bill or you need the larger 256K context window.
Confidence note. This is a partial-evidence comparison with 5 shared benchmark results across 2 evidence categories; 2 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 is clearly ahead on the BenchAlign aggregate, 65.98 to 49. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5's sharpest advantage is in agentic, where it averages 56.2 against 43.1. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 56.2% to 43.1%.
GLM-5 is also the more expensive model on tokens at $1.00 input / $3.20 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. Ornith-1.0-9B 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. Ornith-1.0-9B gives you the larger context window at 256K, 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 | Ornith-1.0-9B |
|---|---|---|---|
| Agentic | GLM-556.2 | Margin← 13.1 | Ornith-1.0-9B43.1 |
| Coding | GLM-566.3 | Margin← 7.1 | Ornith-1.0-9B59.2 |
| Reasoning | GLM-560.8 | MarginNo overlap | Ornith-1.0-9BNot measured |
| Knowledge | GLM-566.6 | MarginNo overlap | Ornith-1.0-9BNot measured |
| Math | GLM-556.3 | MarginNo overlap | Ornith-1.0-9BNot measured |
| Multilingual | GLM-583.1 | MarginNo overlap | Ornith-1.0-9BNot measured |
| Inst. Following | GLM-592.6 | 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 56.2%B 43.1%Winner: GLM-5Δ 13.1Terminal-Bench 2.0: GLM-5 scored 56.2%; Ornith-1.0-9B scored 43.1%. GLM-5 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 55.1%B 42.9%Winner: GLM-5Δ 12.2SWE-bench Pro: GLM-5 scored 55.1%; Ornith-1.0-9B scored 42.9%. GLM-5 wins this benchmark. - Source ↗
SWE-bench Verified
CodingA 77.8%B 69.4%Winner: GLM-5Δ 8.4SWE-bench Verified: GLM-5 scored 77.8%; Ornith-1.0-9B scored 69.4%. GLM-5 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5 | Ornith-1.0-9B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 output | Ornith-1.0-9B$0 input / $0 output | Ornith-1.0-9B has the lower combined listed price. |
| Generation speedtokens per second | GLM-574 tok/s | Ornith-1.0-9BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-51.64 s | Ornith-1.0-9BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5200K | Ornith-1.0-9B256K | Ornith-1.0-9B lists the larger context window. |
Benchmark Deep Dive
AgenticGLM-5 wins13 benchmarks
| Benchmark | GLM-5 | Ornith-1.0-9B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 56.2% | 43.1% | GLM-5 leads |
| Claw-EvalSource | 57.7% | 63.1% | Ornith-1.0-9B leads |
| 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 wins10 benchmarks
| Benchmark | GLM-5 | Ornith-1.0-9B | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 77.8% | 69.4% | GLM-5 leads |
| SWE-bench Verified*Source | 72.8% | — | Not comparable |
| SWE-bench ProSource | 55.1% | 42.9% | GLM-5 leads |
| SWE MultilingualSource | 73.3% | 52% | GLM-5 leads |
| SWE-RebenchSource | 62.8% | — | Not comparable |
| React Native EvalsSource | 74.8% | — | Not comparable |
| Terminal-Bench HardSource | 43.2% | — | Not comparable |
| AA-SciCodeSource | 46.2% | — | Not comparable |
| NL2RepoSource | — | 27.2% | Not comparable |
| Terminal-Bench 2.0Source | — | 43.1% | Not comparable |
Reasoning4 benchmarks
Knowledge12 benchmarks
| Benchmark | GLM-5 | Ornith-1.0-9B | Result |
|---|---|---|---|
| GPQASource | 86% | — | Not comparable |
| GPQA-DSource | 86.0% | — | Not comparable |
| 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 |
Math8 benchmarks
| Benchmark | GLM-5 | Ornith-1.0-9B | 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
Multimodal1 benchmarks
| Benchmark | GLM-5 | Ornith-1.0-9B | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1282 | — | Not comparable |
Frequently Asked Questions (3)
Which is better, GLM-5 or Ornith-1.0-9B?
GLM-5 is ahead on BenchLM's BenchAlign leaderboard, 65.98 to 49. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 56.2% and 43.1%.
Which is better for coding, GLM-5 or Ornith-1.0-9B?
GLM-5 has the edge for coding in this comparison, averaging 66.3 versus 59.2. Inside this category, SWE Multilingual is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GLM-5 or Ornith-1.0-9B?
GLM-5 has the edge for agentic tasks in this comparison, averaging 56.2 versus 43.1. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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