Model comparison
GLM-4.7 vs Ornith-1.0-9B
Head-to-head evidence from 2 shared benchmark results across 2 categories. Overall scores shown here use BenchLM's provisional ranking lane.
Verified leaderboard positions: GLM-4.7 #32; Ornith-1.0-9B unranked
Evidence parity. GLM-4.7 and Ornith-1.0-9B share 2 comparable benchmark results. 2 of 8 categories are comparable. 29 results are unique to GLM-4.7; 5 to Ornith-1.0-9B.
Updated July 14, 2026- Shared results
- 2
- GLM-4.7 only
- 29
- Ornith-1.0-9B only
- 5
- Comparable categories
- 2 / 8
Pick GLM-4.7 if you want the stronger benchmark profile. Ornith-1.0-9B only becomes the better choice if you need the larger 256K context window.
Confidence note. This is a partial-evidence comparison with 2 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-4.7 is clearly ahead on the provisional aggregate, 62 to 48. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-4.7's sharpest advantage is in coding, where it averages 73.8 against 52.8. The single biggest benchmark swing on the page is SWE-bench Verified, 73.8% to 69.4%.
Ornith-1.0-9B gives you the larger context window at 256K, compared with 200K for GLM-4.7.
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-4.7 | Δ | Ornith-1.0-9B |
|---|---|---|---|
| Coding | GLM-4.773.8 | Margin← 21.0 | Ornith-1.0-9B52.8 |
| Agentic | GLM-4.745.7 | Margin← 2.6 | Ornith-1.0-9B43.1 |
| Knowledge | GLM-4.752.1 | MarginNo overlap | Ornith-1.0-9BNot measured |
| Math | GLM-4.71.8 | MarginNo overlap | Ornith-1.0-9BNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
SWE-bench Verified
CodingA 73.8%B 69.4%Winner: GLM-4.7Δ 4.4SWE-bench Verified: GLM-4.7 scored 73.8%; Ornith-1.0-9B scored 69.4%. GLM-4.7 wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 41%B 43.1%Winner: Ornith-1.0-9BΔ 2.1Terminal-Bench 2.0: GLM-4.7 scored 41%; Ornith-1.0-9B scored 43.1%. Ornith-1.0-9B wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-4.7 | Ornith-1.0-9B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-4.7$0 input / $0 output | Ornith-1.0-9B$0 input / $0 output | Listed prices are equal. |
| Generation speedtokens per second | GLM-4.782 tok/s | Ornith-1.0-9BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-4.71.10 s | Ornith-1.0-9BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-4.7200K | Ornith-1.0-9B256K | Ornith-1.0-9B lists the larger context window. |
Benchmark Deep Dive
AgenticGLM-4.7 wins9 benchmarks
| Benchmark | GLM-4.7 | Ornith-1.0-9B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 41% | 43.1% | Ornith-1.0-9B leads |
| BrowseCompSource | 52% | — | Not comparable |
| VITA-BenchSource | 15.5% | — | Not comparable |
| AA Agentic IndexSource | 25.4% | — | Not comparable |
| Tau2-TelecomSource | 95.9% | — | Not comparable |
| Gert LabsSource | 39.95% | — | Not comparable |
| GDPval-AASource | 33.3% | — | Not comparable |
| GDPval-AASource | 1165 | — | Not comparable |
| Claw-EvalSource | — | 63.1% | Not comparable |
CodingGLM-4.7 wins11 benchmarks
| Benchmark | GLM-4.7 | Ornith-1.0-9B | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 73.8% | 69.4% | GLM-4.7 leads |
| LiveCodeBenchSource | 84.9% | — | Not comparable |
| SWE-RebenchSource | 58.7% | — | Not comparable |
| AA Coding IndexSource | 45.3% | — | Not comparable |
| Terminal-Bench HardSource | 31.8% | — | Not comparable |
| AA-SciCodeSource | 45.1% | — | Not comparable |
| AA LiveCodeBenchSource | 89.4% | — | 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
Knowledge9 benchmarks
| Benchmark | GLM-4.7 | Ornith-1.0-9B | Result |
|---|---|---|---|
| GPQASource | 85.7% | — | Not comparable |
| MMLU-ProSource | 84.3% | — | Not comparable |
| HLESource | 24.8% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 33.7% | — | Not comparable |
| AA-GPQA DiamondSource | 85.9% | — | Not comparable |
| AA-HLESource | 25.1% | — | Not comparable |
| AA-Omniscience IndexSource | -34.6% | — | Not comparable |
| AA-Omniscience AccuracySource | 29.3% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 90.3% | — | Not comparable |
Math3 benchmarks
Multimodal1 benchmarks
| Benchmark | GLM-4.7 | Ornith-1.0-9B | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1260 | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GLM-4.7 | Ornith-1.0-9B | Result |
|---|---|---|---|
| AA-IFBenchSource | 67.9% | — | Not comparable |
Frequently Asked Questions (3)
Which is better, GLM-4.7 or Ornith-1.0-9B?
GLM-4.7 is ahead on BenchLM's provisional leaderboard, 62 to 48. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 73.8% and 69.4%.
Which is better for coding, GLM-4.7 or Ornith-1.0-9B?
GLM-4.7 has the edge for coding in this comparison, averaging 73.8 versus 52.8. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GLM-4.7 or Ornith-1.0-9B?
GLM-4.7 has the edge for agentic tasks in this comparison, averaging 45.7 versus 43.1. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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