Skip to main content

Model comparison

GLM-4.7 vs Ornith-1.0-9B

Data verified

Head-to-head evidence from 2 shared benchmark results across 2 categories. Overall scores shown here use BenchLM's provisional ranking lane.

62/100
Margin
14.0pts
← winning
DeepReinforce AI
48/100
2 category wins0 category wins

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 scores and score margins for GLM-4.7 and Ornith-1.0-9B
CategoryGLM-4.7ΔOrnith-1.0-9B
CodingGLM-4.773.8Margin 21.0Ornith-1.0-9B52.8
AgenticGLM-4.745.7Margin 2.6Ornith-1.0-9B43.1
KnowledgeGLM-4.752.1MarginNo overlapOrnith-1.0-9BNot measured
MathGLM-4.71.8MarginNo overlapOrnith-1.0-9BNot measured

Decisive benchmark drivers

The largest measured benchmark gaps in this matchup, with exact reported values.

More
A · GLM-4.7B · Ornith-1.0-9B
  1. SWE-bench Verified

    Coding
    Source ↗
    A 73.8%B 69.4%
    Winner: GLM-4.7Δ 4.4
    SWE-bench Verified: GLM-4.7 scored 73.8%; Ornith-1.0-9B scored 69.4%. GLM-4.7 wins this benchmark.
  2. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 41%B 43.1%
    Winner: Ornith-1.0-9BΔ 2.1
    Terminal-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.

MetricGLM-4.7Ornith-1.0-9BComparison
Input / output priceUSD per 1M tokensGLM-4.7$0 input / $0 outputOrnith-1.0-9B$0 input / $0 outputListed prices are equal.
Generation speedtokens per secondGLM-4.782 tok/sOrnith-1.0-9BNot availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenGLM-4.71.10 sOrnith-1.0-9BNot availableA complete latency comparison is not available.
Context windowmaximum listed tokensGLM-4.7200KOrnith-1.0-9B256KOrnith-1.0-9B lists the larger context window.

Benchmark Deep Dive

AgenticGLM-4.7 wins
BenchmarkGLM-4.7Ornith-1.0-9BResult
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 1165Not comparable
Claw-EvalSource 63.1%Not comparable
CodingGLM-4.7 wins
BenchmarkGLM-4.7Ornith-1.0-9BResult
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
Reasoning
BenchmarkGLM-4.7Ornith-1.0-9BResult
AA-LCRSource 64.0%Not comparable
CritPtSource 1.7%Not comparable
Knowledge
BenchmarkGLM-4.7Ornith-1.0-9BResult
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
Math
BenchmarkGLM-4.7Ornith-1.0-9BResult
AIME 2025Source 95.7%Not comparable
FrontierMath v2 (Tiers 1-3)Source 2.439%Not comparable
FrontierMath v2 (Tier 4)Source 0.000%Not comparable
Multimodal
BenchmarkGLM-4.7Ornith-1.0-9BResult
Design Arena WebsiteSource 1260Not comparable
Inst. Following
BenchmarkGLM-4.7Ornith-1.0-9BResult
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.

Related Comparisons

Last updated: July 14, 2026

The AI models change fast. We track them for you.

A weekly brief for engineers and researchers covering new models, ranking shifts, and pricing changes.

Free. No spam. Unsubscribe anytime.