Model comparison

GLM-5 vs Ornith-1.0-397B

Data verified July 18, 2026

Head-to-head evidence from 5 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

GLM-5

Z.AI

66.06/100

No comparison

Ornith-1.0-397B

DeepReinforce AI

N/A

0 category wins2 category wins

Public leaderboard positions: GLM-5 #28 (Supported); Ornith-1.0-397B unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. GLM-5 and Ornith-1.0-397B share 5 comparable benchmark results. 2 of 8 categories are comparable. 44 results are unique to GLM-5; 2 to Ornith-1.0-397B.

Updated July 18, 2026

Shared results: 5
GLM-5 only: 44
Ornith-1.0-397B only: 2
Comparable categories: 2 / 8

Treat this as a split decision. GLM-5 makes more sense if you would rather avoid the extra latency and token burn of a reasoning model; Ornith-1.0-397B is the better fit if agentic is the priority or you want the cheaper token bill.

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 and Ornith-1.0-397B 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.

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-1.0-397B. That is roughly Infinityx on output cost alone. Ornith-1.0-397B 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-1.0-397B 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 scores and score margins for GLM-5 and Ornith-1.0-397B
Category	GLM-5	Δ	Ornith-1.0-397B
Agentic	GLM-556.2	Margin→ 21.3	Ornith-1.0-397B77.5
Coding	GLM-566.3	Margin→ 8.3	Ornith-1.0-397B74.6
Reasoning	GLM-560.8	MarginNo overlap	Ornith-1.0-397BNot measured
Knowledge	GLM-566.4	MarginNo overlap	Ornith-1.0-397BNot measured
Math	GLM-556.3	MarginNo overlap	Ornith-1.0-397BNot measured
Multilingual	GLM-583.1	MarginNo overlap	Ornith-1.0-397BNot measured
Inst. Following	GLM-592.6	MarginNo overlap	Ornith-1.0-397BNot measured

Decisive benchmark drivers

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

A · GLM-5B · Ornith-1.0-397B

Terminal-Bench 2.0
Agentic
Source ↗
A 56.2%B 77.5%
Winner: Ornith-1.0-397BΔ 21.3
Terminal-Bench 2.0: GLM-5 scored 56.2%; Ornith-1.0-397B scored 77.5%. Ornith-1.0-397B wins this benchmark.
SWE-bench Pro
Coding
Source ↗
A 55.1%B 62.2%
Winner: Ornith-1.0-397BΔ 7.1
SWE-bench Pro: GLM-5 scored 55.1%; Ornith-1.0-397B scored 62.2%. Ornith-1.0-397B wins this benchmark.
SWE-bench Verified
Coding
Source ↗
A 77.8%B 82.4%
Winner: Ornith-1.0-397BΔ 4.6
SWE-bench Verified: GLM-5 scored 77.8%; Ornith-1.0-397B scored 82.4%. Ornith-1.0-397B wins this benchmark.

Operational comparison

Runtime and commercial metrics are compared only when both models have a complete sourced value.

Metric	GLM-5	Ornith-1.0-397B	Comparison
Input / output priceUSD per 1M tokens	GLM-5$1 input / $3.2 output	Ornith-1.0-397B$0 input / $0 output	Ornith-1.0-397B has the lower combined listed price.
Generation speedtokens per second	GLM-574 tok/s	Ornith-1.0-397BNot available	A complete speed comparison is not available.
First-answer latencyseconds to first token	GLM-51.64 s	Ornith-1.0-397BNot available	A complete latency comparison is not available.
Context windowmaximum listed tokens	GLM-5200K	Ornith-1.0-397B256K	Ornith-1.0-397B lists the larger context window.

Benchmark Deep Dive

AgenticOrnith-1.0-397B wins

13 benchmarks

Benchmark	GLM-5	Ornith-1.0-397B	Result
Terminal-Bench 2.0Source	56.2%	77.5%	Ornith-1.0-397B leads
Claw-EvalSource	57.7%	77.1%	Ornith-1.0-397B 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

CodingOrnith-1.0-397B wins

9 benchmarks

Benchmark	GLM-5	Ornith-1.0-397B	Result
SWE-bench VerifiedSource	77.8%	82.4%	Ornith-1.0-397B leads
SWE-bench Verified*Source	72.8%	—	Not comparable
SWE-bench ProSource	55.1%	62.2%	Ornith-1.0-397B leads
SWE MultilingualSource	73.3%	78.9%	Ornith-1.0-397B leads
SWE-RebenchSource	62.8%	—	Not comparable
React Native EvalsSource	74.8%	—	Not comparable
AA-SciCodeSource	46.2%	—	Not comparable
NL2RepoSource	—	48.2%	Not comparable
Terminal-Bench 2.0Source	—	77.5%	Not comparable

Reasoning

4 benchmarks

Benchmark	GLM-5	Ornith-1.0-397B	Result
LongBench v2Source	60.8%	—	Not comparable
AI-NeedleSource	63.3%	—	Not comparable
AA-LCRSource	63.3%	—	Not comparable
CritPtSource	2.0%	—	Not comparable

Knowledge

12 benchmarks

Benchmark	GLM-5	Ornith-1.0-397B	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

Math

8 benchmarks

Benchmark	GLM-5	Ornith-1.0-397B	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

Multilingual

2 benchmarks

Benchmark	GLM-5	Ornith-1.0-397B	Result
MMLU-ProXSource	83.1%	—	Not comparable
NOVA-63Source	55.1%	—	Not comparable

Multimodal

1 benchmarks

Benchmark	GLM-5	Ornith-1.0-397B	Result
Design Arena WebsiteSource	1280	—	Not comparable

Inst. Following

2 benchmarks

Benchmark	GLM-5	Ornith-1.0-397B	Result
IFEvalSource	92.6%	—	Not comparable
AA-IFBenchSource	72.3%	—	Not comparable

Frequently Asked Questions (3)

Which is better, GLM-5 or Ornith-1.0-397B?

GLM-5 and Ornith-1.0-397B 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 coding, GLM-5 or Ornith-1.0-397B?

Ornith-1.0-397B has the edge for coding in this comparison, averaging 74.6 versus 66.3. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.

Which is better for agentic tasks, GLM-5 or Ornith-1.0-397B?

Ornith-1.0-397B has the edge for agentic tasks in this comparison, averaging 77.5 versus 56.2. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.

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Last updated: July 18, 2026

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