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Model comparison

Claude 4.1 Opus vs GLM-5

Data verified

Head-to-head evidence from 3 shared benchmark results across 3 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

45.88/100
Margin
20.2pts
winning →
GLM-5
Z.AI
66.06/100
1 category wins0 category wins

Public leaderboard positions: Claude 4.1 Opus #142 (Supported); GLM-5 #28 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. Claude 4.1 Opus and GLM-5 share 3 comparable benchmark results. 1 of 8 categories are comparable. 1 result is unique to Claude 4.1 Opus; 46 to GLM-5.

Updated July 20, 2026
Shared results
3
Claude 4.1 Opus only
1
GLM-5 only
46
Comparable categories
1 / 8

Pick GLM-5 if you want the stronger benchmark profile. Claude 4.1 Opus only becomes the better choice if coding is the priority.

Confidence note. This is a partial-evidence comparison with 3 shared benchmark results across 3 evidence categories; 1 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, 66.06 to 45.88. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

Claude 4.1 Opus is also the more expensive model on tokens at $15.00 input / $75.00 output per 1M tokens, versus $1.00 input / $3.20 output per 1M tokens for GLM-5. That is roughly 23.4x on output cost alone.

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 Claude 4.1 Opus and GLM-5
CategoryClaude 4.1 OpusΔGLM-5
CodingClaude 4.1 Opus74.5Margin← 8.2GLM-566.3
AgenticClaude 4.1 OpusNot measuredMarginNo overlapGLM-556.2
ReasoningClaude 4.1 OpusNot measuredMarginNo overlapGLM-560.8
KnowledgeClaude 4.1 OpusNot measuredMarginNo overlapGLM-566.4
MathClaude 4.1 OpusNot measuredMarginNo overlapGLM-556.3
MultilingualClaude 4.1 OpusNot measuredMarginNo overlapGLM-583.1
Inst. FollowingClaude 4.1 OpusNot measuredMarginNo overlapGLM-592.6

Decisive benchmark drivers

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

More
A · Claude 4.1 OpusB · GLM-5

  1. SWE-bench Verified

    Coding
    Source ↗
    A 74.5%B 77.8%
    Winner: GLM-5Δ 3.3
    SWE-bench Verified: Claude 4.1 Opus scored 74.5%; GLM-5 scored 77.8%. GLM-5 wins this benchmark.

Operational comparison

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

MetricClaude 4.1 OpusGLM-5Comparison
Input / output priceUSD per 1M tokensClaude 4.1 Opus$15 input / $75 outputGLM-5$1 input / $3.2 outputGLM-5 has the lower combined listed price.
Generation speedtokens per secondClaude 4.1 Opus29 tok/sGLM-574 tok/sGLM-5 has the higher measured throughput.
First-answer latencyseconds to first tokenClaude 4.1 Opus1.66 sGLM-51.64 sGLM-5 reaches the first token sooner.
Context windowmaximum listed tokensClaude 4.1 Opus200KGLM-5200KListed context windows are equal.

Benchmark Deep Dive

Agentic
BenchmarkClaude 4.1 OpusGLM-5Result
JobBenchSource 21.9%—Not comparable
Terminal-Bench 2.0Source —56.2%Not comparable
Claw-EvalSource —57.7%Not comparable
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
CodingClaude 4.1 Opus wins
BenchmarkClaude 4.1 OpusGLM-5Result
SWE-bench VerifiedSource 74.5%77.8%GLM-5 leads
SWE-bench Verified*Source —72.8%Not comparable
SWE-bench ProSource —55.1%Not comparable
SWE MultilingualSource —73.3%Not comparable
SWE-RebenchSource —62.8%Not comparable
React Native EvalsSource —74.8%Not comparable
AA-SciCodeSource —46.2%Not comparable
Reasoning
BenchmarkClaude 4.1 OpusGLM-5Result
LongBench v2Source —60.8%Not comparable
AI-NeedleSource —63.3%Not comparable
AA-LCRSource —63.3%Not comparable
CritPtSource —2.0%Not comparable
Knowledge
BenchmarkClaude 4.1 OpusGLM-5Result
Artificial Analysis Intelligence IndexSource 28.2%39.5%GLM-5 leads
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
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
BenchmarkClaude 4.1 OpusGLM-5Result
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
BenchmarkClaude 4.1 OpusGLM-5Result
MMLU-ProXSource —83.1%Not comparable
NOVA-63Source —55.1%Not comparable
Multimodal
BenchmarkClaude 4.1 OpusGLM-5Result
Design Arena WebsiteSource 12091280GLM-5 leads
Inst. Following
BenchmarkClaude 4.1 OpusGLM-5Result
IFEvalSource —92.6%Not comparable
AA-IFBenchSource —72.3%Not comparable
Frequently Asked Questions (2)

Which is better, Claude 4.1 Opus or GLM-5?

GLM-5 is ahead on BenchLM's BenchAlign leaderboard, 66.06 to 45.88. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 74.5% and 77.8%.

Which is better for coding, Claude 4.1 Opus or GLM-5?

Claude 4.1 Opus has the edge for coding in this comparison, averaging 74.5 versus 66.3. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.

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