Updated October 29, 2026. Rank says Claude Opus 4.6 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Claude Opus 4.6 has the higher public score estimate, 61.82 versus 60.03, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 8 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Code generation, repair, and software-engineering tasks
GLM-5.2
GLM-5.2 has the higher public coding point estimate, 53.7 to 46.1, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
GLM-5.2
GLM-5.2 has the higher public agentic point estimate, 57.1 to 46.5, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
1K fresh input + 500 output tokens
GLM-5.2
GLM-5.2 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
GLM-5.2
GLM-5.2 has the lower estimated token cost for this stated workload. Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
50K fresh input + 3K output tokens
GLM-5.2
GLM-5.2 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
Like-for-like · BenchAlign v5.8
GLM-5.2 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
HLEKnowledge
Normalized gap 12.5SWE-bench ProCoding
Normalized gap 8.7FrontierMath v2 (Tiers 1-3)Math
Normalized gap 6.8FrontierMath v2 (Tier 4)Math
Normalized gap 2.4HLE w/o toolsKnowledge
Normalized gap 0.5Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
| Category | Claude Opus 4.6 | GLM-5.2 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 46.5Supported · #59/135 | 57.1Supported · #38/135 | Like-for-likeBenchAlign v5.8 lane · 11 vs 8 public rows | GLM-5.2 leads · intervals overlap |
| Coding | 46.1Supported · #55/156 | 53.7Supported · #34/156 | Like-for-likeBenchAlign v5.8 lane · 9 vs 9 public rows | GLM-5.2 leads · intervals overlap |
| Knowledge | 58.7Estimated · #53/179 | 56.4Supported · #56/179 | Directional onlyBenchAlign v5.8 lane · 9 vs 6 public rows | Directional only |
| Instruction following | 52.4#94/157 | 89.9#29/157 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 51.2Unranked · 3 rankable rows | 78.8Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | 74.7#26/58 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 69.1Unranked · 3 rankable rows | 78.4#3/12 | Not comparableProvisional lane · 2 vs 4 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
1K fresh input + 500 output tokens
GLM-5.2 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
GLM-5.2 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
GLM-5.2 has the lower modeled cost
Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
Claude Opus 4.6
1M
GLM-5.2
1M
Claude Opus 4.6
Not sourced
GLM-5.2
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Opus 4.6
Not published
GLM-5.2
Not published
Claude Opus 4.6
Not sourced
GLM-5.2
Not sourced
Claude Opus 4.6
Not sourced
GLM-5.2
Not sourced
Claude Opus 4.6
Not sourced
GLM-5.2
Not sourced
Claude Opus 4.6
Non-Reasoning
GLM-5.2
Reasoning
Claude Opus 4.6
Proprietary
GLM-5.2
Open Weight
Claude Opus 4.6
Proprietary
GLM-5.2
Open Weight
Claude Opus 4.6
2026-02-01
GLM-5.2
2026-06-16
Claude Opus 4.6 has the higher public score estimate, 61.82 versus 60.03, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
GLM-5.2 has the higher public coding point estimate, 53.7 to 46.1, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
GLM-5.2 has the higher public agentic tasks point estimate, 57.1 to 46.5, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.0175 on Claude Opus 4.6 and $0.0036 on GLM-5.2; repository review costs $0.325 and $0.0832; the cache-heavy agent loop costs $1.35 and $0.352. Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Both models list the same context window, 1M.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Not directly comparable
BrowseComp
Not directly comparable
OSWorld-Verified
Not directly comparable
AndroidWorld
Not directly comparable
Claw-Eval
Not directly comparable
DeepSearchQA
Not directly comparable
CyberGym
Not directly comparable
Gert Labs
Not directly comparable
ResearchClawBench
Shared sourceGLM-5.2 leads this result
JobBench
Not directly comparable
ApprenticeBench
Not directly comparable
Terminal-Bench 3.0
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
MCP Atlas
Not directly comparable
Toolathlon
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
HLE w/ tools
Not directly comparable
MazeBench
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE-bench Verified*
Not directly comparable
LiveCodeBench Pro
Not directly comparable
SWE-bench Pro
GLM-5.2 leads this result
SWE-Rebench
Not directly comparable
React Native Evals
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Vibe Code Bench
Not directly comparable
FrontierCode 1.1 Main
Not directly comparable
NL2Repo
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
ProgramBench
Not directly comparable
CursorBench 3.2
Not directly comparable
OpenHarmony Bench
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
PostTrainBench v1.1
Not directly comparable
ParseBench
Not directly comparable
MMMU-Pro
Not directly comparable
ERQA
Not directly comparable
ScreenSpot Pro
Not directly comparable
MedXpertQA (MM)
Not directly comparable
GPQA
Claude Opus 4.6 leads this result
GPQA-D
GLM-5.2 leads this result
SuperGPQA
Not directly comparable
MMLU-Pro
Not directly comparable
MMLU-Pro (Arcee)
Not directly comparable
HLE
Claude Opus 4.6 leads this result
HLE w/o tools
GLM-5.2 leads this result
HealthBench Hard
Not directly comparable
MedXpertQA (Text)
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
AIME25 (Arcee)
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared sourceClaude Opus 4.6 leads this result
FrontierMath v2 (Tier 4)
Shared sourceGLM-5.2 leads this result
AIME26
Not directly comparable
HMMT Nov 2025
Not directly comparable
HMMT Feb 2026
Not directly comparable
MMAnswerBench
Not directly comparable
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Last updated October 29, 2026
