Updated November 13, 2026. Rank says Claude Sonnet 5 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Claude Sonnet 5 has the higher public score estimate, 63.83 versus 59.74, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 16 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Claude Sonnet 5: $2 input / $10 output
GLM-5.2: $1.40 input / $4.40 output
Claude Sonnet 5: 1M
GLM-5.2: 1M
Claude Sonnet 5: June 30, 2026
GLM-5.2: June 16, 2026
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, 52.2 to 51.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
Claude Sonnet 5
Claude Sonnet 5 has the higher public agentic point estimate, 62.5 to 54.2, 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
Claude Sonnet 5
Claude Sonnet 5 has the lower estimated token cost for this stated workload. 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.
The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.
We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 category rests 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 16.9LiveCodeBench (Vals)Coding
Normalized gap 12.9CursorBench 3.2Coding
Normalized gap 6.5FrontierMath v2 (Tiers 1-3)Math
Normalized gap 6.4HLE w/o toolsKnowledge
Normalized gap 2.7Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, reasoning, multimodal, 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 Sonnet 5 | GLM-5.2 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 62.5Supported · #22/135 | 54.2Supported · #42/135 | Like-for-likeBenchAlign v5.8 lane · 10 vs 8 public rows | Claude Sonnet 5 leads · intervals overlap |
| Coding | 51.7Supported · #40/162 | 52.2Supported · #36/162 | Like-for-likeBenchAlign v5.8 lane · 11 vs 11 public rows | GLM-5.2 leads · intervals overlap |
| Knowledge | 61.8Supported · #43/180 | 56.1Supported · #56/180 | Like-for-likeBenchAlign v5.8 lane · 6 vs 6 public rows | Claude Sonnet 5 leads · intervals overlap |
| Reasoning | 59.8Estimated · #32/160 | 42.7Supported · #72/160 | Directional onlyBenchAlign v5.8 lane · 0 vs 1 public rows | Directional only |
| Multimodal | 60.4Supported · #34/67 | Not ranked | Not comparableBenchAlign v5.8 lane · 3 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 89.9#29/157 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 64.9Unranked · 2 rankable rows | 74.6#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, reasoning, multimodal, 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
Claude Sonnet 5 has the lower modeled cost
GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Enter a month of tokens to price it at both models’ listed API rates.
Standard listed rates, no batch or volume discounts.
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 Sonnet 5
GLM-5.2
1M
Claude Sonnet 5
claude-sonnet-5
Anthropic model overviewGLM-5.2
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Sonnet 5
$0.2 per 1M cached input tokens
Claude API pricingGLM-5.2
Not published
Claude Sonnet 5
text, image
Anthropic model overviewGLM-5.2
Claude Sonnet 5
GLM-5.2
Claude Sonnet 5
Generally Available · Claude API
Anthropic model overviewGLM-5.2
Not sourced
Claude Sonnet 5
Reasoning
GLM-5.2
Reasoning
Claude Sonnet 5
Proprietary
GLM-5.2
Open Weight
Claude Sonnet 5
Proprietary
GLM-5.2
Open Weight
Claude Sonnet 5
2026-06-30
GLM-5.2
2026-06-16
Claude Sonnet 5 has the higher public score estimate, 63.83 versus 59.74, 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, 52.2 to 51.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Claude Sonnet 5 has the higher public agentic tasks point estimate, 62.5 to 54.2, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.007 on Claude Sonnet 5 and $0.0036 on GLM-5.2; repository review costs $0.13 and $0.0832; the cache-heavy agent loop costs $0.18 and $0.352. 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 4.1
Not directly comparable
Terminal-Bench 3.0
Shared sourceClaude Sonnet 5 leads this result
Terminal-Bench 2.1
GLM-5.2 leads this result
BrowseComp
Not directly comparable
HLE w/ tools
Claude Sonnet 5 leads this result
OSWorld-Verified
Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Sonnet 5 leads this result
ApprenticeBench
Not directly comparable
MazeBench
Tie
OSWorld 2.1
Not directly comparable
MCP Atlas
Not directly comparable
Toolathlon
Not directly comparable
ResearchClawBench
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE-bench Pro
Claude Sonnet 5 leads this result
SWE Multilingual
Not directly comparable
SWE Multimodal
Not directly comparable
Terminal-Bench 2.1
GLM-5.2 leads this result
FrontierCode 1.1 Main
Not directly comparable
CursorBench 3.2
Shared sourceClaude Sonnet 5 leads this result
VulcanBench CII v1
Not directly comparable
LiveCodeBench (Vals)
Claude Sonnet 5 leads this result
SWE-bench (Vals)
GLM-5.2 leads this result
CursorBench 4.0
Not directly comparable
NL2Repo
Not directly comparable
ProgramBench
Not directly comparable
OpenHarmony Bench v1.0
Not directly comparable
PostTrainBench v1.1
Not directly comparable
PostTrainBench
Not directly comparable
PostTrainBench v1
Not directly comparable
CritPt
Not directly comparable
HLE
Claude Sonnet 5 leads this result
HLE w/o tools
Claude Sonnet 5 leads this result
HLE-Verified
Not directly comparable
LABBench2
Not directly comparable
GPQA Diamond (Vals)
Claude Sonnet 5 leads this result
MMLU-Pro (Vals)
Claude Sonnet 5 leads this result
GPQA
Not directly comparable
GPQA-D
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared sourceClaude Sonnet 5 leads this result
FrontierMath v2 (Tier 4)
Shared sourceTie
AIME26
Not directly comparable
HMMT Nov 2025
Not directly comparable
HMMT Feb 2026
Not directly comparable
MMAnswerBench
Not directly comparable
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 7,000+ subscribers.
Last updated November 13, 2026
