Updated November 12, 2026. Rank says Claude Sonnet 5.5 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Claude Sonnet 5.5 has the higher public score, 83.07 versus 55.5, and the 90% score intervals do not overlap. 4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Claude Sonnet 5.5: $2 input / $10 output
GLM-5.1: $1.40 input / $4.40 output
Claude Sonnet 5.5: 1M
GLM-5.1: 203K
Claude Sonnet 5.5: September 28, 2026
GLM-5.1: April 7, 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
Claude Sonnet 5.5
Claude Sonnet 5.5 has the higher public coding point estimate, 81 to 46.2, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
Claude Sonnet 5.5
Claude Sonnet 5.5 has the larger documented context window.
1K fresh input + 500 output tokens
GLM-5.1
GLM-5.1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
GLM-5.1
GLM-5.1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GLM-5.1 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GLM-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate.
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
Claude Sonnet 5.5 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.
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.
FrontierMath v2 (Tiers 1-3)Math
Normalized gap 51.9SWE-bench ProCoding
Normalized gap 22.9Each 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 5.5 | GLM-5.1 | Basis | Reading |
|---|---|---|---|---|
| Coding | 81.0Supported · #4/162 | 46.2Supported · #55/162 | Like-for-likeBenchAlign v5.8 lane · 12 vs 8 public rows | Claude Sonnet 5.5 leads |
| Knowledge | 82.1Supported · #6/180 | 52.0Supported · #69/180 | Like-for-likeBenchAlign v5.8 lane · 17 vs 4 public rows | Claude Sonnet 5.5 leads |
| Agentic | 72.7Supported · #9/135 | 44.0Estimated · #63/135 | Directional onlyBenchAlign v5.8 lane · 19 vs 10 public rows | Directional only |
| Reasoning | 77.0Supported · #6/160 | 44.6Estimated · #73/160 | Directional onlyBenchAlign v5.8 lane · 0 vs 0 public rows | Directional only |
| Multimodal | 78.9Estimated · #7/67 | Not ranked | Not comparableBenchAlign v5.8 lane · 10 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 | 93.8#7/157 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 89.7Unranked · 4 rankable rows | 55.7#8/12 | Not comparableProvisional lane · 2 vs 3 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-5.1 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
GLM-5.1 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
GLM-5.1 does not fit this workload in one request. GLM-5.1 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 5.5
GLM-5.1
203K
Claude Sonnet 5.5
claude-sonnet-5-5
Anthropic Claude Sonnet 5.5 model documentationGLM-5.1
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Sonnet 5.5
$0.1 per 1M cached input tokens
Claude API pricingGLM-5.1
Not published
Claude Sonnet 5.5
GLM-5.1
Claude Sonnet 5.5
GLM-5.1
Claude Sonnet 5.5
Generally Available · Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry, Claude Platform on AWS
Anthropic Claude Sonnet 5.5 model documentationGLM-5.1
Not sourced
Claude Sonnet 5.5
Reasoning
GLM-5.1
Reasoning
Claude Sonnet 5.5
Proprietary
GLM-5.1
Open Weight
Claude Sonnet 5.5
Proprietary
GLM-5.1
Open Weight
Claude Sonnet 5.5
2026-09-28
GLM-5.1
2026-04-07
Claude Sonnet 5.5 has the higher public score, 83.07 versus 55.5, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Claude Sonnet 5.5 has the higher public coding point estimate, 81 to 46.2, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Claude Sonnet 5.5 scores higher for agentic tasks on the public lane, 72.7 to 44. GLM-5.1 is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
For the stated presets, chat costs $0.007 on Claude Sonnet 5.5 and $0.0036 on GLM-5.1; repository review costs $0.13 and $0.0832; the cache-heavy agent loop costs $0.16 and $0.352. GLM-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate.
Claude Sonnet 5.5 has the larger documented context window: 1M, compared with 203K.
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 4.0
Not directly comparable
HLE w/ tools
Claude Sonnet 5.5 leads this result
DRACO
Not directly comparable
Terminal-Bench-Science 0.1
Not directly comparable
LAB all-pass (Harvey held-out)
Not directly comparable
LAB criterion-pass (Harvey held-out)
Not directly comparable
Toolathlon Verified Pass@3
Not directly comparable
Toolathlon Verified Pass³
Not directly comparable
Toolathlon Verified avg. turns
Not directly comparable
AutomationBench (Zapier 1.0.6)
Not directly comparable
Toolathlon-Verified
Not directly comparable
Hermes Index
Not directly comparable
Hermes Bench
Not directly comparable
MazeBench
Not directly comparable
OSWorld 2.1
Not directly comparable
OSWorld 2.1 Offline
Not directly comparable
Binary Exploitation Benchmark
Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Sonnet 5.5 leads this result
Terminal-Bench 2.0
Not directly comparable
BrowseComp
Not directly comparable
τ³-bench results
Not directly comparable
MCP Atlas
Not directly comparable
CyberGym
Not directly comparable
Claw-Eval
Not directly comparable
Gert Labs
Not directly comparable
ResearchClawBench
Not directly comparable
FrontierCode 1.1 Main
Not directly comparable
CursorBench 4.0
Not directly comparable
SWE-bench Pro
Claude Sonnet 5.5 leads this result
SWE Multilingual
Not directly comparable
SWE Multimodal
Not directly comparable
DeepSWE
Not directly comparable
FrontierCode 1.1 Extended
Not directly comparable
ProgramBench
Not directly comparable
FrontierSWE v2
Not directly comparable
Bug Hunt Bench
Not directly comparable
FrontierCode 2.0
Not directly comparable
VulcanBench Frontier v4
Not directly comparable
NL2Repo
Not directly comparable
SWE-Rebench
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Vibe Code Bench
Not directly comparable
OpenHarmony Bench
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
ExtractBench Pro
Not directly comparable
ExtractBench (LlamaIndex)
Not directly comparable
ParseBench
Not directly comparable
Chartography (no tools)
Not directly comparable
Chartography (tools)
Not directly comparable
BenchCAD Vision2Code (no tools)
Not directly comparable
BenchCAD Vision2Code (tools)
Not directly comparable
Biomedical image analysis
Not directly comparable
OfficeQA
Not directly comparable
OfficeQA Pro
Not directly comparable
HealthBench (raw)
Not directly comparable
HealthBench (length-adjusted)
Not directly comparable
HealthBench Professional (raw)
Not directly comparable
HealthBench Professional
Not directly comparable
BioMysteryBench (human-solvable)
Not directly comparable
BioMysteryBench (human-difficult)
Not directly comparable
SpatialBench Verified
Not directly comparable
SingleCellBench
Not directly comparable
Protein Design
Not directly comparable
Morphology-to-molecule matching
Not directly comparable
Medicinal chemistry
Not directly comparable
Protein Design library ranking
Not directly comparable
De novo protein-binder design
Not directly comparable
Protocols (troubleshooting)
Not directly comparable
Protocols (understanding)
Not directly comparable
HLE w/o tools
Not directly comparable
PhysicianBench
Not directly comparable
GPQA-D
Not directly comparable
HLE
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
ArXivMath Aug. 2026 (no tools)
Not directly comparable
ArXivMath Aug. 2026 (tools)
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared sourceClaude Sonnet 5.5 leads this result
FrontierMath v2 (Tier 4)
Not directly comparable
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 12, 2026
