Updated October 10, 2026. Rank says Claude Sonnet 5 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Claude Sonnet 5 has the higher public point estimate, 65.92 versus 57.36. Their conditional score ranges overlap. These ranges do not establish rank confidence. 9 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
Claude Sonnet 5
Claude Sonnet 5 has the higher public coding point estimate, 58.3 to 49.3, 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, 64.8 to 55.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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 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.
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.
LiveCodeBench (Vals)Coding
Normalized gap 1.9MMLU-Pro (Vals)Knowledge
Normalized gap 1.4CharXivMultimodal
Normalized gap 1.1Each 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 Sonnet 5 | GLM-5.3-Flash | Basis | Reading |
|---|---|---|---|---|
| Agentic | 64.8Supported · #16/123 | 55.7Supported · #34/123 | Like-for-likeBenchAlign v5.8 lane · 7 vs 6 public rows | Claude Sonnet 5 leads · intervals overlap |
| Coding | 58.3Supported · #24/146 | 49.3Supported · #42/146 | Like-for-likeBenchAlign v5.8 lane · 11 vs 8 public rows | Claude Sonnet 5 leads · intervals overlap |
| Knowledge | 65.6Supported · #29/177 | 60.5Supported · #43/177 | Like-for-likeBenchAlign v5.8 lane · 6 vs 2 public rows | Claude Sonnet 5 leads · intervals overlap |
| Multimodal | 82.0#15/54 | 84.0#14/54 | Directional onlyProvisional lane · 1 vs 2 weighted rows | Directional only |
| Reasoning | 82.5Unranked · 2 rankable rows | 81.1#11/28 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 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-5.3-Flash has no comparable published API token rate.
50K fresh input + 3K output tokens
GLM-5.3-Flash has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
GLM-5.3-Flash has no comparable published API token 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 Sonnet 5
GLM-5.3-Flash
Claude Sonnet 5
claude-sonnet-5
Anthropic model overviewGLM-5.3-Flash
glm-5.3-flash
GLM-5.3-Flash model cardA 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-5.3-Flash
No comparable hosted API rate
GLM-5.3-Flash model cardClaude Sonnet 5
text, image
Anthropic model overviewGLM-5.3-Flash
Not sourced
Claude Sonnet 5
GLM-5.3-Flash
Not sourced
Claude Sonnet 5
Generally Available · Claude API
Anthropic model overviewGLM-5.3-Flash
Not sourced
Claude Sonnet 5
Reasoning
GLM-5.3-Flash
Reasoning
Claude Sonnet 5
Proprietary
GLM-5.3-Flash
Open Weight
Claude Sonnet 5
Proprietary
GLM-5.3-Flash
Open Weight
Claude Sonnet 5
2026-06-30
GLM-5.3-Flash
2026-08-26
Claude Sonnet 5 has the higher public point estimate, 65.92 versus 57.36. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Claude Sonnet 5 has the higher public coding point estimate, 58.3 to 49.3, 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, 64.8 to 55.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Both models list the same context window, 1M.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 3.0
Not directly comparable
Terminal-Bench 2.1
GLM-5.3-Flash 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
Toolathlon-Verified
Not directly comparable
AutomationBench
Not directly comparable
Agents' Last Exam
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE-bench Pro
Not directly comparable
SWE Multilingual
Not directly comparable
SWE Multimodal
Not directly comparable
Terminal-Bench 2.1
GLM-5.3-Flash leads this result
FrontierCode 1.1 Main
Not directly comparable
CursorBench 3.2
Not directly comparable
VulcanBench CII v1
Not directly comparable
LiveCodeBench (Vals)
Claude Sonnet 5 leads this result
SWE-bench (Vals)
GLM-5.3-Flash leads this result
CursorBench 4.0
Not directly comparable
DeepSWE
Not directly comparable
NL2Repo
Not directly comparable
OpenHarmony Bench
Not directly comparable
FrontierSWE v2
Not directly comparable
Bug Hunt Bench
Not directly comparable
CharXiv
GLM-5.3-Flash leads this result
CharXiv w/o tools
Not directly comparable
OfficeQA Pro
Not directly comparable
Chartography (tools)
Not directly comparable
BabyVision
Not directly comparable
MMVU
Not directly comparable
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
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
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Last updated October 10, 2026
