Coding work
Code generation, repair, and software-engineering tasks
Claude Fable 5
Claude Fable 5 leads on the public coding lane, 72.9 to 56.8, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Claude Fable 5 vs GLM-5.3
Updated September 28, 2026. Rank says Claude Fable 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
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Decision reading
Claude Fable 5 has the higher public score, 78.89 versus 65.44, and the 90% score intervals do not overlap. 9 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 9
- Claude Fable 5 only
- 12
- GLM-5.3 only
- 15
- Like-for-like categories
- 3 / 8
Supported: Claude Fable 5 · Estimated: GLM-5.3How the comparison works
Which one for your work
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.
Agentic work
Tool use, computer use, and multi-step task completion
Claude Fable 5
Claude Fable 5 leads on the public agentic lane, 73.8 to 67.4, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Show secondary and unsupported calls
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documentedChat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-ratesRepository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Which one for a specific job
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.
72.9Claude Fable 556.8GLM-5.3
Like-for-like · BenchAlign v5.7
Claude Fable 5 leads the like-for-like coding row.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
12 Claude Fable 5 only
9 Shared
15 GLM-5.3 only
Shape of the matched evidence
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 9.3Claude Fable 5:89.8%GLM-5.3:80.5%MMLU-Pro (Vals)Knowledge
Normalized gap 4.7Claude Fable 5:91.5%GLM-5.3:86.8%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.7 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.
Agentic
Like-for-like- Claude Fable 5
- 73.8
- Supported · #4/117
- GLM-5.3
- 67.4
- Supported · #9/117
- Basis
- BenchAlign v5.7 lane · 5 vs 9 public rows
- Reading
- Claude Fable 5 leads · intervals overlap
Coding
Like-for-like- Claude Fable 5
- 72.9
- Supported · #5/142
- GLM-5.3
- 56.8
- Supported · #24/142
- Basis
- BenchAlign v5.7 lane · 10 vs 13 public rows
- Reading
- Claude Fable 5 leads
Knowledge
Like-for-like- Claude Fable 5
- 81.7
- Supported · #4/168
- GLM-5.3
- 61.9
- Supported · #35/168
- Basis
- BenchAlign v5.7 lane · 2 vs 2 public rows
- Reading
- Claude Fable 5 leads
Reasoning
Not comparable- Claude Fable 5
- 80.7
- Unranked · 4 rankable rows
- GLM-5.3
- 77.1
- #11/27
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Fable 5
- 62.6
- Unranked · 2 rankable rows
- GLM-5.3
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Fable 5
- Not ranked
- GLM-5.3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Fable 5
- 75.7
- #58/124
- GLM-5.3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Fable 5
- Not ranked
- GLM-5.3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Claude Fable 5 | GLM-5.3 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 73.8Supported · #4/117 | 67.4Supported · #9/117 | Like-for-likeBenchAlign v5.7 lane · 5 vs 9 public rows | Claude Fable 5 leads · intervals overlap |
| Coding | 72.9Supported · #5/142 | 56.8Supported · #24/142 | Like-for-likeBenchAlign v5.7 lane · 10 vs 13 public rows | Claude Fable 5 leads |
| Knowledge | 81.7Supported · #4/168 | 61.9Supported · #35/168 | Like-for-likeBenchAlign v5.7 lane · 2 vs 2 public rows | Claude Fable 5 leads |
| Reasoning | 80.7Unranked · 4 rankable rows | 77.1#11/27 | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | 62.6Unranked · 2 rankable rows | 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 |
| Instruction following | 75.7#58/124 | 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 v5.7) 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.
Supported evidence per lane · bars run 0–100Methodology
What each workload costs
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.
Chat turn
1K fresh input + 500 output tokens
- Claude Fable 5
- $0.035
- Fits in one request
- GLM-5.3
- Self-hosted; infrastructure cost varies
- Fits in one request
GLM-5.3 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Claude Fable 5
- $0.65
- Fits in one request
- GLM-5.3
- Self-hosted; infrastructure cost varies
- Fits in one request
GLM-5.3 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Claude Fable 5
- $0.9
- Fits in one request
- GLM-5.3
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
GLM-5.3 has no comparable published API token rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationClaude Fable 5GLM-5.3
Context window
Maximum documented context; output-token limits may be lower.
Claude Fable 5
GLM-5.3
API model ID
Claude Fable 5
claude-fable-5
Anthropic model overviewGLM-5.3
zai-org/GLM-5.3
Z.AI GLM-5.3 model cardCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Fable 5
$1 per 1M cached input tokens
Claude API pricingGLM-5.3
No comparable hosted API rate
Z.AI GLM-5.3 model cardDocumented inputs
Claude Fable 5
text, image
Anthropic model overviewGLM-5.3
Not sourced
Documented outputs
Claude Fable 5
GLM-5.3
Not sourced
Provider availability
Claude Fable 5
Generally Available · Claude API
Anthropic model overviewGLM-5.3
Not sourced
Reasoning profile
Claude Fable 5
Reasoning
GLM-5.3
Reasoning
Weight access
Claude Fable 5
Proprietary
GLM-5.3
Open Weight
License
Claude Fable 5
Proprietary
GLM-5.3
Open Weight
Release date
Claude Fable 5
2026-06-09
GLM-5.3
2026-08-14
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- Claude Fable 5 has the higher public score, 78.89 versus 65.44, and the 90% score intervals do not overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Claude Fable 5 or GLM-5.3?
Claude Fable 5 has the higher public score, 78.89 versus 65.44, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Which is better for coding, Claude Fable 5 or GLM-5.3?
Claude Fable 5 leads the public coding lane, 72.9 to 56.8, with Supported evidence for both models and non-overlapping 90% intervals.
Which is better for agentic tasks, Claude Fable 5 or GLM-5.3?
Claude Fable 5 leads the public agentic tasks lane, 73.8 to 67.4, with Supported evidence for both models, although the 90% intervals overlap.
Which costs less, Claude Fable 5 or GLM-5.3?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, Claude Fable 5 or GLM-5.3?
Both models list the same context window, 1M.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence36 rows
Agentic
Terminal-Bench 3.0
Claude Fable 534.0%SourceGLM-5.3—Not directly comparable
Terminal-Bench 2.1
Claude Fable 584.3%SourceGLM-5.388.2%SourceGLM-5.3 leads this result
OSWorld-Verified
Claude Fable 585%SourceGLM-5.3—Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Fable 580.5%SourceGLM-5.371.5%SourceClaude Fable 5 leads this result
ApprenticeBench
Claude Fable 534%SourceGLM-5.3—Not directly comparable
terminalBench3
Claude Fable 5—GLM-5.328.3%SourceNot directly comparable
CyberGym
Claude Fable 5—GLM-5.384.5%SourceNot directly comparable
ExploitGym
Claude Fable 5—GLM-5.315.0%SourceNot directly comparable
Toolathlon-Verified
Claude Fable 5—GLM-5.373.0%SourceNot directly comparable
AutomationBench
Claude Fable 5—GLM-5.348.2%SourceNot directly comparable
Agents' Last Exam
Claude Fable 5—GLM-5.328.5%SourceNot directly comparable
HLE w/ tools
Claude Fable 5—GLM-5.362.5%SourceNot directly comparable
Coding
SWE-bench Verified
Claude Fable 595%SourceGLM-5.3—Not directly comparable
SWE-bench Pro
Claude Fable 580%SourceGLM-5.3—Not directly comparable
FrontierSWE v2
Shared sourceClaude Fable 547.0%GLM-5.330.2%Claude Fable 5 leads this result
FrontierCode 1.1 Main
Claude Fable 553.5%SourceGLM-5.3—Not directly comparable
Terminal-Bench 2.1
Claude Fable 584.3%SourceGLM-5.388.2%SourceGLM-5.3 leads this result
cursorBench31
Claude Fable 570.6%SourceGLM-5.3—Not directly comparable
cursorBench32
Claude Fable 570.5%SourceGLM-5.3—Not directly comparable
VulcanBench v3
Claude Fable 589.5%SourceGLM-5.378.3%SourceClaude Fable 5 leads this result
LiveCodeBench (Vals)
Claude Fable 589.8%SourceGLM-5.380.5%SourceClaude Fable 5 leads this result
SWE-bench (Vals)
Claude Fable 595.0%SourceGLM-5.395.4%SourceGLM-5.3 leads this result
terminalBench3
Claude Fable 5—GLM-5.328.3%SourceNot directly comparable
DeepSWE
Claude Fable 5—GLM-5.366.9%SourceNot directly comparable
NL2Repo
Claude Fable 5—GLM-5.358%SourceNot directly comparable
ProgramBench
Claude Fable 5—GLM-5.319.0%SourceNot directly comparable
FrontierSWE
Claude Fable 5—GLM-5.378.1%SourceNot directly comparable
sweMarathon
Claude Fable 5—GLM-5.342.5%SourceNot directly comparable
PostTrain Bench
Claude Fable 5—GLM-5.339.8%SourceNot directly comparable
OpenHarmony Bench
Claude Fable 5—GLM-5.360.8%SourceNot directly comparable
Reasoning
Multimodal
Knowledge
36 public results · 9 shared
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Last updated September 28, 2026