Long documents
Prompts that approach the documented context limit
GPT-5.4
GPT-5.4 has the larger documented context window.
Confidence: documented
Claude Fable 5.2 vs GPT-5.4
Updated November 9, 2026. Rank says Claude Fable 5.2 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
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Decision reading
Claude Fable 5.2 has the higher public point estimate, 91.19 versus 66.07. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 5 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 5
- Claude Fable 5.2 only
- 25
- GPT-5.4 only
- 40
- Like-for-like categories
- 2 / 8
Estimated: Claude Fable 5.2 · Supported: GPT-5.4. Conditional ranges do not establish rank confidence.How 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.
Chat turn cost
1K fresh input + 500 output tokens
GPT-5.4
GPT-5.4 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GPT-5.4
GPT-5.4 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
GPT-5.4
GPT-5.4 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Claude Fable 5.2 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Claude Fable 5.2 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
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.
86.9Claude Fable 5.248.0GPT-5.4
Directional only · BenchAlign v5.8
Claude Fable 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.
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.
25 Claude Fable 5.2 only
5 Shared
40 GPT-5.4 only
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
ARC-AGI-3Reasoning
Normalized gap 65.5Claude Fable 5.2:65.7%GPT-5.4:0.2%SWE-bench ProCoding
Normalized gap 35.3Claude Fable 5.2:93%GPT-5.4:57.7%HLE w/o toolsKnowledge
Normalized gap 33.4Claude Fable 5.2:73.2%GPT-5.4:39.8%HLEKnowledge
Normalized gap 22.1Claude Fable 5.2:74.2%GPT-5.4:52.1%OfficeQA ProMultimodal
Normalized gap 19.7Claude Fable 5.2:72.9%GPT-5.4:53.2%
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 v5.8 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.
Reasoning
Like-for-like- Claude Fable 5.2
- 90.7
- Supported · #1/157
- GPT-5.4
- 62.6
- Supported · #25/157
- Basis
- BenchAlign v5.8 lane · 3 vs 4 public rows
- Reading
- Claude Fable 5.2 leads
Knowledge
Like-for-like- Claude Fable 5.2
- 97.6
- Supported · #1/179
- GPT-5.4
- 67.1
- Supported · #22/179
- Basis
- BenchAlign v5.8 lane · 3 vs 7 public rows
- Reading
- Claude Fable 5.2 leads
Agentic
Directional only- Claude Fable 5.2
- 90.7
- Estimated · #1/132
- GPT-5.4
- 53.2
- Supported · #48/132
- Basis
- BenchAlign v5.8 lane · 8 vs 16 public rows
- Reading
- Directional only
Coding
Directional only- Claude Fable 5.2
- 86.9
- Estimated · #1/158
- GPT-5.4
- 48.0
- Supported · #53/158
- Basis
- BenchAlign v5.8 lane · 8 vs 6 public rows
- Reading
- Directional only
Multimodal
Directional only- Claude Fable 5.2
- 89.8
- Estimated · #1/67
- GPT-5.4
- 65.0
- Estimated · #25/67
- Basis
- BenchAlign v5.8 lane · 6 vs 10 public rows
- Reading
- Directional only
Multilingual
Not comparable- Claude Fable 5.2
- Not ranked
- GPT-5.4
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Fable 5.2
- Not ranked
- GPT-5.4
- 90.7
- #25/157
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Fable 5.2
- Not ranked
- GPT-5.4
- 78.5
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
| Category | Claude Fable 5.2 | GPT-5.4 | Basis | Reading |
|---|---|---|---|---|
| Reasoning | 90.7Supported · #1/157 | 62.6Supported · #25/157 | Like-for-likeBenchAlign v5.8 lane · 3 vs 4 public rows | Claude Fable 5.2 leads |
| Knowledge | 97.6Supported · #1/179 | 67.1Supported · #22/179 | Like-for-likeBenchAlign v5.8 lane · 3 vs 7 public rows | Claude Fable 5.2 leads |
| Agentic | 90.7Estimated · #1/132 | 53.2Supported · #48/132 | Directional onlyBenchAlign v5.8 lane · 8 vs 16 public rows | Directional only |
| Coding | 86.9Estimated · #1/158 | 48.0Supported · #53/158 | Directional onlyBenchAlign v5.8 lane · 8 vs 6 public rows | Directional only |
| Multimodal | 89.8Estimated · #1/67 | 65.0Estimated · #25/67 | Directional onlyBenchAlign v5.8 lane · 6 vs 10 public rows | Directional only |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 90.7#25/157 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 78.5Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 2 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.
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 5.2
- $0.028
- Fits in one request
- GPT-5.4
- $0.01
- Fits in one request
GPT-5.4 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude Fable 5.2
- $0.52
- Fits in one request
- GPT-5.4
- $0.17
- Fits in one request
GPT-5.4 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Claude Fable 5.2
- $0.6
- Fits in one request
- GPT-5.4
- $0.25
- Fits in one request
GPT-5.4 has the lower modeled cost
Costs use the listed standard API rates.
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 5.2GPT-5.4
Context window
Maximum documented context; output-token limits may be lower.
Claude Fable 5.2
GPT-5.4
1.05M
OpenAI pricingAPI model ID
Claude Fable 5.2
claude-fable-5-2
Anthropic Claude Fable 5.2 model documentationGPT-5.4
gpt-5.4
OpenAI pricingCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Fable 5.2
$0.2 per 1M cached input tokens
Claude API pricingGPT-5.4
$0.25 per 1M cached input tokens
OpenAI pricingDocumented inputs
Claude Fable 5.2
GPT-5.4
Not sourced
Documented outputs
Claude Fable 5.2
GPT-5.4
Not sourced
Provider availability
Claude Fable 5.2
Generally Available · Claude API, Claude products, AWS, Google Cloud, Microsoft Azure
Anthropic Fable 5.2 and Mythos 5.2 launchGPT-5.4
Not sourced
Reasoning profile
Claude Fable 5.2
Reasoning
GPT-5.4
Reasoning
Weight access
Claude Fable 5.2
Proprietary
GPT-5.4
Proprietary
License
Claude Fable 5.2
Proprietary
GPT-5.4
Proprietary
Release date
Claude Fable 5.2
2026-11-09
GPT-5.4
2026-03-05
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 5.2 has the higher public point estimate, 91.19 versus 66.07. Their conditional score ranges do not overlap. These ranges do not establish rank confidence.
- Workload cost
- Repository review: $0.52 vs $0.17. Cache-heavy agent loop: $0.6 vs $0.25.
- Context tradeoff
- GPT-5.4 has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Claude Fable 5.2 or GPT-5.4?
Claude Fable 5.2 has the higher public point estimate, 91.19 versus 66.07. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Claude Fable 5.2 or GPT-5.4?
Claude Fable 5.2 scores higher for coding on the public lane, 86.9 to 48. Claude Fable 5.2 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, Claude Fable 5.2 or GPT-5.4?
Claude Fable 5.2 scores higher for agentic tasks on the public lane, 90.7 to 53.2. Claude Fable 5.2 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.
Which costs less, Claude Fable 5.2 or GPT-5.4?
For the stated presets, chat costs $0.028 on Claude Fable 5.2 and $0.01 on GPT-5.4; repository review costs $0.52 and $0.17; the cache-heavy agent loop costs $0.6 and $0.25. Costs use the listed standard API rates.
Which has the larger context window, Claude Fable 5.2 or GPT-5.4?
GPT-5.4 has the larger documented context window: 1.05M, compared with 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 evidence70 rows
Agentic
Terminal-Bench 4.0
Claude Fable 5.267.60%SourceGPT-5.4—Not directly comparable
Terminal-Bench-Science 0.1
Claude Fable 5.274.6%SourceGPT-5.4—Not directly comparable
AutomationBench
Claude Fable 5.251.4%SourceGPT-5.4—Not directly comparable
OSWorld 2.1 Offline
Claude Fable 5.289.3%SourceGPT-5.4—Not directly comparable
WANDR
Claude Fable 5.282.1%SourceGPT-5.4—Not directly comparable
Remote Labor Index
Claude Fable 5.241.3%SourceGPT-5.4—Not directly comparable
RoboDojo Sim
Claude Fable 5.264.9%SourceGPT-5.4—Not directly comparable
DRACO
Claude Fable 5.287.7%SourceGPT-5.4—Not directly comparable
LifeVal
Claude Fable 5.2—GPT-5.473.0%SourceNot directly comparable
Terminal-Bench 2.0
Claude Fable 5.2—GPT-5.475.1%SourceNot directly comparable
CyberGym
Claude Fable 5.2—GPT-5.479.0%SourceNot directly comparable
BrowseComp
Claude Fable 5.2—GPT-5.482.7%SourceNot directly comparable
OSWorld-Verified
Claude Fable 5.2—GPT-5.475%SourceNot directly comparable
MCP Atlas
Claude Fable 5.2—GPT-5.470.6%SourceNot directly comparable
Toolathlon
Claude Fable 5.2—GPT-5.454.6%SourceNot directly comparable
τ²-bench results
Claude Fable 5.2—GPT-5.498.9%SourceNot directly comparable
Claw-Eval
Claude Fable 5.2—GPT-5.460.3%SourceNot directly comparable
DeepSearchQA
Claude Fable 5.2—GPT-5.473.6%SourceNot directly comparable
Gert Labs
Claude Fable 5.2—GPT-5.464.89%SourceNot directly comparable
ResearchClawBench
Claude Fable 5.2—GPT-5.415.3%SourceNot directly comparable
JobBench
Claude Fable 5.2—GPT-5.438.9%SourceNot directly comparable
ExploitGym
Claude Fable 5.2—GPT-5.46.0%SourceNot directly comparable
ApprenticeBench
Claude Fable 5.2—GPT-5.411%SourceNot directly comparable
MazeBench
Claude Fable 5.2—GPT-5.40%SourceNot directly comparable
Coding
SWE-bench Pro
Claude Fable 5.293%SourceGPT-5.457.7%SourceClaude Fable 5.2 leads this result
SWE Multilingual
Claude Fable 5.296.6%SourceGPT-5.4—Not directly comparable
SWE Multimodal
Claude Fable 5.274.1%SourceGPT-5.4—Not directly comparable
DeepSWE
Claude Fable 5.274.3%SourceGPT-5.4—Not directly comparable
FrontierSWE v2
Claude Fable 5.269.9%SourceGPT-5.4—Not directly comparable
ProgramBench
Claude Fable 5.285.6%SourceGPT-5.4—Not directly comparable
FrontierCode 2.0
Claude Fable 5.249.1%SourceGPT-5.4—Not directly comparable
CursorBench 4.0
Claude Fable 5.260.0%SourceGPT-5.4—Not directly comparable
LiveCodeBench Pro
Claude Fable 5.2—GPT-5.487.5%SourceNot directly comparable
React Native Evals
Claude Fable 5.2—GPT-5.485.3%SourceNot directly comparable
Terminal-Bench 2.0
Claude Fable 5.2—GPT-5.475.1%SourceNot directly comparable
Vibe Code Bench
Claude Fable 5.2—GPT-5.467.42%SourceNot directly comparable
PostTrainBench v1.1
Claude Fable 5.2—GPT-5.419.0%SourceNot directly comparable
Reasoning
ARC-AGI-3
Claude Fable 5.265.7%SourceGPT-5.40.2%SourceClaude Fable 5.2 leads this result
ARC-AGI-3 Provider Adapter
Claude Fable 5.294.90%SourceGPT-5.4—Not directly comparable
STEM Professor Bench
Claude Fable 5.261.8%SourceGPT-5.4—Not directly comparable
BioBasicsBench
Claude Fable 5.2—GPT-5.433.2%SourceNot directly comparable
ARC-AGI-2
Claude Fable 5.2—GPT-5.474.0%SourceNot directly comparable
ARC-AGI-1
Claude Fable 5.2—GPT-5.493.67%SourceNot directly comparable
Multimodal
Chartography (no tools)
Claude Fable 5.273.4%SourceGPT-5.4—Not directly comparable
Chartography (tools)
Claude Fable 5.291.6%SourceGPT-5.4—Not directly comparable
OfficeQA
Claude Fable 5.283.4%SourceGPT-5.4—Not directly comparable
OfficeQA Pro
Claude Fable 5.272.9%SourceGPT-5.453.2%SourceClaude Fable 5.2 leads this result
BenchCAD 2.1
Claude Fable 5.275.4%SourceGPT-5.4—Not directly comparable
BenchCAD 2.1 (tools)
Claude Fable 5.283.5%SourceGPT-5.4—Not directly comparable
ParseBench
Claude Fable 5.2—GPT-5.461.72%SourceNot directly comparable
MMMU-Pro
Claude Fable 5.2—GPT-5.481.2%SourceNot directly comparable
MMMU-Pro w/ Python
Claude Fable 5.2—GPT-5.482.1%SourceNot directly comparable
CharXiv
Claude Fable 5.2—GPT-5.482.8%SourceNot directly comparable
ERQA
Claude Fable 5.2—GPT-5.465.4%SourceNot directly comparable
SimpleVQA
Claude Fable 5.2—GPT-5.461.1%SourceNot directly comparable
ScreenSpot Pro
Claude Fable 5.2—GPT-5.485.4%SourceNot directly comparable
ZeroBench
Claude Fable 5.2—GPT-5.441.0%SourceNot directly comparable
MedXpertQA (MM)
Claude Fable 5.2—GPT-5.477.1%SourceNot directly comparable
Knowledge
HLE
Claude Fable 5.274.2%SourceGPT-5.452.1%SourceClaude Fable 5.2 leads this result
HLE w/o tools
Claude Fable 5.273.2%SourceGPT-5.439.8%SourceClaude Fable 5.2 leads this result
PhysicianBench
Claude Fable 5.264.4%SourceGPT-5.4—Not directly comparable
GPQA
Claude Fable 5.2—GPT-5.492.8%SourceNot directly comparable
GPQA-D
Claude Fable 5.2—GPT-5.492.8%SourceNot directly comparable
HealthBench Hard
Claude Fable 5.2—GPT-5.440.1%SourceNot directly comparable
MedXpertQA (Text)
Claude Fable 5.2—GPT-5.459.6%SourceNot directly comparable
HealthBench Professional
Claude Fable 5.2—GPT-5.448.1%SourceNot directly comparable
Multilingual
Math
70 public results · 5 shared
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Last updated November 9, 2026