Long documents
Prompts that approach the documented context limit
GPT-5.5 Pro
GPT-5.5 Pro has the larger documented context window.
Confidence: documented
Claude Fable 5.1 vs GPT-5.5 Pro
Updated October 17, 2026. Rank says Claude Fable 5.1 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 5.1 has the higher public point estimate, 83.18 versus 73.37. Their conditional score ranges overlap. These ranges do not establish rank confidence. 6 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 6
- Claude Fable 5.1 only
- 27
- GPT-5.5 Pro only
- 2
- Like-for-like categories
- 0 / 8
Supported: Claude Fable 5.1 · Estimated: GPT-5.5 Pro. 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
Claude Fable 5.1
Claude Fable 5.1 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
Claude Fable 5.1
Claude Fable 5.1 has the lower estimated token cost for this stated workload. GPT-5.5 Pro has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Claude Fable 5.1
Claude Fable 5.1 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
GPT-5.5 Pro is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-5.5 Pro is not ranked on the public lane for agentic, so no winner is named for agentic.
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.
76.8Claude Fable 5.1—GPT-5.5 Pro
Not comparable · BenchAlign v5.8
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
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.
27 Claude Fable 5.1 only
6 Shared
2 GPT-5.5 Pro 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.
HLE w/o toolsKnowledge
Normalized gap 17.8Claude Fable 5.1:60.9%GPT-5.5 Pro:43.1%FrontierMath v2 (Tier 4)Math
Normalized gap 9.8Claude Fable 5.1:87.805%GPT-5.5 Pro:78.049%HLEKnowledge
Normalized gap 7.8Claude Fable 5.1:65%GPT-5.5 Pro:57.2%ARC-AGI-2Reasoning
Normalized gap 5.8Claude Fable 5.1:90%GPT-5.5 Pro:84.2%FrontierMath v2 (Tiers 1-3)Math
Normalized gap 2.5Claude Fable 5.1:90.175%GPT-5.5 Pro:87.719%
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, 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
Not comparable- Claude Fable 5.1
- 80.9
- Supported · #2/128
- GPT-5.5 Pro
- Not ranked
- Basis
- BenchAlign v5.8 lane · 12 vs 1 public rows
- Reading
- Not comparable
Coding
Not comparable- Claude Fable 5.1
- 76.8
- Supported · #3/150
- GPT-5.5 Pro
- Not ranked
- Basis
- BenchAlign v5.8 lane · 11 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Claude Fable 5.1
- 85.2
- #4/28
- GPT-5.5 Pro
- 78.8
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Fable 5.1
- Not ranked
- GPT-5.5 Pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Claude Fable 5.1
- 86.9
- Supported · #2/184
- GPT-5.5 Pro
- Not ranked
- Basis
- BenchAlign v5.8 lane · 4 vs 2 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Fable 5.1
- Not ranked
- GPT-5.5 Pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Fable 5.1
- Not ranked
- GPT-5.5 Pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Fable 5.1
- 96.8
- Unranked · 2 rankable rows
- GPT-5.5 Pro
- 94.0
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Not comparable
| Category | Claude Fable 5.1 | GPT-5.5 Pro | Basis | Reading |
|---|---|---|---|---|
| Agentic | 80.9Supported · #2/128 | Not ranked | Not comparableBenchAlign v5.8 lane · 12 vs 1 public rows | Not comparable |
| Coding | 76.8Supported · #3/150 | Not ranked | Not comparableBenchAlign v5.8 lane · 11 vs 0 public rows | Not comparable |
| Reasoning | 85.2#4/28 | 78.8Unranked · 3 rankable rows | Not comparableProvisional lane · 1 vs 1 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 86.9Supported · #2/184 | Not ranked | Not comparableBenchAlign v5.8 lane · 4 vs 2 public 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 | 96.8Unranked · 2 rankable rows | 94.0Unranked · 3 rankable rows | Not comparableProvisional lane · 2 vs 2 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.
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 5.1
- $0.035
- Fits in one request
- GPT-5.5 Pro
- $0.12
- Fits in one request
Claude Fable 5.1 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude Fable 5.1
- $0.65
- Fits in one request
- GPT-5.5 Pro
- $2.04
- Fits in one request
Claude Fable 5.1 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 5.1
- $0.75
- Fits in one request
- GPT-5.5 Pro
- $8.40
- Fits in one request
- Cached input priced at the published list-input rate
Claude Fable 5.1 has the lower modeled cost
GPT-5.5 Pro has no published cached-input rate, so cached tokens use its listed input 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 5.1GPT-5.5 Pro
Context window
Maximum documented context; output-token limits may be lower.
Claude Fable 5.1
GPT-5.5 Pro
API model ID
Claude Fable 5.1
claude-fable-5-1
Anthropic Fable 5.1 and Mythos 5.1 launchGPT-5.5 Pro
gpt-5.5-pro
OpenAI GPT-5.5 Pro model documentationCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Fable 5.1
$0.25 per 1M cached input tokens
Anthropic Fable 5.1 launchGPT-5.5 Pro
Not published
OpenAI pricingDocumented inputs
Claude Fable 5.1
Not sourced
GPT-5.5 Pro
text, image
OpenAI model catalogDocumented outputs
Claude Fable 5.1
Not sourced
GPT-5.5 Pro
Provider availability
Claude Fable 5.1
Generally Available · Claude API, Claude products, AWS, Google Cloud, Microsoft Azure
Anthropic Fable 5.1 and Mythos 5.1 launchGPT-5.5 Pro
Generally Available · OpenAI Responses API
OpenAI model catalogReasoning profile
Claude Fable 5.1
Reasoning
GPT-5.5 Pro
Reasoning
Weight access
Claude Fable 5.1
Proprietary
GPT-5.5 Pro
Proprietary
License
Claude Fable 5.1
Proprietary
GPT-5.5 Pro
Proprietary
Release date
Claude Fable 5.1
2026-09-01
GPT-5.5 Pro
2026-04-23
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 5.1 has the higher public point estimate, 83.18 versus 73.37. Their conditional score ranges overlap. These ranges do not establish rank confidence.
- Workload cost
- Repository review: $0.65 vs $2.04. Cache-heavy agent loop: $0.75 vs $8.40.
- Context tradeoff
- GPT-5.5 Pro 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 5.1 or GPT-5.5 Pro?
Claude Fable 5.1 has the higher public point estimate, 83.18 versus 73.37. Their conditional score ranges 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 5.1 or GPT-5.5 Pro?
GPT-5.5 Pro is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Claude Fable 5.1 or GPT-5.5 Pro?
GPT-5.5 Pro is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Claude Fable 5.1 or GPT-5.5 Pro?
For the stated presets, chat costs $0.035 on Claude Fable 5.1 and $0.12 on GPT-5.5 Pro; repository review costs $0.65 and $2.04; the cache-heavy agent loop costs $0.75 and $8.40. GPT-5.5 Pro has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Claude Fable 5.1 or GPT-5.5 Pro?
GPT-5.5 Pro 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 evidence35 rows
Agentic
Terminal-Bench 4.0
Claude Fable 5.155.80%SourceGPT-5.5 Pro—Not directly comparable
Terminal-Bench-Science 0.1
Claude Fable 5.152.6%SourceGPT-5.5 Pro—Not directly comparable
OSWorld 2.0
Claude Fable 5.141.7%SourceGPT-5.5 Pro—Not directly comparable
AutomationBench
Claude Fable 5.131.4%SourceGPT-5.5 Pro—Not directly comparable
Toolathlon-Verified
Claude Fable 5.177.8%SourceGPT-5.5 Pro—Not directly comparable
Toolathlon Verified Pass@3
Claude Fable 5.181.5%SourceGPT-5.5 Pro—Not directly comparable
Toolathlon Verified Pass³
Claude Fable 5.173.1%SourceGPT-5.5 Pro—Not directly comparable
Toolathlon Verified avg. turns
Claude Fable 5.123.7 turnsSourceGPT-5.5 Pro—Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Fable 5.185.0%SourceGPT-5.5 Pro—Not directly comparable
ApprenticeBench
Claude Fable 5.172%SourceGPT-5.5 Pro—Not directly comparable
CWE-bench v1
Claude Fable 5.158.0%SourceGPT-5.5 Pro—Not directly comparable
MazeBench
Claude Fable 5.110%SourceGPT-5.5 Pro—Not directly comparable
BrowseComp
Claude Fable 5.1—GPT-5.5 Pro90.1%SourceNot directly comparable
Coding
Bug Hunt Bench
Claude Fable 5.143.0 fixesSourceGPT-5.5 Pro—Not directly comparable
SWE-bench Pro
Claude Fable 5.181.2%SourceGPT-5.5 Pro—Not directly comparable
SWE Multilingual
Claude Fable 5.189.1%SourceGPT-5.5 Pro—Not directly comparable
SWE Multimodal
Claude Fable 5.154.7%SourceGPT-5.5 Pro—Not directly comparable
DeepSWE
Claude Fable 5.167.4%SourceGPT-5.5 Pro—Not directly comparable
FrontierSWE v2
Claude Fable 5.156.3%SourceGPT-5.5 Pro—Not directly comparable
ProgramBench
Claude Fable 5.187.6%SourceGPT-5.5 Pro—Not directly comparable
CursorBench 3.2
Claude Fable 5.173.4%SourceGPT-5.5 Pro—Not directly comparable
LiveCodeBench (Vals)
Claude Fable 5.190.5%SourceGPT-5.5 Pro—Not directly comparable
CursorBench 4.0
Claude Fable 5.151.8%SourceGPT-5.5 Pro—Not directly comparable
PostTrainBench v1.1
Claude Fable 5.140.2%SourceGPT-5.5 Pro—Not directly comparable
Reasoning
Knowledge
HLE
Claude Fable 5.165%SourceGPT-5.5 Pro57.2%SourceClaude Fable 5.1 leads this result
HLE w/o tools
Claude Fable 5.160.9%SourceGPT-5.5 Pro43.1%SourceClaude Fable 5.1 leads this result
GPQA Diamond (Vals)
Claude Fable 5.193.4%SourceGPT-5.5 Pro—Not directly comparable
MMLU-Pro (Vals)
Claude Fable 5.192.4%SourceGPT-5.5 Pro—Not directly comparable
Instruction following
Gray Swan IPI (15 attempts)
Claude Fable 5.11.0%SourceGPT-5.5 Pro—Not directly comparable
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceClaude Fable 5.190.175%GPT-5.5 Pro87.719%Claude Fable 5.1 leads this result
FrontierMath v2 (Tier 4)
Shared sourceClaude Fable 5.187.805%GPT-5.5 Pro78.049%Claude Fable 5.1 leads this result
FrontierMath (legacy)
Claude Fable 5.1—GPT-5.5 Pro52.4%SourceNot directly comparable
35 public results · 6 shared
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Last updated October 17, 2026