Agentic
Directional only- Claude Fable 5
- 84.6
- GPT-5.5
- 81.6
- Weighted basis
- 2 vs 3 rows
- Reading
- Directional only
Claude Fable 5 vs GPT-5.5
Updated August 22, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Claude Fable 5 has the higher public score estimate, 82.7 versus 73.33, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
8 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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-5.5
GPT-5.5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GPT-5.5
GPT-5.5 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-5.5
GPT-5.5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
4 Claude Fable 5 only
8 Shared
25 GPT-5.5 only
- Shared results
- 8
- Claude Fable 5 only
- 4
- GPT-5.5 only
- 25
- Like-for-like categories
- 0 / 8
3 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Coding
Directional only- Claude Fable 5
- 89.2
- GPT-5.5
- 58.6
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Multimodal
Directional only- Claude Fable 5
- 57.9
- GPT-5.5
- 70.4
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
Reasoning
Not comparable- Claude Fable 5
- Not measured
- GPT-5.5
- 85.0
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Knowledge
Not comparable- Claude Fable 5
- Not measured
- GPT-5.5
- 57.8
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Math
Not comparable- Claude Fable 5
- Not measured
- GPT-5.5
- 47.6
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Fable 5
- Not measured
- GPT-5.5
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Fable 5
- Not measured
- GPT-5.5
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Claude Fable 5 | GPT-5.5 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 84.6 | 81.6 | Directional only2 vs 3 rows | Directional only |
| Coding | 89.2 | 58.6 | Directional only2 vs 1 rows | Directional only |
| Multimodal | 57.9 | 70.4 | Directional only1 vs 2 rows | Directional only |
| Reasoning | Not measured | 85.0 | Not comparable0 vs 1 rows | Not comparable |
| Knowledge | Not measured | 57.8 | Not comparable0 vs 2 rows | Not comparable |
| Math | Not measured | 47.6 | Not comparable0 vs 2 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
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.
SWE-bench Pro
Coding
OSWorld-Verified
Agentic
OfficeQA Pro
Multimodal
Terminal-Bench 2.0
Agentic
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
- GPT-5.5
- $0.02
- Fits in one request
GPT-5.5 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude Fable 5
- $0.65
- Fits in one request
- GPT-5.5
- $0.34
- Fits in one request
GPT-5.5 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
- $0.9
- Fits in one request
- GPT-5.5
- $0.5
- Fits in one request
GPT-5.5 has the lower modeled cost
Costs use the listed standard API rates.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationClaude Fable 5GPT-5.5
Context window
Maximum documented context; output-token limits may be lower.
Claude Fable 5
GPT-5.5
API model ID
Claude Fable 5
claude-fable-5
Anthropic model overviewGPT-5.5
gpt-5.5
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
$1 per 1M cached input tokens
Claude API pricingGPT-5.5
$0.5 per 1M cached input tokens
OpenAI pricingDocumented inputs
Claude Fable 5
text, image
Anthropic model overviewGPT-5.5
Not sourced
Documented outputs
Claude Fable 5
GPT-5.5
Not sourced
Provider availability
Claude Fable 5
Generally Available · Claude API
Anthropic model overviewGPT-5.5
Not sourced
Reasoning profile
Claude Fable 5
Reasoning
GPT-5.5
Reasoning
Weight access
Claude Fable 5
Proprietary
GPT-5.5
Proprietary
License
Claude Fable 5
Proprietary
GPT-5.5
Proprietary
Release date
Claude Fable 5
2026-06-09
GPT-5.5
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 has the higher public score estimate, 82.7 versus 73.33, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.65 vs $0.34. Cache-heavy agent loop: $0.9 vs $0.5.
- Context tradeoff
- Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence37 rows
Agentic
Terminal-Bench 3.0
Claude Fable 534.0%SourceGPT-5.5—Not directly comparable
Terminal-Bench 2.0
Claude Fable 584.3%SourceGPT-5.582%SourceClaude Fable 5 leads this result
OSWorld-Verified
Claude Fable 585%SourceGPT-5.578.7%SourceClaude Fable 5 leads this result
CyberGym
Claude Fable 5—GPT-5.581.8%SourceNot directly comparable
BrowseComp
Claude Fable 5—GPT-5.584.4%SourceNot directly comparable
MCP Atlas
Claude Fable 5—GPT-5.575.3%SourceNot directly comparable
Toolathlon
Claude Fable 5—GPT-5.555.6%SourceNot directly comparable
τ²-bench results
Claude Fable 5—GPT-5.598%SourceNot directly comparable
Gert Labs
Claude Fable 5—GPT-5.572.93%SourceNot directly comparable
ResearchClawBench
Claude Fable 5—GPT-5.517.0%SourceNot directly comparable
OSWorld 2.0
Claude Fable 5—GPT-5.513.0%SourceNot directly comparable
JobBench
Claude Fable 5—GPT-5.542.7%SourceNot directly comparable
ExploitGym
Claude Fable 5—GPT-5.513.4%SourceNot directly comparable
Coding
SWE-bench Verified
Claude Fable 595%SourceGPT-5.5—Not directly comparable
SWE-bench Pro
Claude Fable 580%SourceGPT-5.558.6%SourceClaude Fable 5 leads this result
FrontierCode 1.1 Main
Shared sourceClaude Fable 553.5%GPT-5.543.0%Claude Fable 5 leads this result
Terminal-Bench 2.0
Claude Fable 584.3%SourceGPT-5.582.0%SourceClaude Fable 5 leads this result
cursorBench31
Shared sourceClaude Fable 570.6%GPT-5.559.2%Claude Fable 5 leads this result
cursorBench32
Shared sourceClaude Fable 570.5%GPT-5.558.4%Claude Fable 5 leads this result
VulcanBench v3
Claude Fable 587.0%SourceGPT-5.5—Not directly comparable
Vibe Code Bench
Claude Fable 5—GPT-5.569.85%SourceNot directly comparable
React Native Evals
Claude Fable 5—GPT-5.584.7%SourceNot directly comparable
Reasoning
Knowledge
Math
Multimodal
Blueprint-Bench 2
Claude Fable 538.6%SourceGPT-5.5—Not directly comparable
OfficeQA Pro
Claude Fable 557.9%SourceGPT-5.554.1%SourceClaude Fable 5 leads this result
MMMU-Pro
Claude Fable 5—GPT-5.581.2%SourceNot directly comparable
MMMU-Pro w/ Python
Claude Fable 5—GPT-5.583.2%SourceNot directly comparable
Frequently asked questions
Which is better, Claude Fable 5 or GPT-5.5?
Claude Fable 5 has the higher public score estimate, 82.7 versus 73.33, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Claude Fable 5 or GPT-5.5?
The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, Claude Fable 5 or GPT-5.5?
The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which costs less, Claude Fable 5 or GPT-5.5?
For the stated presets, chat costs $0.035 on Claude Fable 5 and $0.02 on GPT-5.5; repository review costs $0.65 and $0.34; the cache-heavy agent loop costs $0.9 and $0.5. Costs use the listed standard API rates.
Which has the larger context window, Claude Fable 5 or GPT-5.5?
Both models list the same context window, 1M.
Related comparisons
Last updated August 22, 2026
Watch Claude Fable 5 vs GPT-5.5
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.