Updated November 13, 2026. Rank says Claude Fable 5 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Claude Fable 5 has the higher public score, 76.29 versus 63.83, and the 90% score intervals do not overlap. 19 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Claude Fable 5: $10 input / $50 output
Claude Sonnet 5: $2 input / $10 output
Claude Fable 5: 1M
Claude Sonnet 5: 1M
Claude Fable 5: June 9, 2026
Claude Sonnet 5: June 30, 2026
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 Fable 5
Claude Fable 5 has the higher public coding point estimate, 67.8 to 51.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
Claude Fable 5
Claude Fable 5 has the higher public agentic point estimate, 72.7 to 62.5, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
1K fresh input + 500 output tokens
Claude Sonnet 5
Claude Sonnet 5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Claude Sonnet 5
Claude Sonnet 5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Claude Sonnet 5
Claude Sonnet 5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
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 Fable 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.
The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.
We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy
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.
FrontierMath v2 (Tier 4)Math
Normalized gap 60.9FrontierMath v2 (Tiers 1-3)Math
Normalized gap 21.4SWE-bench ProCoding
Normalized gap 16.8SWE-bench VerifiedCoding
Normalized gap 9.8CursorBench 3.2Coding
Normalized gap 9.0Each 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.
| Category | Claude Fable 5 | Claude Sonnet 5 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 72.7Supported · #10/135 | 62.5Supported · #22/135 | Like-for-likeBenchAlign v5.8 lane · 7 vs 10 public rows | Claude Fable 5 leads · intervals overlap |
| Coding | 67.8Supported · #12/162 | 51.7Supported · #40/162 | Like-for-likeBenchAlign v5.8 lane · 10 vs 11 public rows | Claude Fable 5 leads |
| Multimodal | 74.5Supported · #10/67 | 60.4Supported · #34/67 | Like-for-likeBenchAlign v5.8 lane · 3 vs 3 public rows | Claude Fable 5 leads · intervals overlap |
| Knowledge | 79.4Supported · #7/180 | 61.8Supported · #43/180 | Like-for-likeBenchAlign v5.8 lane · 2 vs 6 public rows | Claude Fable 5 leads · intervals overlap |
| Reasoning | 71.6Supported · #16/160 | 59.8Estimated · #32/160 | Directional onlyBenchAlign v5.8 lane · 2 vs 0 public rows | Directional only |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 77.1#67/157 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 90.4Unranked · 2 rankable rows | 64.9Unranked · 2 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, 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.
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
Claude Sonnet 5 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Claude Sonnet 5 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Claude Sonnet 5 has the lower modeled cost
Costs use the listed standard API rates.
Enter a month of tokens to price it at both models’ listed API rates.
Standard listed rates, no batch or volume discounts.
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 Fable 5
Claude Sonnet 5
Claude Fable 5
claude-fable-5
Anthropic model overviewClaude Sonnet 5
claude-sonnet-5
Anthropic model overviewA 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 pricingClaude Sonnet 5
$0.2 per 1M cached input tokens
Claude API pricingClaude Fable 5
text, image
Anthropic model overviewClaude Sonnet 5
text, image
Anthropic model overviewClaude Fable 5
Claude Sonnet 5
Claude Fable 5
Generally Available · Claude API
Anthropic model overviewClaude Sonnet 5
Generally Available · Claude API
Anthropic model overviewClaude Fable 5
Reasoning
Claude Sonnet 5
Reasoning
Claude Fable 5
Proprietary
Claude Sonnet 5
Proprietary
Claude Fable 5
Proprietary
Claude Sonnet 5
Proprietary
Claude Fable 5
2026-06-09
Claude Sonnet 5
2026-06-30
Claude Fable 5 has the higher public score, 76.29 versus 63.83, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Claude Fable 5 has the higher public coding point estimate, 67.8 to 51.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Claude Fable 5 has the higher public agentic tasks point estimate, 72.7 to 62.5, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.035 on Claude Fable 5 and $0.007 on Claude Sonnet 5; repository review costs $0.65 and $0.13; the cache-heavy agent loop costs $0.9 and $0.18. Costs use the listed standard API rates.
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 4.1
Shared sourceClaude Fable 5 leads this result
Terminal-Bench 3.0
Shared sourceClaude Fable 5 leads this result
Terminal-Bench 2.1
Claude Fable 5 leads this result
OSWorld-Verified
Claude Fable 5 leads this result
Terminal-Bench 2.1 (Vals)
Claude Fable 5 leads this result
ApprenticeBench
Shared sourceClaude Fable 5 leads this result
MazeBench
Claude Fable 5 leads this result
BrowseComp
Not directly comparable
HLE w/ tools
Not directly comparable
OSWorld 2.1
Not directly comparable
SWE-bench Verified
Claude Fable 5 leads this result
SWE-bench Pro
Claude Fable 5 leads this result
FrontierSWE v2
Not directly comparable
FrontierCode 1.1 Main
Shared sourceClaude Fable 5 leads this result
Terminal-Bench 2.1
Claude Fable 5 leads this result
cursorBench31
Not directly comparable
CursorBench 3.2
Shared sourceClaude Fable 5 leads this result
VulcanBench v3
Not directly comparable
LiveCodeBench (Vals)
Claude Fable 5 leads this result
SWE-bench (Vals)
Claude Fable 5 leads this result
SWE Multilingual
Not directly comparable
SWE Multimodal
Not directly comparable
VulcanBench CII v1
Not directly comparable
CursorBench 4.0
Not directly comparable
ParseBench
Shared sourceClaude Fable 5 leads this result
Blueprint-Bench 2
Not directly comparable
OfficeQA Pro
Not directly comparable
CharXiv
Not directly comparable
CharXiv w/o tools
Not directly comparable
GPQA Diamond (Vals)
Claude Fable 5 leads this result
MMLU-Pro (Vals)
Claude Fable 5 leads this result
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
HLE-Verified
Not directly comparable
LABBench2
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared sourceClaude Fable 5 leads this result
FrontierMath v2 (Tier 4)
Shared sourceClaude Fable 5 leads this result
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Last updated November 13, 2026
