Agentic
Like-for-like- Claude Fable 5
- 74.8
- Supported · #3/151
- Claude Sonnet 4.6
- 45.0
- Supported · #96/151
- Basis
- BenchAlign lane · 4 vs 8 public rows
- Reading
- Claude Fable 5 leads
Five or fewer confirmed AI changes, with original sources, on mornings when something changed.A free source-linked morning brief.
See the free Radar BriefUpdated September 4, 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, 80.9 versus 64.21, and the 90% score intervals do not overlap.
10 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
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 leads on the public coding lane, 76.9 to 52.4, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Tool use, computer use, and multi-step task completion
Claude Fable 5
Claude Fable 5 leads on the public agentic lane, 74.8 to 45, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Prompts that approach the documented context limit
Claude Fable 5
Claude Fable 5 has the larger documented context window.
Confidence: documented
1K fresh input + 500 output tokens
Claude Sonnet 4.6
Claude Sonnet 4.6 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
50K fresh input + 3K output tokens
Claude Sonnet 4.6
Claude Sonnet 4.6 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Claude Sonnet 4.6 does not fit this workload in one request. Claude Sonnet 4.6 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
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.
Each row shows the public-lane category score for both models: the BenchAlign 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.
| Category | Claude Fable 5 | Claude Sonnet 4.6 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 74.8Supported · #3/151 | 45.0Supported · #96/151 | Like-for-likeBenchAlign lane · 4 vs 8 public rows | Claude Fable 5 leads |
| Coding | 76.9Supported · #2/183 | 52.4Supported · #55/183 | Like-for-likeBenchAlign lane · 10 vs 8 public rows | Claude Fable 5 leads |
| Knowledge | 83.5Supported · #2/181 | 56.7Supported · #51/181 | Like-for-likeBenchAlign lane · 2 vs 6 public rows | Claude Fable 5 leads |
| Instruction following | 78.3#54/120 | 47.9#84/120 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 76.2#11/22 | 65.8Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 49.0Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 62.5Unranked · 2 rankable rows | 54.1#33/48 | Not comparableProvisional lane · 1 vs 1 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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.
Terminal-Bench 2.0
Agentic
SWE-bench Verified
Coding
OSWorld-Verified
Agentic
LiveCodeBench (Vals)
Coding
MMLU-Pro (Vals)
Knowledge
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 4.6 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Claude Sonnet 4.6 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Claude Sonnet 4.6 does not fit this workload in one request. Claude Sonnet 4.6 has no published cached-input rate, so cached tokens use its listed input rate.
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 4.6
200K
Claude Fable 5
claude-fable-5
Anthropic model overviewClaude Sonnet 4.6
Not sourced
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 pricingClaude Sonnet 4.6
Not published
Claude Fable 5
text, image
Anthropic model overviewClaude Sonnet 4.6
Not sourced
Claude Fable 5
Claude Sonnet 4.6
Not sourced
Claude Fable 5
Generally Available · Claude API
Anthropic model overviewClaude Sonnet 4.6
Not sourced
Claude Fable 5
Reasoning
Claude Sonnet 4.6
Non-Reasoning
Claude Fable 5
Proprietary
Claude Sonnet 4.6
Proprietary
Claude Fable 5
Proprietary
Claude Sonnet 4.6
Proprietary
Claude Fable 5
2026-06-09
Claude Sonnet 4.6
2026-02-01
Run the same representative tasks against both endpoints before changing production traffic.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 3.0
Not directly comparable
Terminal-Bench 2.0
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
Claw-Eval
Not directly comparable
CyberGym
Not directly comparable
Gert Labs
Not directly comparable
OSWorld 2.0
Not directly comparable
JobBench
Not directly comparable
SWE-bench Verified
Claude Fable 5 leads this result
SWE-bench Pro
Not directly comparable
FrontierSWE v2
Not directly comparable
FrontierCode 1.1 Main
Shared sourceClaude Fable 5 leads this result
Terminal-Bench 2.0
Not directly comparable
cursorBench31
Shared sourceClaude Fable 5 leads this result
cursorBench32
Not directly comparable
VulcanBench v3
Not directly comparable
LiveCodeBench (Vals)
Claude Fable 5 leads this result
SWE-bench (Vals)
Claude Fable 5 leads this result
SWE-Rebench
Not directly comparable
React Native Evals
Not directly comparable
Vibe Code Bench
Not directly comparable
GPQA Diamond (Vals)
Claude Fable 5 leads this result
MMLU-Pro (Vals)
Claude Fable 5 leads this result
GPQA
Not directly comparable
SuperGPQA
Not directly comparable
MMLU-Pro
Not directly comparable
HLE
Not directly comparable
Claude Fable 5 has the higher public score, 80.9 versus 64.21, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Claude Fable 5 leads the public coding lane, 76.9 to 52.4, with Supported evidence for both models and non-overlapping 90% intervals.
Claude Fable 5 leads the public agentic tasks lane, 74.8 to 45, with Supported evidence for both models and non-overlapping 90% intervals.
For the stated presets, chat costs $0.035 on Claude Fable 5 and $0.0105 on Claude Sonnet 4.6; repository review costs $0.65 and $0.195; the cache-heavy agent loop costs $0.9 and $0.81. Claude Sonnet 4.6 does not fit this workload in one request. Claude Sonnet 4.6 has no published cached-input rate, so cached tokens use its listed input rate.
Claude Fable 5 has the larger documented context window: 1M, compared with 200K.
Last updated September 4, 2026
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