Updated November 6, 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.
Claude Fable 5.1 has the higher public score, 81.3 versus 62.59, and the 90% score intervals do not overlap. 9 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
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 5.1
Claude Fable 5.1 has the higher public coding point estimate, 76.6 to 51.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
GPT-6 Luna
GPT-6 Luna has the larger documented context window.
1K fresh input + 500 output tokens
GPT-6 Luna
GPT-6 Luna 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
GPT-6 Luna
GPT-6 Luna has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
GPT-6 Luna
GPT-6 Luna has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-6 Luna is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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 5.1 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.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 categories rest 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 31.7ARC-AGI-2Reasoning
Normalized gap 30.7FrontierMath v2 (Tiers 1-3)Math
Normalized gap 11.2Each 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.
| Category | Claude Fable 5.1 | GPT-6 Luna | Basis | Reading |
|---|---|---|---|---|
| Coding | 76.6Supported · #4/157 | 51.8Supported · #42/157 | Like-for-likeBenchAlign v5.8 lane · 12 vs 3 public rows | Claude Fable 5.1 leads |
| Knowledge | 85.8Supported · #3/178 | 63.3Supported · #40/178 | Like-for-likeBenchAlign v5.8 lane · 4 vs 5 public rows | Claude Fable 5.1 leads |
| Agentic | 80.1Supported · #2/131 | 53.5Estimated · #45/131 | Directional onlyBenchAlign v5.8 lane · 13 vs 5 public rows | Directional only |
| Reasoning | 83.5#5/25 | 57.5#22/25 | Directional onlyProvisional lane · 1 vs 2 weighted rows | Directional only |
| Multimodal | Not ranked | 81.9Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 0 weighted 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 | 93.3Unranked · 2 rankable rows | 80.2Unranked · 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, 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
GPT-6 Luna has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
GPT-6 Luna has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
GPT-6 Luna has the lower modeled cost
Costs use the listed standard API rates.
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 5.1
GPT-6 Luna
Claude Fable 5.1
claude-fable-5-1
Anthropic Fable 5.1 and Mythos 5.1 launchGPT-6 Luna
gpt-6-luna
OpenAI GPT-6 Luna model documentationA 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-6 Luna
$0.01 per 1M cached input tokens
OpenAI GPT-6 Luna model documentationClaude Fable 5.1
Not sourced
GPT-6 Luna
text, image
OpenAI GPT-6 Luna model documentationClaude Fable 5.1
Not sourced
GPT-6 Luna
Claude Fable 5.1
Generally Available · Claude API, Claude products, AWS, Google Cloud, Microsoft Azure
Anthropic Fable 5.1 and Mythos 5.1 launchGPT-6 Luna
Generally Available · OpenAI Responses API, OpenAI Chat Completions API, ChatGPT Work, Codex
OpenAI GPT-6 Sol and Luna launchClaude Fable 5.1
Reasoning
GPT-6 Luna
Reasoning
Claude Fable 5.1
Proprietary
GPT-6 Luna
Proprietary
Claude Fable 5.1
Proprietary
GPT-6 Luna
Proprietary
Claude Fable 5.1
2026-09-01
GPT-6 Luna
2026-09-22
Claude Fable 5.1 has the higher public score, 81.3 versus 62.59, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Claude Fable 5.1 has the higher public coding point estimate, 76.6 to 51.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Claude Fable 5.1 scores higher for agentic tasks on the public lane, 80.1 to 53.5. GPT-6 Luna 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.
For the stated presets, chat costs $0.035 on Claude Fable 5.1 and $0.00035 on GPT-6 Luna; repository review costs $0.65 and $0.0065; the cache-heavy agent loop costs $0.75 and $0.009. Costs use the listed standard API rates.
GPT-6 Luna has the larger documented context window: 1.05M, compared with 1M.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 4.0
Not directly comparable
Terminal-Bench-Science 0.1
Not directly comparable
OSWorld 2.0
Not directly comparable
AutomationBench
Not directly comparable
Toolathlon-Verified
Not directly comparable
Toolathlon Verified Pass@3
Not directly comparable
Toolathlon Verified Pass³
Not directly comparable
Toolathlon Verified avg. turns
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
ApprenticeBench
Not directly comparable
CWE-bench v1
Not directly comparable
MazeBench
Claude Fable 5.1 leads this result
OSWorld 2.1
Not directly comparable
ExploitGym
Not directly comparable
Hermes Index
Not directly comparable
Hermes Bench
Not directly comparable
OSWorld 2.1 Offline
Not directly comparable
Bug Hunt Bench
Shared sourceClaude Fable 5.1 leads this result
SWE-bench Pro
Not directly comparable
SWE Multilingual
Not directly comparable
SWE Multimodal
Not directly comparable
DeepSWE
Claude Fable 5.1 leads this result
FrontierSWE v2
Not directly comparable
ProgramBench
Not directly comparable
CursorBench 3.2
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
CursorBench 4.0
Not directly comparable
PostTrainBench v1.1
Not directly comparable
FrontierCode 2.0
Shared sourceClaude Fable 5.1 leads this result
ARC-AGI-1
Claude Fable 5.1 leads this result
ARC-AGI-2
Claude Fable 5.1 leads this result
GraphWalks BFS 256K–1M
Not directly comparable
BioBasicsBench
Not directly comparable
ARC-AGI-3
Not directly comparable
ARC-AGI-3 Provider Adapter
Not directly comparable
ParseBench
Shared sourceClaude Fable 5.1 leads this result
ExtractBench Pro
Not directly comparable
ExtractBench (LlamaIndex)
Not directly comparable
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
HealthBench (raw)
Not directly comparable
HealthBench (length-adjusted)
Not directly comparable
HealthBench Professional
Not directly comparable
HealthBench Professional (raw)
Not directly comparable
HealthBench Hard
Not directly comparable
Gray Swan IPI (15 attempts)
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared sourceClaude Fable 5.1 leads this result
FrontierMath v2 (Tier 4)
Shared sourceClaude Fable 5.1 leads this result
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Last updated November 6, 2026
