Updated October 25, 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, 83.05 versus 65.78, and the 90% score intervals do not overlap. 12 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, 77 to 61.1, 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 5.1
Claude Fable 5.1 has the higher public agentic point estimate, 80.8 to 61.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
1K fresh input + 500 output tokens
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 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
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 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 5.1 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.
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 61.0AutomationBenchAgentic
Normalized gap 29.1FrontierMath v2 (Tiers 1-3)Math
Normalized gap 22.8HLEKnowledge
Normalized gap 22.4Each 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 | DeepSeek V4.1 Flash 1017 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 80.8Supported · #2/132 | 61.7Supported · #24/132 | Like-for-likeBenchAlign v5.8 lane · 12 vs 11 public rows | Claude Fable 5.1 leads |
| Coding | 77.0Supported · #3/153 | 61.1Supported · #18/153 | Like-for-likeBenchAlign v5.8 lane · 11 vs 8 public rows | Claude Fable 5.1 leads |
| Knowledge | 86.2Supported · #2/187 | 64.4Supported · #37/187 | Like-for-likeBenchAlign v5.8 lane · 4 vs 3 public rows | Claude Fable 5.1 leads |
| Reasoning | 85.2#5/27 | 82.5Unranked · 4 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 78.8Unranked · 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 | 96.8Unranked · 2 rankable rows | 70.1Unranked · 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.
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
DeepSeek V4.1 Flash 1017 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
DeepSeek V4.1 Flash 1017 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
DeepSeek V4.1 Flash 1017 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
DeepSeek V4.1 Flash 1017
Claude Fable 5.1
claude-fable-5-1
Anthropic Fable 5.1 and Mythos 5.1 launchDeepSeek V4.1 Flash 1017
deepseek-flash
DeepSeek models and pricingA 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 launchDeepSeek V4.1 Flash 1017
$0.006 per 1M cached input tokens
DeepSeek: Models & PricingClaude Fable 5.1
Not sourced
DeepSeek V4.1 Flash 1017
text, image
DeepSeek vision guideClaude Fable 5.1
Not sourced
DeepSeek V4.1 Flash 1017
Claude Fable 5.1
Generally Available · Claude API, Claude products, AWS, Google Cloud, Microsoft Azure
Anthropic Fable 5.1 and Mythos 5.1 launchDeepSeek V4.1 Flash 1017
Generally Available · DeepSeek API, open weights
DeepSeek-V4.1-Flash-1017 API changelogClaude Fable 5.1
Reasoning
DeepSeek V4.1 Flash 1017
Reasoning
Claude Fable 5.1
Proprietary
DeepSeek V4.1 Flash 1017
Open Weight
Claude Fable 5.1
Proprietary
DeepSeek V4.1 Flash 1017
Open Weight
Claude Fable 5.1
2026-09-01
DeepSeek V4.1 Flash 1017
2026-10-17
Claude Fable 5.1 has the higher public score, 83.05 versus 65.78, 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, 77 to 61.1, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Claude Fable 5.1 has the higher public agentic tasks point estimate, 80.8 to 61.7, 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 5.1 and $0.0009 on DeepSeek V4.1 Flash 1017; repository review costs $0.65 and $0.0186; the cache-heavy agent loop costs $0.75 and $0.0192. Costs use the listed standard API rates.
Both models list the same context window, 1M.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 4.0
Claude Fable 5.1 leads this result
Terminal-Bench-Science 0.1
Not directly comparable
OSWorld 2.0
Not directly comparable
AutomationBench
DeepSeek V4.1 Flash 1017 leads this result
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)
Claude Fable 5.1 leads this result
ApprenticeBench
Not directly comparable
CWE-bench v1
Shared sourceClaude Fable 5.1 leads this result
MazeBench
Claude Fable 5.1 leads this result
Terminal-Bench 2.1
Not directly comparable
terminalBench3
Not directly comparable
CyberGym
Not directly comparable
ExploitGym
Not directly comparable
HLE w/ tools
Not directly comparable
Agents' Last Exam
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
DeepSeek V4.1 Flash 1017 leads this result
FrontierSWE v2
Not directly comparable
ProgramBench
Claude Fable 5.1 leads this result
CursorBench 3.2
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
CursorBench 4.0
Not directly comparable
PostTrainBench v1.1
Not directly comparable
Codeforces
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
terminalBench3
Not directly comparable
NL2Repo
Not directly comparable
OpenHarmony Bench
Not directly comparable
ARC-AGI-1
Not directly comparable
ARC-AGI-2
Not directly comparable
GraphWalks BFS 256K–1M
Not directly comparable
STEMBench
Not directly comparable
JevBench 1.4
Not directly comparable
JevBench 1.5
Not directly comparable
ParseBench
Shared sourceClaude Fable 5.1 leads this result
ExtractBench (LlamaIndex)
Not directly comparable
Chartography (tools)
Not directly comparable
BabyVision w/ Python
Not directly comparable
ZeroBench w/ Python
Not directly comparable
HLE
Claude Fable 5.1 leads this result
HLE w/o tools
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
GPQA
Not directly comparable
GPQA-D
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
Apex
Not directly comparable
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Last updated October 25, 2026
