Updated October 17, 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, 83.18 versus 49.52, and the 90% score intervals do not overlap. 6 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, 76.8 to 41.8, 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, 80.9 to 38.5, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
Claude Fable 5.1
Claude Fable 5.1 has the larger documented context window.
1K fresh input + 500 output tokens
Kimi K2.7 Code
Kimi K2.7 Code 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
Kimi K2.7 Code
Kimi K2.7 Code has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Kimi K2.7 Code
Kimi K2.7 Code has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
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.
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 75.6FrontierMath v2 (Tiers 1-3)Math
Normalized gap 36.1CursorBench 3.2Coding
Normalized gap 23.7LiveCodeBench (Vals)Coding
Normalized gap 8.4Each 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 | Kimi K2.7 Code | Basis | Reading |
|---|---|---|---|---|
| Agentic | 80.9Supported · #2/128 | 38.5Supported · #69/128 | Like-for-likeBenchAlign v5.8 lane · 12 vs 4 public rows | Claude Fable 5.1 leads |
| Coding | 76.8Supported · #3/150 | 41.8Supported · #62/150 | Like-for-likeBenchAlign v5.8 lane · 11 vs 7 public rows | Claude Fable 5.1 leads |
| Knowledge | 86.9Supported · #2/184 | 54.3Estimated · #64/184 | Directional onlyBenchAlign v5.8 lane · 4 vs 0 public rows | Directional only |
| Reasoning | 85.2#4/28 | 80.3Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | 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 | 76.6#63/148 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 96.8Unranked · 2 rankable rows | 58.3Unranked · 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
Kimi K2.7 Code has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Kimi K2.7 Code has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Kimi K2.7 Code 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
Kimi K2.7 Code
256K
Claude Fable 5.1
claude-fable-5-1
Anthropic Fable 5.1 and Mythos 5.1 launchKimi K2.7 Code
Not sourced
A 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 launchKimi K2.7 Code
$0.19 per 1M cached input tokens
Claude Fable 5.1
Not sourced
Kimi K2.7 Code
Not sourced
Claude Fable 5.1
Not sourced
Kimi K2.7 Code
Not sourced
Claude Fable 5.1
Generally Available · Claude API, Claude products, AWS, Google Cloud, Microsoft Azure
Anthropic Fable 5.1 and Mythos 5.1 launchKimi K2.7 Code
Not sourced
Claude Fable 5.1
Reasoning
Kimi K2.7 Code
Reasoning
Claude Fable 5.1
Proprietary
Kimi K2.7 Code
Open Weight
Claude Fable 5.1
Proprietary
Kimi K2.7 Code
Open Weight
Claude Fable 5.1
2026-09-01
Kimi K2.7 Code
2026-06-12
Claude Fable 5.1 has the higher public score, 83.18 versus 49.52, 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, 76.8 to 41.8, 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, 80.9 to 38.5, 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 5.1 and $0.00295 on Kimi K2.7 Code; repository review costs $0.65 and $0.0595; the cache-heavy agent loop costs $0.75 and $0.097. Costs use the listed standard API rates.
Claude Fable 5.1 has the larger documented context window: 1M, compared with 256K.
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
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)
Claude Fable 5.1 leads this result
ApprenticeBench
Not directly comparable
CWE-bench v1
Not directly comparable
MazeBench
Not directly comparable
Kimi Claw 24/7
Not directly comparable
MCP Atlas
Not directly comparable
MCP Mark Verified
Not directly comparable
Bug Hunt Bench
Not directly comparable
SWE-bench Pro
Not directly comparable
SWE Multilingual
Not directly comparable
SWE Multimodal
Not directly comparable
DeepSWE
Not directly comparable
FrontierSWE v2
Not directly comparable
ProgramBench
Claude Fable 5.1 leads this result
CursorBench 3.2
Shared sourceClaude Fable 5.1 leads this result
LiveCodeBench (Vals)
Claude Fable 5.1 leads this result
CursorBench 4.0
Not directly comparable
PostTrainBench v1.1
Not directly comparable
Kimi Code Bench v2
Not directly comparable
MLS-Bench Lite
Not directly comparable
OpenHarmony Bench
Not directly comparable
SWE-bench (Vals)
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
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
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 5,600+ readers.
Last updated October 17, 2026
