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Model A
Claude Fable 5

Anthropic

81.43/100

Supported · Public rank #5

90% interval 77.685.3

Claude Fable 5 vs Kimi K2.7 Code

Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Moonshot AI logo
Model B
Kimi K2.7 Code

Moonshot AI

65.49/100

Estimated · Public rank #36

90% interval 49.071.2

Decision reading

Claude Fable 5 has the higher public score, 81.43 versus 65.49, and the 90% score intervals do not overlap.

1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

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Which one for your work

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.

  • Coding work

    Code generation, repair, and software-engineering tasks

    Claude Fable 5

    Claude Fable 5 leads on the public coding lane, 77 to 50.9, with Supported evidence for both models and non-overlapping 90% intervals.

    Confidence: stronger

  • Long documents

    Prompts that approach the documented context limit

    Claude Fable 5

    Claude Fable 5 has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    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.

    Confidence: listed-rates

Show secondary and unsupported calls
  • Cache-heavy agent loop cost

    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. Kimi K2.7 Code has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

  • Repository review cost

    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.

    Confidence: listed-rates

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    Kimi K2.7 Code is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    Confidence: limited

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

Shared results
1
Claude Fable 5 only
17
Kimi K2.7 Code only
7
Like-for-like categories
1 / 8

3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

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.

Coding

Like-for-like
Claude Fable 5
77.0
Supported · #2/151
Kimi K2.7 Code
50.9
Supported · #51/151
Basis
BenchAlign lane · 10 vs 5 public rows
Reading
Claude Fable 5 leads

Agentic

Directional only
Claude Fable 5
74.6
Supported · #3/152
Kimi K2.7 Code
46.8
Estimated · #72/152
Basis
BenchAlign lane · 4 vs 3 public rows
Reading
Directional only

Knowledge

Directional only
Claude Fable 5
83.3
Supported · #2/183
Kimi K2.7 Code
61.6
Estimated · #31/183
Basis
BenchAlign lane · 2 vs 0 public rows
Reading
Directional only

Instruction following

Directional only
Claude Fable 5
78.5
#56/123
Kimi K2.7 Code
76.6
#58/123
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Directional only

Reasoning

Not comparable
Claude Fable 5
77.6
#6/20
Kimi K2.7 Code
75.5
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Claude Fable 5
Not ranked
Kimi K2.7 Code
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Claude Fable 5
Not ranked
Kimi K2.7 Code
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Claude Fable 5
62.5
Unranked · 2 rankable rows
Kimi K2.7 Code
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
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.

Shape of the matched evidence

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.

  • cursorBench32

    Coding

    Claude Fable 5: 70.5%Kimi K2.7 Code: 49.7%Normalized gap 20.8Shared source

What each workload costs

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.

Chat turn

1K fresh input + 500 output tokens

Claude Fable 5
$0.035
Fits in one request
Kimi K2.7 Code
$0.00295
Fits in one request

Kimi K2.7 Code has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Fable 5
$0.65
Fits in one request
Kimi K2.7 Code
$0.0595
Fits in one request

Kimi K2.7 Code has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Claude Fable 5
$0.9
Fits in one request
Kimi K2.7 Code
$0.249
Fits in one request
Cached input priced at the published list-input rate

Kimi K2.7 Code has the lower modeled cost

Kimi K2.7 Code has no published cached-input rate, so cached tokens use its listed input rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Context window

Maximum documented context; output-token limits may be lower.

Claude Fable 5

Kimi K2.7 Code

256K

Cached-input rate

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 pricing

Kimi K2.7 Code

Not published

Reasoning profile

Claude Fable 5

Reasoning

Kimi K2.7 Code

Reasoning

Weight access

Claude Fable 5

Proprietary

Kimi K2.7 Code

Open Weight

License

Claude Fable 5

Proprietary

Kimi K2.7 Code

Open Weight

Release date

Claude Fable 5

2026-06-09

Kimi K2.7 Code

2026-06-12

If you already use one of these models
Deployment change
The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal
Claude Fable 5 has the higher public score, 81.43 versus 65.49, and the 90% score intervals do not overlap.
Workload cost
Repository review: $0.65 vs $0.0595. Cache-heavy agent loop: $0.9 vs $0.249.
Context tradeoff
Claude Fable 5 has the larger documented window (1M).

Run the same representative tasks against both endpoints before changing production traffic.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Claude Fable 5
API / mo$0
Self-host / moNot listed
Break-even
Proprietary model — self-hosting not applicable.
Kimi K2.7 Code
API / mo$3,713
Self-host / mo$18,221
Break-even326M/day
Model the full break-even

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence25 rows

Agentic

  • Terminal-Bench 3.0

    Claude Fable 534.0%
    Source
    Kimi K2.7 Code

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Fable 584.3%
    Source
    Kimi K2.7 Code

    Not directly comparable

  • OSWorld-Verified

    Claude Fable 585%
    Source
    Kimi K2.7 Code

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Claude Fable 580.5%
    Source
    Kimi K2.7 Code

    Not directly comparable

  • Kimi Claw 24/7

    Claude Fable 5
    Kimi K2.7 Code46.9%
    Source

    Not directly comparable

  • MCP Atlas

    Claude Fable 5
    Kimi K2.7 Code76%
    Source

    Not directly comparable

  • MCP Mark Verified

    Claude Fable 5
    Kimi K2.7 Code81.1%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Fable 595%
    Source
    Kimi K2.7 Code

    Not directly comparable

  • SWE-bench Pro

    Claude Fable 580%
    Source
    Kimi K2.7 Code

    Not directly comparable

  • FrontierSWE v2

    Claude Fable 547.0%
    Source
    Kimi K2.7 Code

    Not directly comparable

  • FrontierCode 1.1 Main

    Claude Fable 553.5%
    Source
    Kimi K2.7 Code

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Fable 584.3%
    Source
    Kimi K2.7 Code

    Not directly comparable

  • cursorBench31

    Claude Fable 570.6%
    Source
    Kimi K2.7 Code

    Not directly comparable

  • cursorBench32

    Shared source
    Claude Fable 570.5%
    Kimi K2.7 Code49.7%

    Claude Fable 5 leads this result

  • VulcanBench v3

    Claude Fable 589.5%
    Source
    Kimi K2.7 Code

    Not directly comparable

  • LiveCodeBench (Vals)

    Claude Fable 589.8%
    Source
    Kimi K2.7 Code

    Not directly comparable

  • SWE-bench (Vals)

    Claude Fable 595.0%
    Source
    Kimi K2.7 Code

    Not directly comparable

  • Kimi Code Bench v2

    Claude Fable 5
    Kimi K2.7 Code62.0%
    Source

    Not directly comparable

  • ProgramBench

    Claude Fable 5
    Kimi K2.7 Code53.6%
    Source

    Not directly comparable

  • MLS-Bench Lite

    Claude Fable 5
    Kimi K2.7 Code35.1%
    Source

    Not directly comparable

  • OpenHarmony Bench

    Claude Fable 5
    Kimi K2.7 Code52.1%
    Source

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    Claude Fable 593.2%
    Source
    Kimi K2.7 Code

    Not directly comparable

  • MMLU-Pro (Vals)

    Claude Fable 591.5%
    Source
    Kimi K2.7 Code

    Not directly comparable

Multimodal

  • Blueprint-Bench 2

    Claude Fable 538.6%
    Source
    Kimi K2.7 Code

    Not directly comparable

  • OfficeQA Pro

    Claude Fable 557.9%
    Source
    Kimi K2.7 Code

    Not directly comparable

Frequently asked questions

Which is better, Claude Fable 5 or Kimi K2.7 Code?

Claude Fable 5 has the higher public score, 81.43 versus 65.49, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, Claude Fable 5 or Kimi K2.7 Code?

Claude Fable 5 leads the public coding lane, 77 to 50.9, with Supported evidence for both models and non-overlapping 90% intervals.

Which is better for agentic tasks, Claude Fable 5 or Kimi K2.7 Code?

Claude Fable 5 scores higher for agentic tasks on the public lane, 74.6 to 46.8. Kimi K2.7 Code 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.

Which costs less, Claude Fable 5 or Kimi K2.7 Code?

For the stated presets, chat costs $0.035 on Claude Fable 5 and $0.00295 on Kimi K2.7 Code; repository review costs $0.65 and $0.0595; the cache-heavy agent loop costs $0.9 and $0.249. Kimi K2.7 Code has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Claude Fable 5 or Kimi K2.7 Code?

Claude Fable 5 has the larger documented context window: 1M, compared with 256K.

Related comparisons

Last updated September 10, 2026

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