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BenchLM
Model A
Kimi K2.7 Code

Moonshot AI

54.4/100

Estimated · Public rank #101

90% interval 42.8–65.9

Kimi K2.7 Code vs Ornith-1.5-397B

Updated August 19, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Ornith-1.5-397B

Ornith AI

68.5/100

Estimated · Public rank #21

90% interval 58.6–78.4

Decision reading

Ornith-1.5-397B has the higher public score estimate, 68.5 versus 54.35, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

1 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

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.

  • Long documents

    Prompts that approach the documented context limit

    Ornith-1.5-397B

    Ornith-1.5-397B has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Chat turn cost

    1K fresh input + 500 output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

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

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: rate-fallback

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

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
Kimi K2.7 Code only
7
Ornith-1.5-397B only
17
Like-for-like categories
0 / 8

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Agentic

Not comparable
Kimi K2.7 Code
Not measured
Ornith-1.5-397B
86.6
Weighted basis
0 vs 1 rows
Reading
Not comparable

Coding

Not comparable
Kimi K2.7 Code
Not measured
Ornith-1.5-397B
78.0
Weighted basis
0 vs 2 rows
Reading
Not comparable

Reasoning

Not comparable
Kimi K2.7 Code
Not measured
Ornith-1.5-397B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Kimi K2.7 Code
Not measured
Ornith-1.5-397B
51.1
Weighted basis
0 vs 2 rows
Reading
Not comparable

Math

Not comparable
Kimi K2.7 Code
Not measured
Ornith-1.5-397B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Kimi K2.7 Code
Not measured
Ornith-1.5-397B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Kimi K2.7 Code
Not measured
Ornith-1.5-397B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Kimi K2.7 Code
Not measured
Ornith-1.5-397B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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

Kimi K2.7 Code
$0.00295
Fits in one request
Ornith-1.5-397B
Self-hosted; infrastructure cost varies
Fits in one request

Ornith-1.5-397B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Kimi K2.7 Code
$0.0595
Fits in one request
Ornith-1.5-397B
Self-hosted; infrastructure cost varies
Fits in one request

Ornith-1.5-397B has no comparable published API token rate.

Cache-heavy agent loop

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

Kimi K2.7 Code
$0.249
Fits in one request
Cached input priced at the published list-input rate
Ornith-1.5-397B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

Kimi K2.7 Code has no published cached-input rate, so cached tokens use its listed input rate. Ornith-1.5-397B has no comparable published API token 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.

Kimi K2.7 Code

256K

Ornith-1.5-397B

262K

Ornith-1.5-397B model card

API model ID

Kimi K2.7 Code

Not sourced

Ornith-1.5-397B

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Kimi K2.7 Code

Not published

Ornith-1.5-397B

No comparable hosted API rate

Ornith-1.5-397B model card

Documented inputs

Kimi K2.7 Code

Not sourced

Ornith-1.5-397B

Not sourced

Documented outputs

Kimi K2.7 Code

Not sourced

Ornith-1.5-397B

Not sourced

Provider availability

Kimi K2.7 Code

Not sourced

Ornith-1.5-397B

Not sourced

Reasoning profile

Kimi K2.7 Code

Reasoning

Ornith-1.5-397B

Reasoning

Weight access

Kimi K2.7 Code

Open Weight

Ornith-1.5-397B

Open Weight

License

Kimi K2.7 Code

Open Weight

Ornith-1.5-397B

Open Weight

Release date

Kimi K2.7 Code

2026-06-12

Ornith-1.5-397B

2026-08-18

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
Ornith-1.5-397B has the higher public score estimate, 68.5 versus 54.35, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Ornith-1.5-397B has the larger documented window (262K).

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.

Kimi K2.7 Code
API / mo$3,713
Self-host / mo$18,221
Break-even326M/day
Ornith-1.5-397B
API / mo$0
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
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

  • Kimi Claw 24/7

    Kimi K2.7 Code46.9%
    Source
    Ornith-1.5-397B—

    Not directly comparable

  • MCP Atlas

    Kimi K2.7 Code76%
    Source
    Ornith-1.5-397B80%
    Source

    Ornith-1.5-397B leads this result

  • MCP Mark Verified

    Kimi K2.7 Code81.1%
    Source
    Ornith-1.5-397B—

    Not directly comparable

  • Terminal-Bench 2.1

    Kimi K2.7 Code—
    Ornith-1.5-397B86.1%
    Source

    Not directly comparable

  • HLE w/ tools

    Kimi K2.7 Code—
    Ornith-1.5-397B56.1%
    Source

    Not directly comparable

  • Toolathlon-Verified

    Kimi K2.7 Code—
    Ornith-1.5-397B71.2%
    Source

    Not directly comparable

  • WideResearch

    Kimi K2.7 Code—
    Ornith-1.5-397B80.8%
    Source

    Not directly comparable

  • BrowseComp

    Kimi K2.7 Code—
    Ornith-1.5-397B86.6%
    Source

    Not directly comparable

  • Claw-Eval

    Kimi K2.7 Code—
    Ornith-1.5-397B81.4%
    Source

    Not directly comparable

Coding

  • Kimi Code Bench v2

    Kimi K2.7 Code62.0%
    Source
    Ornith-1.5-397B—

    Not directly comparable

  • ProgramBench

    Kimi K2.7 Code53.6%
    Source
    Ornith-1.5-397B—

    Not directly comparable

  • MLS-Bench Lite

    Kimi K2.7 Code35.1%
    Source
    Ornith-1.5-397B—

    Not directly comparable

  • cursorBench32

    Kimi K2.7 Code49.7%
    Source
    Ornith-1.5-397B—

    Not directly comparable

  • EEBench

    Kimi K2.7 Code12.8%
    Source
    Ornith-1.5-397B—

    Not directly comparable

  • Terminal-Bench 2.1

    Kimi K2.7 Code—
    Ornith-1.5-397B86.1%
    Source

    Not directly comparable

  • SWE-bench Verified

    Kimi K2.7 Code—
    Ornith-1.5-397B86%
    Source

    Not directly comparable

  • SWE-bench Pro

    Kimi K2.7 Code—
    Ornith-1.5-397B65.1%
    Source

    Not directly comparable

  • SWE Multilingual

    Kimi K2.7 Code—
    Ornith-1.5-397B79.6%
    Source

    Not directly comparable

  • deepSwe

    Kimi K2.7 Code—
    Ornith-1.5-397B56%
    Source

    Not directly comparable

  • frontierBench

    Kimi K2.7 Code—
    Ornith-1.5-397B13.5%
    Source

    Not directly comparable

  • NL2Repo

    Kimi K2.7 Code—
    Ornith-1.5-397B59.5%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Kimi K2.7 Code—
    Ornith-1.5-397B92.8%
    Source

    Not directly comparable

  • GPQA-D

    Kimi K2.7 Code—
    Ornith-1.5-397B92.8%
    Source

    Not directly comparable

  • HLE

    Kimi K2.7 Code—
    Ornith-1.5-397B44.6%
    Source

    Not directly comparable

  • HLE w/o tools

    Kimi K2.7 Code—
    Ornith-1.5-397B44.6%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Kimi K2.7 Code or Ornith-1.5-397B?

Ornith-1.5-397B has the higher public score estimate, 68.5 versus 54.35, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Kimi K2.7 Code or Ornith-1.5-397B?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, Kimi K2.7 Code or Ornith-1.5-397B?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, Kimi K2.7 Code or Ornith-1.5-397B?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, Kimi K2.7 Code or Ornith-1.5-397B?

Ornith-1.5-397B has the larger documented context window: 262K, compared with 256K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 19, 2026

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