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BenchLM
Model A
Kimi K2.7 Code

Moonshot AI

54.0/100

Estimated · Public rank #98

90% interval 42.5–65.6

Kimi K2.7 Code vs Ornith-1.0-397B

Updated August 13, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Ornith-1.0-397B

DeepReinforce AI

—

Evidence status unavailable

90% interval unavailable

Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 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.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

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

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

  • 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
0
Kimi K2.7 Code only
8
Ornith-1.0-397B only
7
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-1.0-397B
77.5
Weighted basis
0 vs 1 rows
Reading
Not comparable

Coding

Not comparable
Kimi K2.7 Code
Not measured
Ornith-1.0-397B
74.6
Weighted basis
0 vs 2 rows
Reading
Not comparable

Reasoning

Not comparable
Kimi K2.7 Code
Not measured
Ornith-1.0-397B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Kimi K2.7 Code
Not measured
Ornith-1.0-397B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Kimi K2.7 Code
Not measured
Ornith-1.0-397B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Kimi K2.7 Code
Not measured
Ornith-1.0-397B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Kimi K2.7 Code
Not measured
Ornith-1.0-397B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Kimi K2.7 Code
Not measured
Ornith-1.0-397B
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-1.0-397B
Self-hosted; infrastructure cost varies
Fits in one request

Ornith-1.0-397B 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-1.0-397B
Self-hosted; infrastructure cost varies
Fits in one request

Ornith-1.0-397B 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-1.0-397B
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-1.0-397B 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-1.0-397B

256K

API model ID

Kimi K2.7 Code

Not sourced

Ornith-1.0-397B

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-1.0-397B

No comparable hosted API rate

Documented inputs

Kimi K2.7 Code

Not sourced

Ornith-1.0-397B

Not sourced

Documented outputs

Kimi K2.7 Code

Not sourced

Ornith-1.0-397B

Not sourced

Provider availability

Kimi K2.7 Code

Not sourced

Ornith-1.0-397B

Not sourced

Reasoning profile

Kimi K2.7 Code

Reasoning

Ornith-1.0-397B

Reasoning

Weight access

Kimi K2.7 Code

Open Weight

Ornith-1.0-397B

Open Weight

License

Kimi K2.7 Code

Open Weight

Ornith-1.0-397B

Open Weight

Release date

Kimi K2.7 Code

2026-06-12

Ornith-1.0-397B

2026-06-01

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Both models list 256K.

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-1.0-397B
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 evidence15 rows

Agentic

  • Kimi Claw 24/7

    Kimi K2.7 Code46.9%
    Source
    Ornith-1.0-397B—

    Not directly comparable

  • MCP Atlas

    Kimi K2.7 Code76%
    Source
    Ornith-1.0-397B—

    Not directly comparable

  • MCP Mark Verified

    Kimi K2.7 Code81.1%
    Source
    Ornith-1.0-397B—

    Not directly comparable

  • Terminal-Bench 2.0

    Kimi K2.7 Code—
    Ornith-1.0-397B77.5%
    Source

    Not directly comparable

  • Claw-Eval

    Kimi K2.7 Code—
    Ornith-1.0-397B77.1%
    Source

    Not directly comparable

Coding

  • Kimi Code Bench v2

    Kimi K2.7 Code62.0%
    Source
    Ornith-1.0-397B—

    Not directly comparable

  • ProgramBench

    Kimi K2.7 Code53.6%
    Source
    Ornith-1.0-397B—

    Not directly comparable

  • MLS-Bench Lite

    Kimi K2.7 Code35.1%
    Source
    Ornith-1.0-397B—

    Not directly comparable

  • cursorBench32

    Kimi K2.7 Code49.7%
    Source
    Ornith-1.0-397B—

    Not directly comparable

  • EEBench

    Kimi K2.7 Code12.8%
    Source
    Ornith-1.0-397B—

    Not directly comparable

  • SWE-bench Verified

    Kimi K2.7 Code—
    Ornith-1.0-397B82.4%
    Source

    Not directly comparable

  • SWE-bench Pro

    Kimi K2.7 Code—
    Ornith-1.0-397B62.2%
    Source

    Not directly comparable

  • SWE Multilingual

    Kimi K2.7 Code—
    Ornith-1.0-397B78.9%
    Source

    Not directly comparable

  • NL2Repo

    Kimi K2.7 Code—
    Ornith-1.0-397B48.2%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Kimi K2.7 Code—
    Ornith-1.0-397B77.5%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Kimi K2.7 Code or Ornith-1.0-397B?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Kimi K2.7 Code or Ornith-1.0-397B?

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-1.0-397B?

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-1.0-397B?

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-1.0-397B?

Both models list the same context window, 256K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 13, 2026

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