Skip to main content
Radar

Five or fewer confirmed AI changes, with original sources, on mornings when something changed.A free source-linked morning brief.

Start free brief
BenchLM
Model A
Ornith-1.5-397B

Ornith AI

68.5/100

Estimated · Public rank #21

90% interval 58.6–78.4

Ornith-1.5-397B vs SWE-1.7

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

Model B
SWE-1.7

Cognition

—

Evidence status unavailable

90% interval unavailable

Decision reading

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality 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: listed-rates

  • 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
Ornith-1.5-397B only
17
SWE-1.7 only
3
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
Ornith-1.5-397B
86.6
SWE-1.7
81.5
Weighted basis
1 vs 1 rows
Reading
Not comparable

Coding

Not comparable
Ornith-1.5-397B
78.0
SWE-1.7
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Ornith-1.5-397B
Not measured
SWE-1.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Ornith-1.5-397B
51.1
SWE-1.7
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Math

Not comparable
Ornith-1.5-397B
Not measured
SWE-1.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Ornith-1.5-397B
Not measured
SWE-1.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Ornith-1.5-397B
Not measured
SWE-1.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Ornith-1.5-397B
Not measured
SWE-1.7
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

Ornith-1.5-397B
Self-hosted; infrastructure cost varies
Fits in one request
SWE-1.7
API rate not published
Fits in one request

Ornith-1.5-397B has no comparable published API token rate. SWE-1.7 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Ornith-1.5-397B
Self-hosted; infrastructure cost varies
Fits in one request
SWE-1.7
API rate not published
Fits in one request

Ornith-1.5-397B has no comparable published API token rate. SWE-1.7 has no comparable published API token rate.

Cache-heavy agent loop

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

Ornith-1.5-397B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable
SWE-1.7
API rate not published
Fits in one request
Cached-input rate unavailable

Ornith-1.5-397B has no comparable published API token rate. SWE-1.7 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.

Ornith-1.5-397B

262K

Ornith-1.5-397B model card

SWE-1.7

256K

API model ID

Ornith-1.5-397B

Not sourced

SWE-1.7

Not sourced

Cached-input rate

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

Ornith-1.5-397B

No comparable hosted API rate

Ornith-1.5-397B model card

SWE-1.7

No comparable hosted API rate

Documented inputs

Ornith-1.5-397B

Not sourced

SWE-1.7

Not sourced

Documented outputs

Ornith-1.5-397B

Not sourced

SWE-1.7

Not sourced

Provider availability

Ornith-1.5-397B

Not sourced

SWE-1.7

Not sourced

Reasoning profile

Ornith-1.5-397B

Reasoning

SWE-1.7

Reasoning

Weight access

Ornith-1.5-397B

Open Weight

SWE-1.7

Proprietary

License

Ornith-1.5-397B

Open Weight

SWE-1.7

Proprietary

Release date

Ornith-1.5-397B

2026-08-18

SWE-1.7

2026-07-08

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
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
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.

Benchmark evidence

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

Browse raw public benchmark evidence21 rows

Agentic

  • Terminal-Bench 2.1

    Ornith-1.5-397B86.1%
    Source
    SWE-1.7—

    Not directly comparable

  • HLE w/ tools

    Ornith-1.5-397B56.1%
    Source
    SWE-1.7—

    Not directly comparable

  • MCP Atlas

    Ornith-1.5-397B80%
    Source
    SWE-1.7—

    Not directly comparable

  • Toolathlon-Verified

    Ornith-1.5-397B71.2%
    Source
    SWE-1.7—

    Not directly comparable

  • WideResearch

    Ornith-1.5-397B80.8%
    Source
    SWE-1.7—

    Not directly comparable

  • BrowseComp

    Ornith-1.5-397B86.6%
    Source
    SWE-1.7—

    Not directly comparable

  • Claw-Eval

    Ornith-1.5-397B81.4%
    Source
    SWE-1.7—

    Not directly comparable

  • Terminal-Bench 2.0

    Ornith-1.5-397B—
    SWE-1.781.5%
    Source

    Not directly comparable

Coding

  • Terminal-Bench 2.1

    Ornith-1.5-397B86.1%
    Source
    SWE-1.7—

    Not directly comparable

  • SWE-bench Verified

    Ornith-1.5-397B86%
    Source
    SWE-1.7—

    Not directly comparable

  • SWE-bench Pro

    Ornith-1.5-397B65.1%
    Source
    SWE-1.7—

    Not directly comparable

  • SWE Multilingual

    Ornith-1.5-397B79.6%
    Source
    SWE-1.777.8%
    Source

    Ornith-1.5-397B leads this result

  • deepSwe

    Ornith-1.5-397B56%
    Source
    SWE-1.7—

    Not directly comparable

  • frontierBench

    Ornith-1.5-397B13.5%
    Source
    SWE-1.7—

    Not directly comparable

  • NL2Repo

    Ornith-1.5-397B59.5%
    Source
    SWE-1.7—

    Not directly comparable

  • FrontierCode 1.1 Main

    Ornith-1.5-397B—
    SWE-1.742.3%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Ornith-1.5-397B—
    SWE-1.781.5%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Ornith-1.5-397B92.8%
    Source
    SWE-1.7—

    Not directly comparable

  • GPQA-D

    Ornith-1.5-397B92.8%
    Source
    SWE-1.7—

    Not directly comparable

  • HLE

    Ornith-1.5-397B44.6%
    Source
    SWE-1.7—

    Not directly comparable

  • HLE w/o tools

    Ornith-1.5-397B44.6%
    Source
    SWE-1.7—

    Not directly comparable

Frequently asked questions

Which is better, Ornith-1.5-397B or SWE-1.7?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Ornith-1.5-397B or SWE-1.7?

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, Ornith-1.5-397B or SWE-1.7?

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, Ornith-1.5-397B or SWE-1.7?

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, Ornith-1.5-397B or SWE-1.7?

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

Watch Ornith-1.5-397B vs SWE-1.7

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 2,000+ readers.