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Model comparison

Interfaze Beta vs Trinity-Large-Preview

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

Interfaze Beta

Interfaze

—

Evidence status unavailable

90% interval unavailable

Trinity-Large-Preview

Arcee AI

55.2/100

Estimated · Public rank #89

90% interval 43.7–66.7

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

    Interfaze Beta

    Interfaze Beta has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    Trinity-Large-Preview

    Trinity-Large-Preview has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

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

    Trinity-Large-Preview

    Trinity-Large-Preview has the lower estimated token cost for this stated workload. Interfaze Beta has no published cached-input rate, so cached tokens use its listed input rate. Trinity-Large-Preview has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    Trinity-Large-Preview

    Trinity-Large-Preview has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • 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

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
Interfaze Beta only
8
Trinity-Large-Preview 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
Interfaze Beta
Not measured
Trinity-Large-Preview
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Interfaze Beta
Not measured
Trinity-Large-Preview
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Interfaze Beta
Not measured
Trinity-Large-Preview
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Interfaze Beta
89.9
Trinity-Large-Preview
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Math

Not comparable
Interfaze Beta
Not measured
Trinity-Large-Preview
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Interfaze Beta
Not measured
Trinity-Large-Preview
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Interfaze Beta
71.1
Trinity-Large-Preview
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Interfaze Beta
Not measured
Trinity-Large-Preview
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

Interfaze Beta
$0.00325
Fits in one request
Trinity-Large-Preview
$0.00075
Fits in one request

Trinity-Large-Preview has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Interfaze Beta
$0.0855
Fits in one request
Trinity-Large-Preview
$0.0155
Fits in one request

Trinity-Large-Preview has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Interfaze Beta
$0.365
Fits in one request
Cached input priced at the published list-input rate
Trinity-Large-Preview
$0.065
Fits in one request
Cached input priced at the published list-input rate

Trinity-Large-Preview has the lower modeled cost

Interfaze Beta has no published cached-input rate, so cached tokens use its listed input rate. Trinity-Large-Preview 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.

Interfaze Beta

1M

Trinity-Large-Preview

512K

API model ID

Interfaze Beta

Not sourced

Trinity-Large-Preview

Not sourced

Cached-input rate

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

Interfaze Beta

Not published

Trinity-Large-Preview

Not published

Documented inputs

Interfaze Beta

Not sourced

Trinity-Large-Preview

Not sourced

Documented outputs

Interfaze Beta

Not sourced

Trinity-Large-Preview

Not sourced

Provider availability

Interfaze Beta

Not sourced

Trinity-Large-Preview

Not sourced

Reasoning profile

Interfaze Beta

Reasoning

Trinity-Large-Preview

Non-Reasoning

Weight access

Interfaze Beta

Proprietary

Trinity-Large-Preview

Open Weight

License

Interfaze Beta

Proprietary

Trinity-Large-Preview

Open Weight

Release date

Interfaze Beta

2026-05-11

Trinity-Large-Preview

2026-01-27

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
Repository review: $0.0855 vs $0.0155. Cache-heavy agent loop: $0.365 vs $0.065.
Context tradeoff
Interfaze Beta has the larger documented window (1M).

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 evidence12 rows

Coding

  • Spider 2.0-Lite

    Interfaze Beta52.9%
    Source
    Trinity-Large-Preview—

    Not directly comparable

Knowledge

  • GPQA

    Interfaze Beta89.9%
    Source
    Trinity-Large-Preview—

    Not directly comparable

  • GPQA-D

    Interfaze Beta89.9%
    Source
    Trinity-Large-Preview63.3%
    Source

    Interfaze Beta leads this result

  • MMMLU

    Interfaze Beta90.9%
    Source
    Trinity-Large-Preview—

    Not directly comparable

  • MMLU

    Interfaze Beta—
    Trinity-Large-Preview87.2%
    Source

    Not directly comparable

  • MMLU-Pro (Arcee)

    Interfaze Beta—
    Trinity-Large-Preview75.2%
    Source

    Not directly comparable

Math

  • AIME25 (Arcee)

    Interfaze Beta—
    Trinity-Large-Preview24.0%
    Source

    Not directly comparable

Multimodal

  • OCRBench V2

    Interfaze Beta70.7%
    Source
    Trinity-Large-Preview—

    Not directly comparable

  • olmOCR

    Interfaze Beta85.7%
    Source
    Trinity-Large-Preview—

    Not directly comparable

  • RefCOCO (avg)

    Interfaze Beta82.1%
    Source
    Trinity-Large-Preview—

    Not directly comparable

  • MMMU-Pro

    Interfaze Beta71.1%
    Source
    Trinity-Large-Preview—

    Not directly comparable

Instruction following

  • SOB Value Acc

    Interfaze Beta79.5%
    Source
    Trinity-Large-Preview—

    Not directly comparable

Frequently asked questions

Which is better, Interfaze Beta or Trinity-Large-Preview?

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, Interfaze Beta or Trinity-Large-Preview?

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, Interfaze Beta or Trinity-Large-Preview?

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, Interfaze Beta or Trinity-Large-Preview?

For the stated presets, chat costs $0.00325 on Interfaze Beta and $0.00075 on Trinity-Large-Preview; repository review costs $0.0855 and $0.0155; the cache-heavy agent loop costs $0.365 and $0.065. Interfaze Beta has no published cached-input rate, so cached tokens use its listed input rate. Trinity-Large-Preview has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Interfaze Beta or Trinity-Large-Preview?

Interfaze Beta has the larger documented context window: 1M, compared with 512K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 10, 2026

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