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Interfaze Beta vs Kimi K3

Updated October 2, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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

Model A
Interfaze logo

Interfaze

—

Evidence status unavailable

90% interval unavailable

Model B
Moonshot AI logo

Moonshot AI

72.14/100

Supported · Public rank #15

90% interval 68.8–75.5

Shared results
3
Interfaze Beta only
6
Kimi K3 only
45
Like-for-like categories
0 / 8
Supported: Kimi K3How the comparison works

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

    Kimi K3

    Kimi K3 has the larger documented context window.

    Confidence: documented
  • Chat turn cost

    1K fresh input + 500 output tokens

    Interfaze Beta

    Interfaze Beta 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

    Kimi K3

    Kimi K3 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.

    Confidence: rate-fallback
Show secondary and unsupported calls
  • Repository review cost

    50K fresh input + 3K output tokens

    Interfaze Beta

    Interfaze Beta 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

    Interfaze Beta is not ranked on the public lane for coding, so no winner is named for coding.

    Confidence: limited
  • Agentic work

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

    Not enough matched evidence

    Interfaze Beta is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

—Interfaze Beta61.4Kimi K3

Not comparable · BenchAlign v5.8

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Same basis rules as the category table below

What is actually comparable

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

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.

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.

Agentic

Not comparable
Interfaze Beta
Not ranked
Kimi K3
68.1
Supported · #8/119
Basis
BenchAlign v5.8 lane · 0 vs 12 public rows
Reading
Not comparable

Coding

Not comparable
Interfaze Beta
Not ranked
Kimi K3
61.4
Supported · #18/144
Basis
BenchAlign v5.8 lane · 1 vs 14 public rows
Reading
Not comparable

Reasoning

Not comparable
Interfaze Beta
Not ranked
Kimi K3
65.8
#18/27
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Interfaze Beta
28.6
Unranked · 5 rankable rows
Kimi K3
89.4
#1/49
Basis
Provisional lane · 1 vs 3 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Interfaze Beta
Not ranked
Kimi K3
67.9
Supported · #18/171
Basis
BenchAlign v5.8 lane · 3 vs 6 public rows
Reading
Not comparable

Multilingual

Not comparable
Interfaze Beta
Not ranked
Kimi K3
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Interfaze Beta
Not ranked
Kimi K3
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Interfaze Beta
Not ranked
Kimi K3
Not ranked
Basis
Provisional lane · 0 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 v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

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
Kimi K3
$0.0105
Fits in one request

Interfaze Beta 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
Kimi K3
$0.195
Fits in one request

Interfaze Beta 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
Kimi K3
$0.27
Fits in one request

Kimi K3 has the lower modeled cost

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

Cached input falls back to the list input rate only where a cached rate is unpublished

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

Kimi K3

1.05M

API model ID

Interfaze Beta

Not sourced

Kimi K3

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

Kimi K3

$0.3 per 1M cached input tokens

Documented inputs

Interfaze Beta

Not sourced

Kimi K3

Not sourced

Documented outputs

Interfaze Beta

Not sourced

Kimi K3

Not sourced

Provider availability

Interfaze Beta

Not sourced

Kimi K3

Not sourced

Reasoning profile

Interfaze Beta

Reasoning

Kimi K3

Reasoning

Weight access

Interfaze Beta

Proprietary

Kimi K3

Pending

License

Interfaze Beta

Proprietary

Kimi K3

Pending

Release date

Interfaze Beta

2026-05-11

Kimi K3

2026-07-16

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 $0.195. Cache-heavy agent loop: $0.365 vs $0.27.
Context tradeoff
Kimi K3 has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Interfaze Beta or Kimi K3?

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 Kimi K3?

Interfaze Beta is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Interfaze Beta or Kimi K3?

Interfaze Beta is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Interfaze Beta or Kimi K3?

For the stated presets, chat costs $0.00325 on Interfaze Beta and $0.0105 on Kimi K3; repository review costs $0.0855 and $0.195; the cache-heavy agent loop costs $0.365 and $0.27. Interfaze Beta has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Interfaze Beta or Kimi K3?

Kimi K3 has the larger documented context window: 1.05M, compared with 1M.

Benchmark evidence

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

Browse raw public benchmark evidence54 rows

Agentic

  • Terminal-Bench 2.1

    Interfaze Beta—
    Kimi K388.3%
    Source

    Not directly comparable

  • BrowseComp

    Interfaze Beta—
    Kimi K391.2%
    Source

    Not directly comparable

  • DeepSearchQA

    Interfaze Beta—
    Kimi K395.0%
    Source

    Not directly comparable

  • Toolathlon-Verified

    Interfaze Beta—
    Kimi K373.2%
    Source

    Not directly comparable

  • MCP Atlas

    Interfaze Beta—
    Kimi K384.2%
    Source

    Not directly comparable

  • AutomationBench

    Interfaze Beta—
    Kimi K330.8%
    Source

    Not directly comparable

  • JobBench

    Interfaze Beta—
    Kimi K352.9%
    Source

    Not directly comparable

  • APEX-Agents

    Interfaze Beta—
    Kimi K337.6%
    Source

    Not directly comparable

  • SpreadsheetBench 2

    Interfaze Beta—
    Kimi K334.8%
    Source

    Not directly comparable

  • DECK-Bench

    Interfaze Beta—
    Kimi K373.5%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Interfaze Beta—
    Kimi K380.9%
    Source

    Not directly comparable

  • ApprenticeBench

    Interfaze Beta—
    Kimi K318%
    Source

    Not directly comparable

Coding

  • Spider 2.0-Lite

    Interfaze Beta52.9%
    Source
    Kimi K3—

    Not directly comparable

  • DeepSWE

    Interfaze Beta—
    Kimi K367.5%
    Source

    Not directly comparable

  • CursorBench 3.2

    Interfaze Beta—
    Kimi K360.8%
    Source

    Not directly comparable

  • FrontierSWE

    Interfaze Beta—
    Kimi K381.2%
    Source

    Not directly comparable

  • ProgramBench

    Interfaze Beta—
    Kimi K377.8%
    Source

    Not directly comparable

  • Kimi Code Bench v2

    Interfaze Beta—
    Kimi K372.9%
    Source

    Not directly comparable

  • sweMarathon

    Interfaze Beta—
    Kimi K342%
    Source

    Not directly comparable

  • PostTrain Bench

    Interfaze Beta—
    Kimi K336.6%
    Source

    Not directly comparable

  • MLS-Bench Lite

    Interfaze Beta—
    Kimi K348.3%
    Source

    Not directly comparable

  • VulcanBench v3

    Interfaze Beta—
    Kimi K373.7%
    Source

    Not directly comparable

  • OpenHarmony Bench

    Interfaze Beta—
    Kimi K357.3%
    Source

    Not directly comparable

  • FrontierSWE v2

    Interfaze Beta—
    Kimi K325.9%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    Interfaze Beta—
    Kimi K387.2%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    Interfaze Beta—
    Kimi K393.4%
    Source

    Not directly comparable

  • PostTrainBench v1.1

    Interfaze Beta—
    Kimi K332.0%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-1

    Interfaze Beta—
    Kimi K394.50%
    Source

    Not directly comparable

  • ARC-AGI-2

    Interfaze Beta—
    Kimi K360.4%
    Source

    Not directly comparable

Multimodal

  • OCRBench V2

    Interfaze Beta70.7%
    Source
    Kimi K3—

    Not directly comparable

  • olmOCR

    Interfaze Beta85.7%
    Source
    Kimi K3—

    Not directly comparable

  • RefCOCO (avg)

    Interfaze Beta82.1%
    Source
    Kimi K3—

    Not directly comparable

  • MMMU-Pro

    Interfaze Beta71.1%
    Source
    Kimi K381.6%
    Source

    Kimi K3 leads this result

  • OfficeQA Pro

    Interfaze Beta—
    Kimi K363.3%
    Source

    Not directly comparable

  • MMMU-Pro w/ Python

    Interfaze Beta—
    Kimi K383.4%
    Source

    Not directly comparable

  • CharXiv w/o tools

    Interfaze Beta—
    Kimi K384.8%
    Source

    Not directly comparable

  • CharXiv

    Interfaze Beta—
    Kimi K391.3%
    Source

    Not directly comparable

  • MathVision

    Interfaze Beta—
    Kimi K394.3%
    Source

    Not directly comparable

  • MathVision w/ Python

    Interfaze Beta—
    Kimi K397.8%
    Source

    Not directly comparable

  • BabyVision w/ Python

    Interfaze Beta—
    Kimi K385.7%
    Source

    Not directly comparable

  • ZeroBench

    Interfaze Beta—
    Kimi K323.0%
    Source

    Not directly comparable

  • ZeroBench w/ Python

    Interfaze Beta—
    Kimi K341.0%
    Source

    Not directly comparable

  • WorldVQA ForceAnswer

    Interfaze Beta—
    Kimi K351.0%
    Source

    Not directly comparable

  • OmniDocBench

    Interfaze Beta—
    Kimi K391.1%
    Source

    Not directly comparable

  • PerceptionBench

    Interfaze Beta—
    Kimi K358.5%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Interfaze Beta89.9%
    Source
    Kimi K393.5%
    Source

    Kimi K3 leads this result

  • GPQA-D

    Interfaze Beta89.9%
    Source
    Kimi K393.5%
    Source

    Kimi K3 leads this result

  • MMMLU

    Interfaze Beta90.9%
    Source
    Kimi K3—

    Not directly comparable

  • HLE

    Interfaze Beta—
    Kimi K356%
    Source

    Not directly comparable

  • HLE w/o tools

    Interfaze Beta—
    Kimi K343.5%
    Source

    Not directly comparable

  • GPQA Diamond (Vals)

    Interfaze Beta—
    Kimi K392.9%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    Interfaze Beta—
    Kimi K388.0%
    Source

    Not directly comparable

Instruction following

  • SOB Value Acc

    Interfaze Beta79.5%
    Source
    Kimi K3—

    Not directly comparable

  • Gray Swan IPI (15 attempts)

    Interfaze Beta—
    Kimi K352.7%
    Source

    Not directly comparable

54 public results · 3 shared

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Last updated October 2, 2026