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BenchLM

Interfaze Beta vs Kimi K2.5

Updated September 24, 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

52.96/100

Supported · Public rank #63

90% interval 44.8–61.1

Shared results
3
Interfaze Beta only
6
Kimi K2.5 only
42
Like-for-like categories
0 / 8
Supported: Kimi K2.5How 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

    Interfaze Beta

    Interfaze Beta has the larger documented context window.

    Confidence: documented
  • Chat turn cost

    1K fresh input + 500 output tokens

    Kimi K2.5

    Kimi K2.5 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 K2.5

    Kimi K2.5 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. Kimi K2.5 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

    Kimi K2.5

    Kimi K2.5 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 Beta38.9Kimi K2.5

Not comparable · BenchAlign v5.7

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 v5.7 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 K2.5
36.1
Estimated · #52/105
Basis
BenchAlign v5.7 lane · 0 vs 14 public rows
Reading
Not comparable

Coding

Not comparable
Interfaze Beta
Not ranked
Kimi K2.5
38.9
Estimated · #61/135
Basis
BenchAlign v5.7 lane · 1 vs 8 public rows
Reading
Not comparable

Reasoning

Not comparable
Interfaze Beta
Not ranked
Kimi K2.5
55.1
Unranked · 3 rankable rows
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Interfaze Beta
27.6
Unranked · 5 rankable rows
Kimi K2.5
65.8
#25/50
Basis
Provisional lane · 1 vs 1 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Interfaze Beta
Not ranked
Kimi K2.5
47.8
Estimated · #65/158
Basis
BenchAlign v5.7 lane · 3 vs 6 public rows
Reading
Not comparable

Multilingual

Not comparable
Interfaze Beta
Not ranked
Kimi K2.5
38.2
#8/12
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Interfaze Beta
Not ranked
Kimi K2.5
84.4
#41/124
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Interfaze Beta
Not ranked
Kimi K2.5
62.1
#4/7
Basis
Provisional lane · 0 vs 4 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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.

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 K2.5
$0.0021
Fits in one request

Kimi K2.5 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 K2.5
$0.039
Fits in one request

Kimi K2.5 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 K2.5
$0.162
Fits in one request
Cached input priced at the published list-input rate

Kimi K2.5 has the lower modeled cost

Interfaze Beta has no published cached-input rate, so cached tokens use its listed input rate. Kimi K2.5 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 K2.5

256K

API model ID

Interfaze Beta

Not sourced

Kimi K2.5

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 K2.5

Not published

Documented inputs

Interfaze Beta

Not sourced

Kimi K2.5

Not sourced

Documented outputs

Interfaze Beta

Not sourced

Kimi K2.5

Not sourced

Provider availability

Interfaze Beta

Not sourced

Kimi K2.5

Not sourced

Reasoning profile

Interfaze Beta

Reasoning

Kimi K2.5

Non-Reasoning

Weight access

Interfaze Beta

Proprietary

Kimi K2.5

Open Weight

License

Interfaze Beta

Proprietary

Kimi K2.5

Open Weight

Release date

Interfaze Beta

2026-05-11

Kimi K2.5

2026-02-01

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
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 $0.039. Cache-heavy agent loop: $0.365 vs $0.162.
Context tradeoff
Interfaze Beta has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Interfaze Beta or Kimi K2.5?

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 K2.5?

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 K2.5?

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 K2.5?

For the stated presets, chat costs $0.00325 on Interfaze Beta and $0.0021 on Kimi K2.5; repository review costs $0.0855 and $0.039; the cache-heavy agent loop costs $0.365 and $0.162. Interfaze Beta has no published cached-input rate, so cached tokens use its listed input rate. Kimi K2.5 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Interfaze Beta or Kimi K2.5?

Interfaze Beta has the larger documented context window: 1M, compared with 256K.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Interfaze Beta
API / mo$3,750
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Kimi K2.5
API / mo$2,700
Self-host / mo$5,221
Break-even132M/day
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 evidence51 rows

Agentic

  • Terminal-Bench 2.0

    Interfaze Beta—
    Kimi K2.550.8%
    Source

    Not directly comparable

  • BrowseComp

    Interfaze Beta—
    Kimi K2.560.6%
    Source

    Not directly comparable

  • Claw-Eval

    Interfaze Beta—
    Kimi K2.552.3%
    Source

    Not directly comparable

  • QwenClawBench

    Interfaze Beta—
    Kimi K2.554.3%
    Source

    Not directly comparable

  • τ³-bench results

    Interfaze Beta—
    Kimi K2.565.7%
    Source

    Not directly comparable

  • DeepSearchQA

    Interfaze Beta—
    Kimi K2.577.1%
    Source

    Not directly comparable

  • DeepPlanning

    Interfaze Beta—
    Kimi K2.514.4%
    Source

    Not directly comparable

  • Toolathlon

    Interfaze Beta—
    Kimi K2.527.8%
    Source

    Not directly comparable

  • MCP Atlas

    Interfaze Beta—
    Kimi K2.529.5%
    Source

    Not directly comparable

  • MCP-Tasks

    Interfaze Beta—
    Kimi K2.559.1%
    Source

    Not directly comparable

  • WideResearch

    Interfaze Beta—
    Kimi K2.572.7%
    Source

    Not directly comparable

  • Gert Labs

    Interfaze Beta—
    Kimi K2.545.88%
    Source

    Not directly comparable

  • ResearchClawBench

    Interfaze Beta—
    Kimi K2.514.0%
    Source

    Not directly comparable

  • JobBench

    Interfaze Beta—
    Kimi K2.58.7%
    Source

    Not directly comparable

Coding

  • Spider 2.0-Lite

    Interfaze Beta52.9%
    Source
    Kimi K2.5—

    Not directly comparable

  • SWE-bench Verified

    Interfaze Beta—
    Kimi K2.576.8%
    Source

    Not directly comparable

  • SWE-bench Verified*

    Interfaze Beta—
    Kimi K2.570.8%
    Source

    Not directly comparable

  • LiveCodeBench v6

    Interfaze Beta—
    Kimi K2.585.0%
    Source

    Not directly comparable

  • SWE-bench Pro

    Interfaze Beta—
    Kimi K2.550.7%
    Source

    Not directly comparable

  • SWE Multilingual

    Interfaze Beta—
    Kimi K2.573%
    Source

    Not directly comparable

  • SWE-Rebench

    Interfaze Beta—
    Kimi K2.558.5%
    Source

    Not directly comparable

  • React Native Evals

    Interfaze Beta—
    Kimi K2.577.2%
    Source

    Not directly comparable

  • SciCode

    Interfaze Beta—
    Kimi K2.548.7%
    Source

    Not directly comparable

Reasoning

  • LongBench v2

    Interfaze Beta—
    Kimi K2.561%
    Source

    Not directly comparable

Multimodal

  • OCRBench V2

    Interfaze Beta70.7%
    Source
    Kimi K2.5—

    Not directly comparable

  • olmOCR

    Interfaze Beta85.7%
    Source
    Kimi K2.5—

    Not directly comparable

  • RefCOCO (avg)

    Interfaze Beta82.1%
    Source
    Kimi K2.5—

    Not directly comparable

  • MMMU-Pro

    Interfaze Beta71.1%
    Source
    Kimi K2.578.5%
    Source

    Kimi K2.5 leads this result

  • Video-MME

    Interfaze Beta—
    Kimi K2.587.4%
    Source

    Not directly comparable

  • MMVU

    Interfaze Beta—
    Kimi K2.580.4%
    Source

    Not directly comparable

  • VideoMMMU

    Interfaze Beta—
    Kimi K2.586.6%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Interfaze Beta89.9%
    Source
    Kimi K2.587.6%
    Source

    Interfaze Beta leads this result

  • GPQA-D

    Interfaze Beta89.9%
    Source
    Kimi K2.587.6%
    Source

    Interfaze Beta leads this result

  • MMMLU

    Interfaze Beta90.9%
    Source
    Kimi K2.5—

    Not directly comparable

  • SuperGPQA

    Interfaze Beta—
    Kimi K2.569.2%
    Source

    Not directly comparable

  • MMLU-Pro

    Interfaze Beta—
    Kimi K2.587.1%
    Source

    Not directly comparable

  • MMLU-Pro (Arcee)

    Interfaze Beta—
    Kimi K2.587.1%
    Source

    Not directly comparable

  • HLE

    Interfaze Beta—
    Kimi K2.530.1%
    Source

    Not directly comparable

Multilingual

  • MMLU-ProX

    Interfaze Beta—
    Kimi K2.582.3%
    Source

    Not directly comparable

  • NOVA-63

    Interfaze Beta—
    Kimi K2.556.0%
    Source

    Not directly comparable

Instruction following

  • SOB Value Acc

    Interfaze Beta79.5%
    Source
    Kimi K2.5—

    Not directly comparable

  • IFEval

    Interfaze Beta—
    Kimi K2.593.9%
    Source

    Not directly comparable

Math

  • AIME 2025

    Interfaze Beta—
    Kimi K2.596.1%
    Source

    Not directly comparable

  • AIME26

    Interfaze Beta—
    Kimi K2.595.8%
    Source

    Not directly comparable

  • AIME25 (Arcee)

    Interfaze Beta—
    Kimi K2.596.3%
    Source

    Not directly comparable

  • HMMT Feb 2025

    Interfaze Beta—
    Kimi K2.595.4%
    Source

    Not directly comparable

  • HMMT Nov 2025

    Interfaze Beta—
    Kimi K2.591.1%
    Source

    Not directly comparable

  • HMMT Feb 2026

    Interfaze Beta—
    Kimi K2.587.1%
    Source

    Not directly comparable

  • MMAnswerBench

    Interfaze Beta—
    Kimi K2.581.8%
    Source

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Interfaze Beta—
    Kimi K2.527.900%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Interfaze Beta—
    Kimi K2.54.200%
    Source

    Not directly comparable

51 public results · 3 shared

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Last updated September 24, 2026