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Model comparison

Kimi K2.5 vs ZAYA1-8B

Updated August 5, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Kimi K2.5

Moonshot AI

58.8/100

Supported · Public rank #63

90% interval 51.4–66.2

ZAYA1-8B

Zyphra

31.6/100

Estimated · Public rank #201

90% interval 21.7–41.5

Kimi K2.5 has the higher public score, 58.83 versus 31.58, and the 90% score intervals do not overlap.

7 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

    Kimi K2.5

    Kimi K2.5 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

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. ZAYA1-8B does not fit this workload in one request. Kimi K2.5 has no published cached-input rate, so cached tokens use its listed input rate. ZAYA1-8B has no comparable published API token rate.

    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
7
Kimi K2.5 only
38
ZAYA1-8B only
4
Like-for-like categories
0 / 8

3 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Knowledge

Directional only
Kimi K2.5
56.9
ZAYA1-8B
73.6
Weighted basis
4 vs 2 rows
Reading
Directional only

Math

Directional only
Kimi K2.5
60.6
ZAYA1-8B
80.4
Weighted basis
4 vs 2 rows
Reading
Directional only

Instruction following

Directional only
Kimi K2.5
93.9
ZAYA1-8B
64.1
Weighted basis
1 vs 2 rows
Reading
Directional only

Agentic

Not comparable
Kimi K2.5
55.0
ZAYA1-8B
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Kimi K2.5
59.4
ZAYA1-8B
Not measured
Weighted basis
4 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Kimi K2.5
61.0
ZAYA1-8B
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Kimi K2.5
82.3
ZAYA1-8B
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Kimi K2.5
78.5
ZAYA1-8B
Not measured
Weighted basis
1 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.

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.

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 K2.5
$0.0021
Fits in one request
ZAYA1-8B
Self-hosted; infrastructure cost varies
Fits in one request

ZAYA1-8B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Kimi K2.5
$0.039
Fits in one request
ZAYA1-8B
Self-hosted; infrastructure cost varies
Fits in one request

ZAYA1-8B has no comparable published API token rate.

Cache-heavy agent loop

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

Kimi K2.5
$0.162
Fits in one request
Cached input priced at the published list-input rate
ZAYA1-8B
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable

ZAYA1-8B does not fit this workload in one request. Kimi K2.5 has no published cached-input rate, so cached tokens use its listed input rate. ZAYA1-8B 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 K2.5

256K

ZAYA1-8B

131K

API model ID

Kimi K2.5

Not sourced

ZAYA1-8B

Not sourced

Cached-input rate

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

Kimi K2.5

Not published

ZAYA1-8B

No comparable hosted API rate

Documented inputs

Kimi K2.5

Not sourced

ZAYA1-8B

Not sourced

Documented outputs

Kimi K2.5

Not sourced

ZAYA1-8B

Not sourced

Provider availability

Kimi K2.5

Not sourced

ZAYA1-8B

Not sourced

Reasoning profile

Kimi K2.5

Non-Reasoning

ZAYA1-8B

Reasoning

Weight access

Kimi K2.5

Open Weight

ZAYA1-8B

Open Weight

License

Kimi K2.5

Open Weight

ZAYA1-8B

Open Weight

Release date

Kimi K2.5

2026-02-01

ZAYA1-8B

2026-05-05

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
Kimi K2.5 has the higher public score, 58.83 versus 31.58, and the 90% score intervals do not overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Kimi K2.5 has the larger documented window (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 K2.5
API / mo$2,700
Self-host / mo$5,221
Break-even132M/day
ZAYA1-8B
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 evidence49 rows

Agentic

  • Terminal-Bench 2.0

    Kimi K2.550.8%
    Source
    ZAYA1-8B

    Not directly comparable

  • BrowseComp

    Kimi K2.560.6%
    Source
    ZAYA1-8B

    Not directly comparable

  • Claw-Eval

    Kimi K2.552.3%
    Source
    ZAYA1-8B

    Not directly comparable

  • QwenClawBench

    Kimi K2.554.3%
    Source
    ZAYA1-8B

    Not directly comparable

  • τ³-bench results

    Kimi K2.565.7%
    Source
    ZAYA1-8B

    Not directly comparable

  • DeepSearchQA

    Kimi K2.577.1%
    Source
    ZAYA1-8B

    Not directly comparable

  • DeepPlanning

    Kimi K2.514.4%
    Source
    ZAYA1-8B

    Not directly comparable

  • Toolathlon

    Kimi K2.527.8%
    Source
    ZAYA1-8B

    Not directly comparable

  • MCP Atlas

    Kimi K2.529.5%
    Source
    ZAYA1-8B

    Not directly comparable

  • MCP-Tasks

    Kimi K2.559.1%
    Source
    ZAYA1-8B

    Not directly comparable

  • WideResearch

    Kimi K2.572.7%
    Source
    ZAYA1-8B

    Not directly comparable

  • Gert Labs

    Kimi K2.545.88%
    Source
    ZAYA1-8B

    Not directly comparable

  • ResearchClawBench

    Kimi K2.514.0%
    Source
    ZAYA1-8B

    Not directly comparable

  • JobBench

    Kimi K2.58.7%
    Source
    ZAYA1-8B

    Not directly comparable

  • BFCL v4

    Kimi K2.5
    ZAYA1-8B39.2%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Kimi K2.576.8%
    Source
    ZAYA1-8B

    Not directly comparable

  • SWE-bench Verified*

    Kimi K2.570.8%
    Source
    ZAYA1-8B

    Not directly comparable

  • LiveCodeBench v6

    Kimi K2.585.0%
    Source
    ZAYA1-8B65.8%
    Source

    Kimi K2.5 leads this result

  • SWE-bench Pro

    Kimi K2.550.7%
    Source
    ZAYA1-8B

    Not directly comparable

  • SWE Multilingual

    Kimi K2.573%
    Source
    ZAYA1-8B

    Not directly comparable

  • SWE-Rebench

    Kimi K2.558.5%
    Source
    ZAYA1-8B

    Not directly comparable

  • React Native Evals

    Kimi K2.577.2%
    Source
    ZAYA1-8B

    Not directly comparable

  • SciCode

    Kimi K2.548.7%
    Source
    ZAYA1-8B

    Not directly comparable

Reasoning

  • LongBench v2

    Kimi K2.561%
    Source
    ZAYA1-8B

    Not directly comparable

Knowledge

  • GPQA

    Kimi K2.587.6%
    Source
    ZAYA1-8B71%
    Source

    Kimi K2.5 leads this result

  • GPQA-D

    Kimi K2.587.6%
    Source
    ZAYA1-8B71.0%
    Source

    Kimi K2.5 leads this result

  • SuperGPQA

    Kimi K2.569.2%
    Source
    ZAYA1-8B

    Not directly comparable

  • MMLU-Pro

    Kimi K2.587.1%
    Source
    ZAYA1-8B74.2%
    Source

    Kimi K2.5 leads this result

  • MMLU-Pro (Arcee)

    Kimi K2.587.1%
    Source
    ZAYA1-8B

    Not directly comparable

  • HLE

    Kimi K2.530.1%
    Source
    ZAYA1-8B

    Not directly comparable

Math

  • AIME 2025

    Kimi K2.596.1%
    Source
    ZAYA1-8B

    Not directly comparable

  • AIME26

    Kimi K2.595.8%
    Source
    ZAYA1-8B89.1%
    Source

    Kimi K2.5 leads this result

  • AIME25 (Arcee)

    Kimi K2.596.3%
    Source
    ZAYA1-8B

    Not directly comparable

  • HMMT Feb 2025

    Kimi K2.595.4%
    Source
    ZAYA1-8B

    Not directly comparable

  • HMMT Nov 2025

    Kimi K2.591.1%
    Source
    ZAYA1-8B

    Not directly comparable

  • HMMT Feb 2026

    Kimi K2.587.1%
    Source
    ZAYA1-8B71.6%
    Source

    Kimi K2.5 leads this result

  • MMAnswerBench

    Kimi K2.581.8%
    Source
    ZAYA1-8B

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Kimi K2.527.900%
    Source
    ZAYA1-8B

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Kimi K2.54.200%
    Source
    ZAYA1-8B

    Not directly comparable

  • IMOAnswerBench

    Kimi K2.5
    ZAYA1-8B59.3%
    Source

    Not directly comparable

  • Apex

    Kimi K2.5
    ZAYA1-8B32.2%
    Source

    Not directly comparable

Multilingual

  • MMLU-ProX

    Kimi K2.582.3%
    Source
    ZAYA1-8B

    Not directly comparable

  • NOVA-63

    Kimi K2.556.0%
    Source
    ZAYA1-8B

    Not directly comparable

Multimodal

  • MMMU-Pro

    Kimi K2.578.5%
    Source
    ZAYA1-8B

    Not directly comparable

  • Video-MME

    Kimi K2.587.4%
    Source
    ZAYA1-8B

    Not directly comparable

  • MMVU

    Kimi K2.580.4%
    Source
    ZAYA1-8B

    Not directly comparable

  • VideoMMMU

    Kimi K2.586.6%
    Source
    ZAYA1-8B

    Not directly comparable

Instruction following

  • IFEval

    Kimi K2.593.9%
    Source
    ZAYA1-8B85.6%
    Source

    Kimi K2.5 leads this result

  • IFBench

    Kimi K2.5
    ZAYA1-8B52.6%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Kimi K2.5 or ZAYA1-8B?

Kimi K2.5 has the higher public score, 58.83 versus 31.58, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, Kimi K2.5 or ZAYA1-8B?

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 K2.5 or ZAYA1-8B?

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 K2.5 or ZAYA1-8B?

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 K2.5 or ZAYA1-8B?

Kimi K2.5 has the larger documented context window: 256K, compared with 131K.

Related comparisons

Last updated August 5, 2026

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