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BenchLM
Model A
Gemma 4 E2B

Google

42.2/100

Estimated · Public rank #180

90% interval 30.7–53.7

Gemma 4 E2B vs Kimi K2.5 (Reasoning)

Updated August 18, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Kimi K2.5 (Reasoning)

Moonshot AI

59.8/100

Estimated · Public rank #64

90% interval 48.3–71.3

Decision reading

Kimi K2.5 (Reasoning) has the higher public score estimate, 59.81 versus 42.18, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

2 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 (Reasoning)

    Kimi K2.5 (Reasoning) 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. Gemma 4 E2B does not fit this workload in one request. Kimi K2.5 (Reasoning) has no published cached-input rate, so cached tokens use its listed input rate. Gemma 4 E2B 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
2
Gemma 4 E2B only
0
Kimi K2.5 (Reasoning) only
7
Like-for-like categories
1 / 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.

Knowledge

Like-for-like
Gemma 4 E2B
56.9
Kimi K2.5 (Reasoning)
87.2
Weighted basis
2 vs 2 rows
Reading
Kimi K2.5 (Reasoning) leads

Agentic

Not comparable
Gemma 4 E2B
Not measured
Kimi K2.5 (Reasoning)
55.0
Weighted basis
0 vs 2 rows
Reading
Not comparable

Coding

Not comparable
Gemma 4 E2B
Not measured
Kimi K2.5 (Reasoning)
76.8
Weighted basis
0 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
Gemma 4 E2B
Not measured
Kimi K2.5 (Reasoning)
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Gemma 4 E2B
Not measured
Kimi K2.5 (Reasoning)
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Gemma 4 E2B
Not measured
Kimi K2.5 (Reasoning)
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Gemma 4 E2B
Not measured
Kimi K2.5 (Reasoning)
78.5
Weighted basis
0 vs 1 rows
Reading
Not comparable

Instruction following

Not comparable
Gemma 4 E2B
Not measured
Kimi K2.5 (Reasoning)
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.

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

Gemma 4 E2B
Self-hosted; infrastructure cost varies
Fits in one request
Kimi K2.5 (Reasoning)
$0.0021
Fits in one request

Gemma 4 E2B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Gemma 4 E2B
Self-hosted; infrastructure cost varies
Fits in one request
Kimi K2.5 (Reasoning)
$0.039
Fits in one request

Gemma 4 E2B has no comparable published API token rate.

Cache-heavy agent loop

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

Gemma 4 E2B
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable
Kimi K2.5 (Reasoning)
$0.162
Fits in one request
Cached input priced at the published list-input rate

Gemma 4 E2B does not fit this workload in one request. Kimi K2.5 (Reasoning) has no published cached-input rate, so cached tokens use its listed input rate. Gemma 4 E2B 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.

API model ID

Gemma 4 E2B

Not sourced

Kimi K2.5 (Reasoning)

Not sourced

Cached-input rate

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

Gemma 4 E2B

No comparable hosted API rate

Kimi K2.5 (Reasoning)

Not published

Reasoning profile

Gemma 4 E2B

Reasoning

Kimi K2.5 (Reasoning)

Reasoning

Weight access

Gemma 4 E2B

Open Weight

Kimi K2.5 (Reasoning)

Proprietary

License

Gemma 4 E2B

Open Weight

Kimi K2.5 (Reasoning)

Proprietary

Release date

Gemma 4 E2B

2026-04-02

Kimi K2.5 (Reasoning)

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
Kimi K2.5 (Reasoning) has the higher public score estimate, 59.81 versus 42.18, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Kimi K2.5 (Reasoning) has the larger documented window (256K).

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 evidence9 rows

Agentic

  • Terminal-Bench 2.0

    Gemma 4 E2B—
    Kimi K2.5 (Reasoning)50.8%
    Source

    Not directly comparable

  • BrowseComp

    Gemma 4 E2B—
    Kimi K2.5 (Reasoning)60.6%
    Source

    Not directly comparable

  • Gert Labs

    Gemma 4 E2B—
    Kimi K2.5 (Reasoning)32.58%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Gemma 4 E2B—
    Kimi K2.5 (Reasoning)76.8%
    Source

    Not directly comparable

  • Vibe Code Bench

    Gemma 4 E2B—
    Kimi K2.5 (Reasoning)17.54%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Gemma 4 E2B43.4%
    Source
    Kimi K2.5 (Reasoning)87.6%
    Source

    Kimi K2.5 (Reasoning) leads this result

  • MMLU-Pro

    Gemma 4 E2B60%
    Source
    Kimi K2.5 (Reasoning)87.1%
    Source

    Kimi K2.5 (Reasoning) leads this result

Math

  • AIME 2025

    Gemma 4 E2B—
    Kimi K2.5 (Reasoning)96.1%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    Gemma 4 E2B—
    Kimi K2.5 (Reasoning)78.5%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Gemma 4 E2B or Kimi K2.5 (Reasoning)?

Kimi K2.5 (Reasoning) has the higher public score estimate, 59.81 versus 42.18, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Gemma 4 E2B or Kimi K2.5 (Reasoning)?

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, Gemma 4 E2B or Kimi K2.5 (Reasoning)?

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, Gemma 4 E2B or Kimi K2.5 (Reasoning)?

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, Gemma 4 E2B or Kimi K2.5 (Reasoning)?

Kimi K2.5 (Reasoning) has the larger documented context window: 256K, compared with 128K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 18, 2026

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