Gemma 4 E4B vs Kimi K3

Updated October 2, 2026. Rank says Kimi K3 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Kimi K3 has the higher public point estimate, 72.14 versus 31.45. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Gemma 4 E4B

Google

31.45/100

Estimated · Public rank #158

Conditional range 17.1–45.8

Model B

Kimi K3

Moonshot AI

72.14/100

Supported · Public rank #15

90% interval 68.8–75.5

Shared results: 1
Gemma 4 E4B only: 1
Kimi K3 only: 47
Like-for-like categories: 0 / 8

Estimated: Gemma 4 E4B · Supported: Kimi K3. Conditional ranges do not establish rank confidence.How 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

Show secondary and unsupported calls

Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Gemma 4 E4B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemma 4 E4B is not ranked on the public lane for agentic, so no winner is named for agentic.
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 E4B does not fit this workload in one request. Gemma 4 E4B has no comparable published API token rate.
Confidence: listed-rates
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

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.

Pick the job you would use them for

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

15.0Gemma 4 E4B61.4Kimi K3

Directional only · BenchAlign v5.8

Kimi K3 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

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

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

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.

GPQAKnowledge
Normalized gap 34.9
Gemma 4 E4B:58.6%
Kimi K3:93.5%
Gemma 4 E4B source Kimi K3 source

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.

Coding

Directional only

Gemma 4 E4B: 15.0; Estimated · #131/144
Kimi K3: 61.4; Supported · #18/144
Basis: BenchAlign v5.8 lane · 0 vs 14 public rows
Reading: Directional only

Knowledge

Directional only

Gemma 4 E4B: 26.6; Estimated · #148/171
Kimi K3: 67.9; Supported · #18/171
Basis: BenchAlign v5.8 lane · 2 vs 6 public rows
Reading: Directional only

Agentic

Not comparable

Gemma 4 E4B: Not ranked
Kimi K3: 68.1; Supported · #8/119
Basis: BenchAlign v5.8 lane · 0 vs 12 public rows
Reading: Not comparable

Reasoning

Not comparable

Gemma 4 E4B: 44.2; Unranked · 2 rankable rows
Kimi K3: 65.8; #18/27
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Gemma 4 E4B: 36.9; Unranked · 1 rankable row
Kimi K3: 89.4; #1/49
Basis: Provisional lane · 0 vs 3 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Gemma 4 E4B: Not ranked
Kimi K3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Gemma 4 E4B: 50.5; #80/125
Kimi K3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Gemma 4 E4B: Not ranked
Kimi K3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Gemma 4 E4B and Kimi K3
Category	Gemma 4 E4B	Kimi K3	Basis	Reading
Coding	15.0Estimated · #131/144	61.4Supported · #18/144	Directional onlyBenchAlign v5.8 lane · 0 vs 14 public rows	Directional only
Knowledge	26.6Estimated · #148/171	67.9Supported · #18/171	Directional onlyBenchAlign v5.8 lane · 2 vs 6 public rows	Directional only
Agentic	Not ranked	68.1Supported · #8/119	Not comparableBenchAlign v5.8 lane · 0 vs 12 public rows	Not comparable
Reasoning	44.2Unranked · 2 rankable rows	65.8#18/27	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Multimodal	36.9Unranked · 1 rankable row	89.4#1/49	Not comparableProvisional lane · 0 vs 3 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	50.5#80/125	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	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

Gemma 4 E4B: Self-hosted; infrastructure cost varies; Fits in one request
Kimi K3: $0.0105; Fits in one request

Gemma 4 E4B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Gemma 4 E4B: Self-hosted; infrastructure cost varies; Fits in one request
Kimi K3: $0.195; Fits in one request

Gemma 4 E4B has no comparable published API token rate.

Cache-heavy agent loop

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

Gemma 4 E4B: Self-hosted; infrastructure cost varies; Does not fit in one request; Cached-input rate unavailable
Kimi K3: $0.27; Fits in one request

Gemma 4 E4B does not fit this workload in one request. Gemma 4 E4B has no comparable published API token 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.

SpecificationGemma 4 E4BKimi K3

Context window

Maximum documented context; output-token limits may be lower.

Gemma 4 E4B

128K

Google Gemma 4 model documentation

Kimi K3

1.05M

Gemma 4 E4B

Reasoning

Kimi K3

Reasoning

Weight access

Gemma 4 E4B

Open Weight

Kimi K3

Pending

License

Gemma 4 E4B

Open Weight

Kimi K3

Pending

Release date

Gemma 4 E4B

2026-04-02

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: Kimi K3 has the higher public point estimate, 72.14 versus 31.45. Their conditional score ranges do not overlap. These ranges do not establish rank confidence.
Workload cost: A complete comparable API-rate estimate is not available for both models.
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, Gemma 4 E4B or Kimi K3?

Kimi K3 has the higher public point estimate, 72.14 versus 31.45. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Gemma 4 E4B or Kimi K3?

Kimi K3 scores higher for coding on the public lane, 61.4 to 15. Gemma 4 E4B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Gemma 4 E4B or Kimi K3?

Gemma 4 E4B is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Gemma 4 E4B or Kimi K3?

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 E4B or Kimi K3?

Kimi K3 has the larger documented context window: 1.05M, compared with 128K.

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 2.1
Gemma 4 E4B—
Kimi K388.3%
Source
Not directly comparable
BrowseComp
Gemma 4 E4B—
Kimi K391.2%
Source
Not directly comparable
DeepSearchQA
Gemma 4 E4B—
Kimi K395.0%
Source
Not directly comparable
Toolathlon-Verified
Gemma 4 E4B—
Kimi K373.2%
Source
Not directly comparable
MCP Atlas
Gemma 4 E4B—
Kimi K384.2%
Source
Not directly comparable
AutomationBench
Gemma 4 E4B—
Kimi K330.8%
Source
Not directly comparable
JobBench
Gemma 4 E4B—
Kimi K352.9%
Source
Not directly comparable
APEX-Agents
Gemma 4 E4B—
Kimi K337.6%
Source
Not directly comparable
SpreadsheetBench 2
Gemma 4 E4B—
Kimi K334.8%
Source
Not directly comparable
DECK-Bench
Gemma 4 E4B—
Kimi K373.5%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemma 4 E4B—
Kimi K380.9%
Source
Not directly comparable
ApprenticeBench
Gemma 4 E4B—
Kimi K318%
Source
Not directly comparable

Coding

DeepSWE
Gemma 4 E4B—
Kimi K367.5%
Source
Not directly comparable
CursorBench 3.2
Gemma 4 E4B—
Kimi K360.8%
Source
Not directly comparable
FrontierSWE
Gemma 4 E4B—
Kimi K381.2%
Source
Not directly comparable
ProgramBench
Gemma 4 E4B—
Kimi K377.8%
Source
Not directly comparable
Kimi Code Bench v2
Gemma 4 E4B—
Kimi K372.9%
Source
Not directly comparable
sweMarathon
Gemma 4 E4B—
Kimi K342%
Source
Not directly comparable
PostTrain Bench
Gemma 4 E4B—
Kimi K336.6%
Source
Not directly comparable
MLS-Bench Lite
Gemma 4 E4B—
Kimi K348.3%
Source
Not directly comparable
VulcanBench v3
Gemma 4 E4B—
Kimi K373.7%
Source
Not directly comparable
OpenHarmony Bench
Gemma 4 E4B—
Kimi K357.3%
Source
Not directly comparable
FrontierSWE v2
Gemma 4 E4B—
Kimi K325.9%
Source
Not directly comparable
LiveCodeBench (Vals)
Gemma 4 E4B—
Kimi K387.2%
Source
Not directly comparable
SWE-bench (Vals)
Gemma 4 E4B—
Kimi K393.4%
Source
Not directly comparable
PostTrainBench v1.1
Gemma 4 E4B—
Kimi K332.0%
Source
Not directly comparable

Reasoning

ARC-AGI-1
Gemma 4 E4B—
Kimi K394.50%
Source
Not directly comparable
ARC-AGI-2
Gemma 4 E4B—
Kimi K360.4%
Source
Not directly comparable

Multimodal

OfficeQA Pro
Gemma 4 E4B—
Kimi K363.3%
Source
Not directly comparable
MMMU-Pro
Gemma 4 E4B—
Kimi K381.6%
Source
Not directly comparable
MMMU-Pro w/ Python
Gemma 4 E4B—
Kimi K383.4%
Source
Not directly comparable
CharXiv w/o tools
Gemma 4 E4B—
Kimi K384.8%
Source
Not directly comparable
CharXiv
Gemma 4 E4B—
Kimi K391.3%
Source
Not directly comparable
MathVision
Gemma 4 E4B—
Kimi K394.3%
Source
Not directly comparable
MathVision w/ Python
Gemma 4 E4B—
Kimi K397.8%
Source
Not directly comparable
BabyVision w/ Python
Gemma 4 E4B—
Kimi K385.7%
Source
Not directly comparable
ZeroBench
Gemma 4 E4B—
Kimi K323.0%
Source
Not directly comparable
ZeroBench w/ Python
Gemma 4 E4B—
Kimi K341.0%
Source
Not directly comparable
WorldVQA ForceAnswer
Gemma 4 E4B—
Kimi K351.0%
Source
Not directly comparable
OmniDocBench
Gemma 4 E4B—
Kimi K391.1%
Source
Not directly comparable
PerceptionBench
Gemma 4 E4B—
Kimi K358.5%
Source
Not directly comparable

Knowledge

GPQA
Gemma 4 E4B58.6%
Source
Kimi K393.5%
Source
Kimi K3 leads this result
MMLU-Pro
Gemma 4 E4B69.4%
Source
Kimi K3—
Not directly comparable
GPQA-D
Gemma 4 E4B—
Kimi K393.5%
Source
Not directly comparable
HLE
Gemma 4 E4B—
Kimi K356%
Source
Not directly comparable
HLE w/o tools
Gemma 4 E4B—
Kimi K343.5%
Source
Not directly comparable
GPQA Diamond (Vals)
Gemma 4 E4B—
Kimi K392.9%
Source
Not directly comparable
MMLU-Pro (Vals)
Gemma 4 E4B—
Kimi K388.0%
Source
Not directly comparable

Instruction following

Gray Swan IPI (15 attempts)
Gemma 4 E4B—
Kimi K352.7%
Source
Not directly comparable

49 public results · 1 shared

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Last updated October 2, 2026

Gemma 4 E4B vs Kimi K3

Decision reading

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Coding

Knowledge

Agentic

Reasoning

Multimodal

Multilingual

Instruction following

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, Gemma 4 E4B or Kimi K3?

Which is better for coding, Gemma 4 E4B or Kimi K3?

Which is better for agentic tasks, Gemma 4 E4B or Kimi K3?

Which costs less, Gemma 4 E4B or Kimi K3?

Which has the larger context window, Gemma 4 E4B or Kimi K3?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Instruction following

Related comparisons

Watch Gemma 4 E4B vs Kimi K3