Gemma 4 E2B vs Kimi K3.1 Pro

Updated October 23, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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. 2 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 E2B

Google

29.87/100

Estimated · Public rank #182

Conditional range 15.5–44.2

Model B

Kimi K3.1 Pro

Moonshot AI

—

Evidence status unavailable

90% interval unavailable

Shared results: 2
Gemma 4 E2B only: 4
Kimi K3.1 Pro only: 40
Like-for-like categories: 0 / 8

Estimated: Gemma 4 E2B. 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 K3.1 Pro
Kimi K3.1 Pro 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
Kimi K3.1 Pro 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
Gemma 4 E2B and Kimi K3.1 Pro are 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 E2B does not fit this workload in one request. Gemma 4 E2B 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.

11.0Gemma 4 E2B—Kimi K3.1 Pro

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.

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 50.8
Gemma 4 E2B:43.4%
Kimi K3.1 Pro:94.2%
Gemma 4 E2B source Kimi K3.1 Pro source
MMMU-ProMultimodal
Normalized gap 39.9
Gemma 4 E2B:44.2%
Kimi K3.1 Pro:84.1%
Gemma 4 E2B source Kimi K3.1 Pro 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.

Multimodal

Directional only

Gemma 4 E2B: 14.3; #55/56
Kimi K3.1 Pro: 98.0; #1/56
Basis: Provisional lane · 1 vs 3 weighted rows
Reading: Directional only

Agentic

Not comparable

Gemma 4 E2B: Not ranked
Kimi K3.1 Pro: Not ranked
Basis: BenchAlign v5.8 lane · 0 vs 16 public rows
Reading: Not comparable

Coding

Not comparable

Gemma 4 E2B: 11.0; Estimated · #143/153
Kimi K3.1 Pro: Not ranked
Basis: BenchAlign v5.8 lane · 1 vs 6 public rows
Reading: Not comparable

Reasoning

Not comparable

Gemma 4 E2B: 22.8; Unranked · 3 rankable rows
Kimi K3.1 Pro: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Knowledge

Not comparable

Gemma 4 E2B: 22.5; Estimated · #180/187
Kimi K3.1 Pro: Not ranked
Basis: BenchAlign v5.8 lane · 2 vs 5 public rows
Reading: Not comparable

Multilingual

Not comparable

Gemma 4 E2B: Not ranked
Kimi K3.1 Pro: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Gemma 4 E2B: 43.8; #113/153
Kimi K3.1 Pro: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Gemma 4 E2B: Not ranked
Kimi K3.1 Pro: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Gemma 4 E2B and Kimi K3.1 Pro
Category	Gemma 4 E2B	Kimi K3.1 Pro	Basis	Reading
Multimodal	14.3#55/56	98.0#1/56	Directional onlyProvisional lane · 1 vs 3 weighted rows	Directional only
Agentic	Not ranked	Not ranked	Not comparableBenchAlign v5.8 lane · 0 vs 16 public rows	Not comparable
Coding	11.0Estimated · #143/153	Not ranked	Not comparableBenchAlign v5.8 lane · 1 vs 6 public rows	Not comparable
Reasoning	22.8Unranked · 3 rankable rows	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Knowledge	22.5Estimated · #180/187	Not ranked	Not comparableBenchAlign v5.8 lane · 2 vs 5 public rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	43.8#113/153	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.

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 E2B: Self-hosted; infrastructure cost varies; Fits in one request
Kimi K3.1 Pro: $0.007; 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 K3.1 Pro: $0.13; 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 K3.1 Pro: $0.18; Fits in one request

Gemma 4 E2B does not fit this workload in one request. Gemma 4 E2B 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 E2BKimi K3.1 Pro

Context window

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

Gemma 4 E2B

128K

Google Gemma 4 model documentation

Kimi K3.1 Pro

1.05M

Gemma 4 E2B

Reasoning

Kimi K3.1 Pro

Reasoning

Weight access

Gemma 4 E2B

Open Weight

Kimi K3.1 Pro

Pending

License

Gemma 4 E2B

Open Weight

Kimi K3.1 Pro

Pending

Release date

Gemma 4 E2B

2026-04-02

Kimi K3.1 Pro

2026-10-23

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: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Kimi K3.1 Pro 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 E2B or Kimi K3.1 Pro?

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, Gemma 4 E2B or Kimi K3.1 Pro?

Kimi K3.1 Pro is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Gemma 4 E2B or Kimi K3.1 Pro?

Gemma 4 E2B and Kimi K3.1 Pro are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Gemma 4 E2B or Kimi K3.1 Pro?

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 K3.1 Pro?

Kimi K3.1 Pro 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 evidence46 rows

Agentic

Terminal-Bench 4.0
Gemma 4 E2B—
Kimi K3.1 Pro65.70%
Source
Not directly comparable
Terminal-Bench-Science 0.1
Gemma 4 E2B—
Kimi K3.1 Pro38.1%
Source
Not directly comparable
HLE w/ tools
Gemma 4 E2B—
Kimi K3.1 Pro70.0%
Source
Not directly comparable
BrowseComp
Gemma 4 E2B—
Kimi K3.1 Pro92.1%
Source
Not directly comparable
BioMysteryBench
Gemma 4 E2B—
Kimi K3.1 Pro80.6%
Source
Not directly comparable
Agents' Last Exam
Gemma 4 E2B—
Kimi K3.1 Pro43.2%
Source
Not directly comparable
Agents' Last Exam (CLI)
Gemma 4 E2B—
Kimi K3.1 Pro44.2%
Source
Not directly comparable
APEX-Agents
Gemma 4 E2B—
Kimi K3.1 Pro67.2%
Source
Not directly comparable
Frontier Finance
Gemma 4 E2B—
Kimi K3.1 Pro58.6%
Source
Not directly comparable
HANDBOOK.md
Gemma 4 E2B—
Kimi K3.1 Pro44.6%
Source
Not directly comparable
OSWorld 2.0
Gemma 4 E2B—
Kimi K3.1 Pro79.6%
Source
Not directly comparable
Business Operation
Gemma 4 E2B—
Kimi K3.1 Pro37.8%
Source
Not directly comparable
Financial Modeling
Gemma 4 E2B—
Kimi K3.1 Pro57.6%
Source
Not directly comparable
Financial Research
Gemma 4 E2B—
Kimi K3.1 Pro70.4%
Source
Not directly comparable
Kimi Hallu Bench Hard (tools)
Gemma 4 E2B—
Kimi K3.1 Pro8.3%
Source
Not directly comparable
ExploitGym
Gemma 4 E2B—
Kimi K3.1 Pro20.7%
Source
Not directly comparable

Coding

LiveCodeBench v6
Gemma 4 E2B44.0%
Source
Kimi K3.1 Pro—
Not directly comparable
DeepSWE
Gemma 4 E2B—
Kimi K3.1 Pro75.5%
Source
Not directly comparable
ProgramBench (binary)
Gemma 4 E2B—
Kimi K3.1 Pro31.5%
Source
Not directly comparable
Kimi Code Bench v2
Gemma 4 E2B—
Kimi K3.1 Pro77.1%
Source
Not directly comparable
Kimi Code Bench 3.0 Long-horizon
Gemma 4 E2B—
Kimi K3.1 Pro61.2%
Source
Not directly comparable
sweMarathon
Gemma 4 E2B—
Kimi K3.1 Pro51.7%
Source
Not directly comparable
PostTrainBench v1.2
Gemma 4 E2B—
Kimi K3.1 Pro36.5%
Source
Not directly comparable

Reasoning

MRCRv2
Gemma 4 E2B19.1%
Source
Kimi K3.1 Pro—
Not directly comparable

Multimodal

ExtractBench (LlamaIndex)
Gemma 4 E2B51.85%
Source
Kimi K3.1 Pro—
Not directly comparable
MMMU-Pro
Gemma 4 E2B44.2%
Source
Kimi K3.1 Pro84.1%
Source
Kimi K3.1 Pro leads this result
OfficeQA Pro
Gemma 4 E2B—
Kimi K3.1 Pro67.4%
Source
Not directly comparable
OfficeQA Pro v2
Gemma 4 E2B—
Kimi K3.1 Pro64.4%
Source
Not directly comparable
BenchCAD Vision2Code (no tools)
Gemma 4 E2B—
Kimi K3.1 Pro0.728
Source
Not directly comparable
BenchCAD Vision2Code (tools)
Gemma 4 E2B—
Kimi K3.1 Pro0.927
Source
Not directly comparable
MMMU-Pro w/ Python
Gemma 4 E2B—
Kimi K3.1 Pro90%
Source
Not directly comparable
CharXiv
Gemma 4 E2B—
Kimi K3.1 Pro93.4%
Source
Not directly comparable
CharXiv w/o tools
Gemma 4 E2B—
Kimi K3.1 Pro89%
Source
Not directly comparable
Chartography (tools)
Gemma 4 E2B—
Kimi K3.1 Pro90.2%
Source
Not directly comparable
BabyVision w/ Python
Gemma 4 E2B—
Kimi K3.1 Pro95.4%
Source
Not directly comparable
ZeroBench
Gemma 4 E2B—
Kimi K3.1 Pro39.0%
Source
Not directly comparable
ZeroBench w/ Python
Gemma 4 E2B—
Kimi K3.1 Pro63.0%
Source
Not directly comparable
GDP.pdf (Gemini judge)
Gemma 4 E2B—
Kimi K3.1 Pro34.6%
Source
Not directly comparable
GDP.pdf w/ Python (Gemini judge)
Gemma 4 E2B—
Kimi K3.1 Pro85.7%
Source
Not directly comparable
PerceptionBench
Gemma 4 E2B—
Kimi K3.1 Pro68.3%
Source
Not directly comparable

Knowledge

GPQA
Gemma 4 E2B43.4%
Source
Kimi K3.1 Pro94.2%
Source
Kimi K3.1 Pro leads this result
MMLU-Pro
Gemma 4 E2B60%
Source
Kimi K3.1 Pro—
Not directly comparable
GPQA-D
Gemma 4 E2B—
Kimi K3.1 Pro94.2%
Source
Not directly comparable
HLE
Gemma 4 E2B—
Kimi K3.1 Pro70%
Source
Not directly comparable
HLE w/o tools
Gemma 4 E2B—
Kimi K3.1 Pro51.1%
Source
Not directly comparable
Kimi Hallu Bench
Gemma 4 E2B—
Kimi K3.1 Pro5.4%
Source
Not directly comparable

46 public results · 2 shared

Related comparisons

Compare API pricing Read the methodology Open the model selector

Watch Gemma 4 E2B vs Kimi K3.1 Pro

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 5,600+ readers.

Last updated October 23, 2026

Gemma 4 E2B vs Kimi K3.1 Pro

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

Multimodal

Agentic

Coding

Reasoning

Knowledge

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 E2B or Kimi K3.1 Pro?

Which is better for coding, Gemma 4 E2B or Kimi K3.1 Pro?

Which is better for agentic tasks, Gemma 4 E2B or Kimi K3.1 Pro?

Which costs less, Gemma 4 E2B or Kimi K3.1 Pro?

Which has the larger context window, Gemma 4 E2B or Kimi K3.1 Pro?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Related comparisons

Watch Gemma 4 E2B vs Kimi K3.1 Pro