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BenchLM
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Gemini 3.5 Flash vs Gemma 4 E2B

Updated October 2, 2026. Rank says Gemini 3.5 Flash is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Google logo
Gemini 3.5 Flash

Google

63.95/100

Supported · Public rank #41

90% interval 55.9–72.0

Model B
Google logo
Gemma 4 E2B

Google

30.32/100

Estimated · Public rank #165

Conditional range 16.0–44.7

Shared results
0
Gemini 3.5 Flash only
27
Gemma 4 E2B only
2
Like-for-like categories
0 / 8
Supported: Gemini 3.5 Flash · 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

    Gemini 3.5 Flash

    Gemini 3.5 Flash 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 E2B 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 E2B 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 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.

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

52.3Gemini 3.5 Flash13.5Gemma 4 E2B

Directional only · BenchAlign v5.8

Gemini 3.5 Flash 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 leaderboardShortlist models for this job
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.

3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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
Gemini 3.5 Flash
52.3
Supported · #39/144
Gemma 4 E2B
13.5
Estimated · #135/144
Basis
BenchAlign v5.8 lane · 7 vs 0 public rows
Reading
Directional only

Knowledge

Directional only
Gemini 3.5 Flash
64.0
Supported · #30/171
Gemma 4 E2B
24.5
Estimated · #158/171
Basis
BenchAlign v5.8 lane · 4 vs 2 public rows
Reading
Directional only

Instruction following

Directional only
Gemini 3.5 Flash
84.1
#43/125
Gemma 4 E2B
42.4
#97/125
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Directional only

Agentic

Not comparable
Gemini 3.5 Flash
50.7
Supported · #42/119
Gemma 4 E2B
Not ranked
Basis
BenchAlign v5.8 lane · 8 vs 0 public rows
Reading
Not comparable

Reasoning

Not comparable
Gemini 3.5 Flash
62.8
#20/27
Gemma 4 E2B
33.3
Unranked · 2 rankable rows
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 3.5 Flash
87.8
#6/49
Gemma 4 E2B
26.9
Unranked · 1 rankable row
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 3.5 Flash
Not ranked
Gemma 4 E2B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Gemini 3.5 Flash
55.1
Unranked · 2 rankable rows
Gemma 4 E2B
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
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

Gemini 3.5 Flash
$0.006
Fits in one request
Gemma 4 E2B
Self-hosted; infrastructure cost varies
Fits in one request

Gemma 4 E2B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Gemini 3.5 Flash
$0.102
Fits in one request
Gemma 4 E2B
Self-hosted; infrastructure cost varies
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

Gemini 3.5 Flash
$0.15
Fits in one request
Gemma 4 E2B
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable

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.

Cached-input rate

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

Gemini 3.5 Flash

$0.15 per 1M cached input tokens

Google Gemini API pricing

Gemma 4 E2B

No comparable hosted API rate

Reasoning profile

Gemini 3.5 Flash

Reasoning

Gemma 4 E2B

Reasoning

Weight access

Gemini 3.5 Flash

Proprietary

Gemma 4 E2B

Open Weight

License

Gemini 3.5 Flash

Proprietary

Gemma 4 E2B

Open Weight

Release date

Gemini 3.5 Flash

2026-05-19

Gemma 4 E2B

2026-04-02

If you already use one of these models

Deployment change
Both entries list Google as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
Quality signal
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Gemini 3.5 Flash has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Gemini 3.5 Flash or Gemma 4 E2B?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Gemini 3.5 Flash or Gemma 4 E2B?

Gemini 3.5 Flash scores higher for coding on the public lane, 52.3 to 13.5. Gemma 4 E2B 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, Gemini 3.5 Flash or Gemma 4 E2B?

Gemma 4 E2B is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Gemini 3.5 Flash or Gemma 4 E2B?

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, Gemini 3.5 Flash or Gemma 4 E2B?

Gemini 3.5 Flash has the larger documented context window: 1M, 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 evidence29 rows

Agentic

  • Terminal-Bench 2.1

    Gemini 3.5 Flash76.2%
    Source
    Gemma 4 E2B—

    Not directly comparable

  • MCP Atlas

    Gemini 3.5 Flash83.6%
    Source
    Gemma 4 E2B—

    Not directly comparable

  • Toolathlon

    Gemini 3.5 Flash56.5%
    Source
    Gemma 4 E2B—

    Not directly comparable

  • OSWorld-Verified

    Gemini 3.5 Flash78.4%
    Source
    Gemma 4 E2B—

    Not directly comparable

  • Finance Agent v2

    Gemini 3.5 Flash57.9%
    Source
    Gemma 4 E2B—

    Not directly comparable

  • Gert Labs

    Gemini 3.5 Flash61.85%
    Source
    Gemma 4 E2B—

    Not directly comparable

  • ResearchClawBench

    Gemini 3.5 Flash18.0%
    Source
    Gemma 4 E2B—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Gemini 3.5 Flash74.2%
    Source
    Gemma 4 E2B—

    Not directly comparable

Coding

  • Terminal-Bench 2.1

    Gemini 3.5 Flash76.2%
    Source
    Gemma 4 E2B—

    Not directly comparable

  • SWE-bench Pro

    Gemini 3.5 Flash55.1%
    Source
    Gemma 4 E2B—

    Not directly comparable

  • Vibe Code Bench

    Gemini 3.5 Flash48.68%
    Source
    Gemma 4 E2B—

    Not directly comparable

  • cursorBench31

    Gemini 3.5 Flash49.8%
    Source
    Gemma 4 E2B—

    Not directly comparable

  • CursorBench 3.2

    Gemini 3.5 Flash48.8%
    Source
    Gemma 4 E2B—

    Not directly comparable

  • LiveCodeBench (Vals)

    Gemini 3.5 Flash87.6%
    Source
    Gemma 4 E2B—

    Not directly comparable

  • SWE-bench (Vals)

    Gemini 3.5 Flash78.8%
    Source
    Gemma 4 E2B—

    Not directly comparable

Reasoning

  • MRCRv2

    Gemini 3.5 Flash77.3%
    Source
    Gemma 4 E2B—

    Not directly comparable

  • MRCR 1M

    Gemini 3.5 Flash26.6%
    Source
    Gemma 4 E2B—

    Not directly comparable

  • ARC-AGI-2

    Gemini 3.5 Flash72.1%
    Source
    Gemma 4 E2B—

    Not directly comparable

Multimodal

  • CharXiv

    Gemini 3.5 Flash84.2%
    Source
    Gemma 4 E2B—

    Not directly comparable

  • MMMU-Pro

    Gemini 3.5 Flash83.6%
    Source
    Gemma 4 E2B—

    Not directly comparable

  • Blueprint-Bench 2

    Gemini 3.5 Flash33.6%
    Source
    Gemma 4 E2B—

    Not directly comparable

Knowledge

  • GPQA-D

    Gemini 3.5 Flash92.7%
    Source
    Gemma 4 E2B—

    Not directly comparable

  • HLE

    Gemini 3.5 Flash40.2%
    Source
    Gemma 4 E2B—

    Not directly comparable

  • GPQA Diamond (Vals)

    Gemini 3.5 Flash92.7%
    Source
    Gemma 4 E2B—

    Not directly comparable

  • MMLU-Pro (Vals)

    Gemini 3.5 Flash89.5%
    Source
    Gemma 4 E2B—

    Not directly comparable

  • GPQA

    Gemini 3.5 Flash—
    Gemma 4 E2B43.4%
    Source

    Not directly comparable

  • MMLU-Pro

    Gemini 3.5 Flash—
    Gemma 4 E2B60%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Gemini 3.5 Flash38.966%
    Source
    Gemma 4 E2B—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Gemini 3.5 Flash14.583%
    Source
    Gemma 4 E2B—

    Not directly comparable

29 public results · 0 shared

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Last updated October 2, 2026