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BenchLM

Gemini 3.5 Flash-Lite vs Gemini 4 Argon

Updated September 30, 2026. Rank says Gemini 4 Argon 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

Google

50.74/100

Supported · Public rank #78

90% interval 38.6–62.9

Model B
Google logo

Google

64.59/100

Estimated · Public rank #32

90% interval 53.1–76.1

Shared results
0
Gemini 3.5 Flash-Lite only
10
Gemini 4 Argon only
17
Like-for-like categories
2 / 8
Supported: Gemini 3.5 Flash-Lite · Estimated: Gemini 4 ArgonHow 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.

  • Coding work

    Code generation, repair, and software-engineering tasks

    Gemini 4 Argon

    Gemini 4 Argon leads on the public coding lane, 68.4 to 38.6, with Supported evidence for both models and non-overlapping 90% intervals.

    Confidence: stronger
  • Chat turn cost

    1K fresh input + 500 output tokens

    Gemini 3.5 Flash-Lite

    Gemini 3.5 Flash-Lite has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates
  • Cache-heavy agent loop cost

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

    Gemini 3.5 Flash-Lite

    Gemini 3.5 Flash-Lite has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates
Show secondary and unsupported calls
  • Repository review cost

    50K fresh input + 3K output tokens

    Gemini 3.5 Flash-Lite

    Gemini 3.5 Flash-Lite has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates
  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    Gemini 4 Argon is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    Confidence: limited
  • Long documents

    Prompts that approach the documented context limit

    Not enough matched evidence

    A complete context comparison is not sourced.

    Confidence: limited

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.

38.6Gemini 3.5 Flash-Lite68.4Gemini 4 Argon

Like-for-like · BenchAlign v5.7

Gemini 4 Argon leads the like-for-like coding row.

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

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.

1 category rests 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 v5.7 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

Like-for-like
Gemini 3.5 Flash-Lite
38.6
Supported · #69/144
Gemini 4 Argon
68.4
Supported · #8/144
Basis
BenchAlign v5.7 lane · 4 vs 4 public rows
Reading
Gemini 4 Argon leads

Knowledge

Like-for-like
Gemini 3.5 Flash-Lite
48.6
Supported · #67/170
Gemini 4 Argon
72.9
Supported · #11/170
Basis
BenchAlign v5.7 lane · 2 vs 1 public rows
Reading
Gemini 4 Argon leads

Agentic

Directional only
Gemini 3.5 Flash-Lite
34.5
Supported · #72/119
Gemini 4 Argon
63.7
Estimated · #14/119
Basis
BenchAlign v5.7 lane · 3 vs 7 public rows
Reading
Directional only

Reasoning

Not comparable
Gemini 3.5 Flash-Lite
61.3
Unranked · 3 rankable rows
Gemini 4 Argon
77.1
Unranked · 3 rankable rows
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 3.5 Flash-Lite
77.5
Unranked · 1 rankable row
Gemini 4 Argon
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 3.5 Flash-Lite
Not ranked
Gemini 4 Argon
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 3.5 Flash-Lite
Not ranked
Gemini 4 Argon
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Gemini 3.5 Flash-Lite
Not ranked
Gemini 4 Argon
Not ranked
Basis
Provisional lane · 0 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 v5.7) 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-Lite
$0.00155
Fits in one request
Gemini 4 Argon
$0.007
Fit state unavailable

Gemini 3.5 Flash-Lite has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Gemini 3.5 Flash-Lite
$0.0225
Fits in one request
Gemini 4 Argon
$0.13
Fit state unavailable

Gemini 3.5 Flash-Lite has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Gemini 3.5 Flash-Lite
$0.037
Fits in one request
Gemini 4 Argon
$0.16
Fit state unavailable

Gemini 3.5 Flash-Lite has the lower modeled cost

Costs use the listed standard API rates.

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.

Reasoning profile

Gemini 3.5 Flash-Lite

Reasoning

Gemini 4 Argon

Reasoning

Weight access

Gemini 3.5 Flash-Lite

Proprietary

Gemini 4 Argon

Proprietary

License

Gemini 3.5 Flash-Lite

Proprietary

Gemini 4 Argon

Proprietary

Release date

Gemini 3.5 Flash-Lite

2026-07-21

Gemini 4 Argon

2026-09-30

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
Repository review: $0.0225 vs $0.13. Cache-heavy agent loop: $0.037 vs $0.16.
Context tradeoff
A complete documented context comparison is not available.
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Gemini 3.5 Flash-Lite or Gemini 4 Argon?

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-Lite or Gemini 4 Argon?

Gemini 4 Argon leads the public coding lane, 68.4 to 38.6, with Supported evidence for both models and non-overlapping 90% intervals.

Which is better for agentic tasks, Gemini 3.5 Flash-Lite or Gemini 4 Argon?

Gemini 4 Argon scores higher for agentic tasks on the public lane, 63.7 to 34.5. Gemini 4 Argon is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Gemini 3.5 Flash-Lite or Gemini 4 Argon?

For the stated presets, chat costs $0.00155 on Gemini 3.5 Flash-Lite and $0.007 on Gemini 4 Argon; repository review costs $0.0225 and $0.13; the cache-heavy agent loop costs $0.037 and $0.16. Costs use the listed standard API rates.

Which has the larger context window, Gemini 3.5 Flash-Lite or Gemini 4 Argon?

A complete documented context-window comparison is not available.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence27 rows

Agentic

  • Terminal-Bench 2.1

    Gemini 3.5 Flash-Lite54.0%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • OSWorld-Verified

    Gemini 3.5 Flash-Lite74%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Gemini 3.5 Flash-Lite50.2%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • AutomationBench

    Gemini 3.5 Flash-Lite—
    Gemini 4 Argon51.3%
    Source

    Not directly comparable

  • Finance Agent v2

    Gemini 3.5 Flash-Lite—
    Gemini 4 Argon65.4%
    Source

    Not directly comparable

  • Terminal-Bench 4.0

    Gemini 3.5 Flash-Lite—
    Gemini 4 Argon57.40%
    Source

    Not directly comparable

  • Agents' Last Exam

    Gemini 3.5 Flash-Lite—
    Gemini 4 Argon39.5%
    Source

    Not directly comparable

  • OSWorld 2.0

    Gemini 3.5 Flash-Lite—
    Gemini 4 Argon69.2%
    Source

    Not directly comparable

  • CWE-bench v1

    Gemini 3.5 Flash-Lite—
    Gemini 4 Argon68.0%
    Source

    Not directly comparable

  • Terminal-Bench-Science 0.1 (6x verifier timeout)

    Gemini 3.5 Flash-Lite—
    Gemini 4 Argon57.6%
    Source

    Not directly comparable

Coding

  • Terminal-Bench 2.1

    Gemini 3.5 Flash-Lite54.0%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • SWE-bench Pro

    Gemini 3.5 Flash-Lite54.2%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • LiveCodeBench (Vals)

    Gemini 3.5 Flash-Lite79.0%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • SWE-bench (Vals)

    Gemini 3.5 Flash-Lite75.0%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • DeepSWE

    Gemini 3.5 Flash-Lite—
    Gemini 4 Argon77.9%
    Source

    Not directly comparable

  • FrontierSWE v2

    Gemini 3.5 Flash-Lite—
    Gemini 4 Argon55.1%
    Source

    Not directly comparable

  • Vibe Code Bench

    Gemini 3.5 Flash-Lite—
    Gemini 4 Argon91.90%
    Source

    Not directly comparable

  • PostTrainBench v1.1

    Gemini 3.5 Flash-Lite—
    Gemini 4 Argon45.3%
    Source

    Not directly comparable

Reasoning

  • MRCRv2

    Gemini 3.5 Flash-Lite72.2%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • Graphwalks BFS 128K

    Gemini 3.5 Flash-Lite—
    Gemini 4 Argon99.7%
    Source

    Not directly comparable

  • GraphWalks BFS 256K–1M

    Gemini 3.5 Flash-Lite—
    Gemini 4 Argon84.2%
    Source

    Not directly comparable

Multimodal

  • Chartography (no tools)

    Gemini 3.5 Flash-Lite—
    Gemini 4 Argon71.6%
    Source

    Not directly comparable

  • LVBench

    Gemini 3.5 Flash-Lite—
    Gemini 4 Argon91.7%
    Source

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    Gemini 3.5 Flash-Lite83.8%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • MMLU-Pro (Vals)

    Gemini 3.5 Flash-Lite85.8%
    Source
    Gemini 4 Argon—

    Not directly comparable

  • LABBench2

    Gemini 3.5 Flash-Lite—
    Gemini 4 Argon88.8%
    Source

    Not directly comparable

Instruction following

  • Gray Swan IPI (15 attempts)

    Gemini 3.5 Flash-Lite—
    Gemini 4 Argon0.7%
    Source

    Not directly comparable

27 public results · 0 shared

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Last updated September 30, 2026