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Gemini 3.5 Flash-Lite vs GPT-4.1

Updated October 19, 2026. Rank says Gemini 3.5 Flash-Lite is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

Gemini 3.5 Flash-Lite has the higher public score estimate, 49.61 versus 37.57, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 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
Google logo

Google

49.61/100

Supported · Public rank #97

90% interval 37.0–62.3

Model B
OpenAI logo

OpenAI

37.57/100

Supported · Public rank #147

90% interval 21.7–53.5

Shared results
1
Gemini 3.5 Flash-Lite only
11
GPT-4.1 only
6
Like-for-like categories
1 / 8
Supported: Gemini 3.5 Flash-Lite and GPT-4.1How 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.

  • 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. GPT-4.1 has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback
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
  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    GPT-4.1 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

    GPT-4.1 is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited
  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

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.

35.8Gemini 3.5 Flash-Lite—GPT-4.1

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.

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.

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.

  • FrontierMath v2 (Tiers 1-3)Math

    Normalized gap 20.0
    Gemini 3.5 Flash-Lite:25.965%
    GPT-4.1:5.965%
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.

Knowledge

Like-for-like
Gemini 3.5 Flash-Lite
49.1
Supported · #86/186
GPT-4.1
35.6
Supported · #134/186
Basis
BenchAlign v5.8 lane · 2 vs 2 public rows
Reading
Gemini 3.5 Flash-Lite leads · intervals overlap

Agentic

Not comparable
Gemini 3.5 Flash-Lite
35.6
Supported · #81/130
GPT-4.1
Not ranked
Basis
BenchAlign v5.8 lane · 3 vs 1 public rows
Reading
Not comparable

Coding

Not comparable
Gemini 3.5 Flash-Lite
35.8
Supported · #77/151
GPT-4.1
Not ranked
Basis
BenchAlign v5.8 lane · 4 vs 1 public rows
Reading
Not comparable

Reasoning

Not comparable
Gemini 3.5 Flash-Lite
70.9
Unranked · 3 rankable rows
GPT-4.1
71.9
Unranked · 2 rankable rows
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 3.5 Flash-Lite
80.9
Unranked · 1 rankable row
GPT-4.1
54.7
Unranked · 1 rankable row
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 3.5 Flash-Lite
Not ranked
GPT-4.1
61.5
#14/17
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 3.5 Flash-Lite
Not ranked
GPT-4.1
50.3
#92/148
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Gemini 3.5 Flash-Lite
37.8
Unranked · 2 rankable rows
GPT-4.1
27.0
Unranked · 1 rankable row
Basis
Provisional lane · 2 vs 1 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-Lite
$0.00155
Fits in one request
GPT-4.1
$0.006
Fits in one request

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
GPT-4.1
$0.124
Fits in one request

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
GPT-4.1
$0.52
Fits in one request
Cached input priced at the published list-input rate

Gemini 3.5 Flash-Lite has the lower modeled cost

GPT-4.1 has no published cached-input rate, so cached tokens use its listed input 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-Lite

$0.03 per 1M cached input tokens

Google Gemini API pricing

GPT-4.1

Not published

Reasoning profile

Gemini 3.5 Flash-Lite

Reasoning

GPT-4.1

Non-Reasoning

Weight access

Gemini 3.5 Flash-Lite

Proprietary

GPT-4.1

Proprietary

License

Gemini 3.5 Flash-Lite

Proprietary

GPT-4.1

Proprietary

Release date

Gemini 3.5 Flash-Lite

2026-07-21

GPT-4.1

2025-04-14

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
Gemini 3.5 Flash-Lite has the higher public score estimate, 49.61 versus 37.57, but the 90% score intervals overlap.
Workload cost
Repository review: $0.0225 vs $0.124. Cache-heavy agent loop: $0.037 vs $0.52.
Context tradeoff
Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Gemini 3.5 Flash-Lite or GPT-4.1?

Gemini 3.5 Flash-Lite has the higher public score estimate, 49.61 versus 37.57, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Gemini 3.5 Flash-Lite or GPT-4.1?

GPT-4.1 is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Gemini 3.5 Flash-Lite or GPT-4.1?

GPT-4.1 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-Lite or GPT-4.1?

For the stated presets, chat costs $0.00155 on Gemini 3.5 Flash-Lite and $0.006 on GPT-4.1; repository review costs $0.0225 and $0.124; the cache-heavy agent loop costs $0.037 and $0.52. GPT-4.1 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Gemini 3.5 Flash-Lite or GPT-4.1?

Both models list the same context window, 1M.

Benchmark evidence

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

Browse raw public benchmark evidence18 rows

Agentic

  • Terminal-Bench 2.1

    Gemini 3.5 Flash-Lite54.0%
    Source
    GPT-4.1—

    Not directly comparable

  • OSWorld-Verified

    Gemini 3.5 Flash-Lite74%
    Source
    GPT-4.1—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Gemini 3.5 Flash-Lite50.2%
    Source
    GPT-4.1—

    Not directly comparable

  • Gert Labs

    Gemini 3.5 Flash-Lite—
    GPT-4.125.65%
    Source

    Not directly comparable

Coding

  • Terminal-Bench 2.1

    Gemini 3.5 Flash-Lite54.0%
    Source
    GPT-4.1—

    Not directly comparable

  • SWE-bench Pro

    Gemini 3.5 Flash-Lite54.2%
    Source
    GPT-4.1—

    Not directly comparable

  • LiveCodeBench (Vals)

    Gemini 3.5 Flash-Lite79.0%
    Source
    GPT-4.1—

    Not directly comparable

  • SWE-bench (Vals)

    Gemini 3.5 Flash-Lite75.0%
    Source
    GPT-4.1—

    Not directly comparable

  • SWE-bench Verified

    Gemini 3.5 Flash-Lite—
    GPT-4.154.6%
    Source

    Not directly comparable

Reasoning

  • MRCRv2

    Gemini 3.5 Flash-Lite72.2%
    Source
    GPT-4.1—

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    Gemini 3.5 Flash-Lite83.8%
    Source
    GPT-4.1—

    Not directly comparable

  • MMLU-Pro (Vals)

    Gemini 3.5 Flash-Lite85.8%
    Source
    GPT-4.1—

    Not directly comparable

  • MMLU

    Gemini 3.5 Flash-Lite—
    GPT-4.190.2%
    Source

    Not directly comparable

  • GPQA

    Gemini 3.5 Flash-Lite—
    GPT-4.166.3%
    Source

    Not directly comparable

Multilingual

  • MMLU-ProX

    Gemini 3.5 Flash-Lite—
    GPT-4.172.7%
    Source

    Not directly comparable

Instruction following

  • IFEval

    Gemini 3.5 Flash-Lite—
    GPT-4.187.4%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    Gemini 3.5 Flash-Lite25.965%
    GPT-4.15.965%

    Gemini 3.5 Flash-Lite leads this result

  • FrontierMath v2 (Tier 4)

    Gemini 3.5 Flash-Lite0.000%
    Source
    GPT-4.1—

    Not directly comparable

18 public results · 1 shared

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Last updated October 19, 2026