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BenchLM
Google logo
Model A
Gemini 3.5 Flash-Lite

Google

60.5/100

Supported · Public rank #72

90% interval 49.0–72.0

Gemini 3.5 Flash-Lite vs GPT-5.4 Pro

Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

OpenAI logo
Model B
GPT-5.4 Pro

OpenAI

61.76/100

Estimated · Public rank #63

90% interval 50.2–73.3

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.

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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

    GPT-5.4 Pro

    GPT-5.4 Pro has the larger documented context window.

    Confidence: documented

  • 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-5.4 Pro 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-5.4 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

    Gemini 3.5 Flash-Lite and GPT-5.4 Pro are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    Confidence: limited

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

Shared results
0
Gemini 3.5 Flash-Lite only
10
GPT-5.4 Pro only
11
Like-for-like categories
0 / 8

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

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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.

Agentic

Directional only
Gemini 3.5 Flash-Lite
43.4
Estimated · #104/151
GPT-5.4 Pro
58.5
Estimated · #32/151
Basis
BenchAlign lane · 3 vs 1 public rows
Reading
Directional only

Knowledge

Directional only
Gemini 3.5 Flash-Lite
53.0
Supported · #71/181
GPT-5.4 Pro
61.2
Estimated · #36/181
Basis
BenchAlign lane · 2 vs 4 public rows
Reading
Directional only

Coding

Not comparable
Gemini 3.5 Flash-Lite
43.6
Supported · #121/183
GPT-5.4 Pro
Not ranked
Basis
BenchAlign lane · 4 vs 0 public rows
Reading
Not comparable

Reasoning

Not comparable
Gemini 3.5 Flash-Lite
60.8
Unranked · 3 rankable rows
GPT-5.4 Pro
70.1
Unranked · 2 rankable rows
Basis
Provisional lane · 1 vs 1 weighted rows
Reading
Not comparable

Math

Not comparable
Gemini 3.5 Flash-Lite
Not ranked
GPT-5.4 Pro
69.0
Unranked · 4 rankable rows
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 3.5 Flash-Lite
Not ranked
GPT-5.4 Pro
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 3.5 Flash-Lite
76.1
#16/48
GPT-5.4 Pro
87.9
Unranked · 1 rankable row
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 3.5 Flash-Lite
Not ranked
GPT-5.4 Pro
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) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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.

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-5.4 Pro
$0.12
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-5.4 Pro
$2.04
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-5.4 Pro
$8.40
Fits in one request
Cached input priced at the published list-input rate

Gemini 3.5 Flash-Lite has the lower modeled cost

GPT-5.4 Pro has no published cached-input rate, so cached tokens use its listed input rate.

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-5.4 Pro

Not published

OpenAI pricing

Reasoning profile

Gemini 3.5 Flash-Lite

Reasoning

GPT-5.4 Pro

Reasoning

Weight access

Gemini 3.5 Flash-Lite

Proprietary

GPT-5.4 Pro

Proprietary

License

Gemini 3.5 Flash-Lite

Proprietary

GPT-5.4 Pro

Proprietary

Release date

Gemini 3.5 Flash-Lite

2026-07-21

GPT-5.4 Pro

2026-03-05

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
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 $2.04. Cache-heavy agent loop: $0.037 vs $8.40.
Context tradeoff
GPT-5.4 Pro has the larger documented window (1.05M).

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

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

Browse raw public benchmark evidence21 rows

Agentic

  • Terminal-Bench 2.0

    Gemini 3.5 Flash-Lite54%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • OSWorld-Verified

    Gemini 3.5 Flash-Lite74%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Gemini 3.5 Flash-Lite50.2%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • BrowseComp

    Gemini 3.5 Flash-Lite—
    GPT-5.4 Pro89.3%
    Source

    Not directly comparable

Coding

  • Terminal-Bench 2.0

    Gemini 3.5 Flash-Lite54.0%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • SWE-bench Pro

    Gemini 3.5 Flash-Lite54.2%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • LiveCodeBench (Vals)

    Gemini 3.5 Flash-Lite79.0%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • SWE-bench (Vals)

    Gemini 3.5 Flash-Lite75.0%
    Source
    GPT-5.4 Pro—

    Not directly comparable

Reasoning

  • MRCRv2

    Gemini 3.5 Flash-Lite72.2%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • ARC-AGI-2

    Gemini 3.5 Flash-Lite—
    GPT-5.4 Pro83.3%
    Source

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    Gemini 3.5 Flash-Lite83.8%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • MMLU-Pro (Vals)

    Gemini 3.5 Flash-Lite85.8%
    Source
    GPT-5.4 Pro—

    Not directly comparable

  • HLE

    Gemini 3.5 Flash-Lite—
    GPT-5.4 Pro58.7%
    Source

    Not directly comparable

  • FrontierScience

    Gemini 3.5 Flash-Lite—
    GPT-5.4 Pro36.7%
    Source

    Not directly comparable

  • FrontierScience Research

    Gemini 3.5 Flash-Lite—
    GPT-5.4 Pro36.7%
    Source

    Not directly comparable

  • HLE w/o tools

    Gemini 3.5 Flash-Lite—
    GPT-5.4 Pro42.7%
    Source

    Not directly comparable

Math

  • IPhO 2025 (Theory)

    Gemini 3.5 Flash-Lite—
    GPT-5.4 Pro93.5%
    Source

    Not directly comparable

  • FrontierMath (legacy)

    Gemini 3.5 Flash-Lite—
    GPT-5.4 Pro50%
    Source

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Gemini 3.5 Flash-Lite—
    GPT-5.4 Pro50.000%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Gemini 3.5 Flash-Lite—
    GPT-5.4 Pro37.500%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    Gemini 3.5 Flash-Lite—
    GPT-5.4 Pro94%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Gemini 3.5 Flash-Lite or GPT-5.4 Pro?

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 GPT-5.4 Pro?

GPT-5.4 Pro 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-5.4 Pro?

GPT-5.4 Pro scores higher for agentic tasks on the public lane, 58.5 to 43.4. Gemini 3.5 Flash-Lite and GPT-5.4 Pro are 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 GPT-5.4 Pro?

For the stated presets, chat costs $0.00155 on Gemini 3.5 Flash-Lite and $0.12 on GPT-5.4 Pro; repository review costs $0.0225 and $2.04; the cache-heavy agent loop costs $0.037 and $8.40. GPT-5.4 Pro 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-5.4 Pro?

GPT-5.4 Pro has the larger documented context window: 1.05M, compared with 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 4, 2026

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