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BenchLM
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Model A
Gemini 2.5 Flash

Google

50.97/100

Supported · Public rank #121

90% interval 34.0–67.9

Gemini 2.5 Flash vs GPT-5.4 nano

Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

OpenAI logo
Model B
GPT-5.4 nano

OpenAI

59.57/100

Supported · Public rank #68

90% interval 45.5–73.7

Decision reading

GPT-5.4 nano has the higher public score estimate, 59.57 versus 50.97, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

2 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

    Gemini 2.5 Flash

    Gemini 2.5 Flash has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    GPT-5.4 nano

    GPT-5.4 nano 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

    GPT-5.4 nano

    GPT-5.4 nano 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

    GPT-5.4 nano

    GPT-5.4 nano 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

    Gemini 2.5 Flash 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 2.5 Flash is not ranked on the public lane for agentic, so no winner is named for agentic.

    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
2
Gemini 2.5 Flash only
0
GPT-5.4 nano only
16
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.

Knowledge

Directional only
Gemini 2.5 Flash
45.3
Estimated · #108/183
GPT-5.4 nano
47.4
Supported · #97/183
Basis
BenchAlign lane · 0 vs 5 public rows
Reading
Directional only

Instruction following

Directional only
Gemini 2.5 Flash
45.3
#93/123
GPT-5.4 nano
93.2
#9/123
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Directional only

Agentic

Not comparable
Gemini 2.5 Flash
Not ranked
GPT-5.4 nano
34.6
Supported · #133/152
Basis
BenchAlign lane · 0 vs 6 public rows
Reading
Not comparable

Coding

Not comparable
Gemini 2.5 Flash
Not ranked
GPT-5.4 nano
37.1
Supported · #126/151
Basis
BenchAlign lane · 0 vs 3 public rows
Reading
Not comparable

Reasoning

Not comparable
Gemini 2.5 Flash
55.3
Unranked · 2 rankable rows
GPT-5.4 nano
73.7
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Gemini 2.5 Flash
28.7
Unranked · 2 rankable rows
GPT-5.4 nano
43.9
Unranked · 2 rankable rows
Basis
Provisional lane · 2 vs 2 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 2.5 Flash
Not ranked
GPT-5.4 nano
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 2.5 Flash
56.5
Unranked · 1 rankable row
GPT-5.4 nano
23.8
#45/48
Basis
Provisional lane · 0 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) 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.

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

    Gemini 2.5 Flash: 4.844%GPT-5.4 nano: 25.860%Normalized gap 21.0Shared source

  • FrontierMath v2 (Tier 4)

    Math

    Gemini 2.5 Flash: 4.167%GPT-5.4 nano: 6.250%Normalized gap 2.1Shared source

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 2.5 Flash
$0.00155
Fits in one request
GPT-5.4 nano
$0.00082
Fits in one request

GPT-5.4 nano has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Gemini 2.5 Flash
$0.0225
Fits in one request
GPT-5.4 nano
$0.01375
Fits in one request

GPT-5.4 nano 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 2.5 Flash
$0.037
Fits in one request
GPT-5.4 nano
$0.0205
Fits in one request

GPT-5.4 nano has the lower modeled cost

Costs use the listed standard API rates.

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 2.5 Flash

$0.03 per 1M cached input tokens

Google Gemini API pricing

GPT-5.4 nano

$0.02 per 1M cached input tokens

OpenAI pricing

Provider availability

Gemini 2.5 Flash

Not sourced

GPT-5.4 nano

Generally Available · OpenAI Responses API

OpenAI model catalog

Reasoning profile

Gemini 2.5 Flash

Non-Reasoning

GPT-5.4 nano

Reasoning

Weight access

Gemini 2.5 Flash

Proprietary

GPT-5.4 nano

Proprietary

License

Gemini 2.5 Flash

Proprietary

GPT-5.4 nano

Proprietary

Release date

Gemini 2.5 Flash

2025-06-17

GPT-5.4 nano

2026-03-17

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
GPT-5.4 nano has the higher public score estimate, 59.57 versus 50.97, but the 90% score intervals overlap.
Workload cost
Repository review: $0.0225 vs $0.01375. Cache-heavy agent loop: $0.037 vs $0.0205.
Context tradeoff
Gemini 2.5 Flash has the larger documented window (1M).

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 evidence18 rows

Agentic

  • Terminal-Bench 2.0

    Gemini 2.5 Flash—
    GPT-5.4 nano46.3%
    Source

    Not directly comparable

  • OSWorld-Verified

    Gemini 2.5 Flash—
    GPT-5.4 nano39%
    Source

    Not directly comparable

  • MCP Atlas

    Gemini 2.5 Flash—
    GPT-5.4 nano56.1%
    Source

    Not directly comparable

  • Toolathlon

    Gemini 2.5 Flash—
    GPT-5.4 nano35.5%
    Source

    Not directly comparable

  • τ²-bench results

    Gemini 2.5 Flash—
    GPT-5.4 nano92.5%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Gemini 2.5 Flash—
    GPT-5.4 nano41.6%
    Source

    Not directly comparable

Coding

  • Vibe Code Bench

    Gemini 2.5 Flash—
    GPT-5.4 nano26.10%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    Gemini 2.5 Flash—
    GPT-5.4 nano84.0%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    Gemini 2.5 Flash—
    GPT-5.4 nano69.8%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Gemini 2.5 Flash—
    GPT-5.4 nano82.8%
    Source

    Not directly comparable

  • HLE

    Gemini 2.5 Flash—
    GPT-5.4 nano37.7%
    Source

    Not directly comparable

  • HLE w/o tools

    Gemini 2.5 Flash—
    GPT-5.4 nano24.3%
    Source

    Not directly comparable

  • GPQA Diamond (Vals)

    Gemini 2.5 Flash—
    GPT-5.4 nano77.5%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    Gemini 2.5 Flash—
    GPT-5.4 nano77.2%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    Gemini 2.5 Flash4.844%
    GPT-5.4 nano25.860%

    GPT-5.4 nano leads this result

  • FrontierMath v2 (Tier 4)

    Shared source
    Gemini 2.5 Flash4.167%
    GPT-5.4 nano6.250%

    GPT-5.4 nano leads this result

Multimodal

  • MMMU-Pro

    Gemini 2.5 Flash—
    GPT-5.4 nano66.1%
    Source

    Not directly comparable

  • MMMU-Pro w/ Python

    Gemini 2.5 Flash—
    GPT-5.4 nano69.5%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Gemini 2.5 Flash or GPT-5.4 nano?

GPT-5.4 nano has the higher public score estimate, 59.57 versus 50.97, 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 2.5 Flash or GPT-5.4 nano?

Gemini 2.5 Flash is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Gemini 2.5 Flash or GPT-5.4 nano?

Gemini 2.5 Flash is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Gemini 2.5 Flash or GPT-5.4 nano?

For the stated presets, chat costs $0.00155 on Gemini 2.5 Flash and $0.00082 on GPT-5.4 nano; repository review costs $0.0225 and $0.01375; the cache-heavy agent loop costs $0.037 and $0.0205. Costs use the listed standard API rates.

Which has the larger context window, Gemini 2.5 Flash or GPT-5.4 nano?

Gemini 2.5 Flash has the larger documented context window: 1M, compared with 400K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 10, 2026

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