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BenchLM

Gemini 3 Flash vs GPT-4.1 mini

Updated September 29, 2026. Rank says Gemini 3 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

Gemini 3 Flash has the higher public score, 55.5 versus 29.09, and the 90% score intervals do not overlap. 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

55.5/100

Supported · Public rank #56

90% interval 40.3–70.7

Model B
OpenAI logo

OpenAI

29.09/100

Estimated · Public rank #176

90% interval 22.4–35.7

Shared results
1
Gemini 3 Flash only
10
GPT-4.1 mini only
4
Like-for-like categories
1 / 8
Supported: Gemini 3 Flash · Estimated: GPT-4.1 miniHow 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

    GPT-4.1 mini

    GPT-4.1 mini 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 Flash

    Gemini 3 Flash has the lower estimated token cost for this stated workload. GPT-4.1 mini 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

    GPT-4.1 mini

    GPT-4.1 mini 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 mini 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

    GPT-4.1 mini 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.

36.4Gemini 3 Flash19.8GPT-4.1 mini

Directional only · BenchAlign v5.7

Gemini 3 Flash scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.

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.

2 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.

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 31.2
    Gemini 3 Flash:35.640%
    GPT-4.1 mini:4.483%
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 v5.7 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.

Knowledge

Like-for-like
Gemini 3 Flash
54.3
Supported · #47/169
GPT-4.1 mini
31.1
Supported · #136/169
Basis
BenchAlign v5.7 lane · 2 vs 2 public rows
Reading
Gemini 3 Flash leads

Coding

Directional only
Gemini 3 Flash
36.4
Supported · #74/143
GPT-4.1 mini
19.8
Estimated · #123/143
Basis
BenchAlign v5.7 lane · 3 vs 1 public rows
Reading
Directional only

Instruction following

Directional only
Gemini 3 Flash
64.7
#70/124
GPT-4.1 mini
42.8
#94/124
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Directional only

Agentic

Not comparable
Gemini 3 Flash
31.7
Supported · #76/117
GPT-4.1 mini
Not ranked
Basis
BenchAlign v5.7 lane · 4 vs 0 public rows
Reading
Not comparable

Reasoning

Not comparable
Gemini 3 Flash
60.3
Unranked · 2 rankable rows
GPT-4.1 mini
52.5
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 3 Flash
76.9
Unranked · 1 rankable row
GPT-4.1 mini
47.6
Unranked · 1 rankable row
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 3 Flash
Not ranked
GPT-4.1 mini
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Gemini 3 Flash
50.0
Unranked · 2 rankable rows
GPT-4.1 mini
28.4
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 v5.7) 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 Flash
$0.002
Fits in one request
GPT-4.1 mini
$0.0012
Fits in one request

GPT-4.1 mini has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Gemini 3 Flash
$0.034
Fits in one request
GPT-4.1 mini
$0.0248
Fits in one request

GPT-4.1 mini 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 Flash
$0.05
Fits in one request
GPT-4.1 mini
$0.104
Fits in one request
Cached input priced at the published list-input rate

Gemini 3 Flash has the lower modeled cost

GPT-4.1 mini 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.

Context window

Maximum documented context; output-token limits may be lower.

GPT-4.1 mini

1M

Cached-input rate

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

Gemini 3 Flash

$0.05 per 1M cached input tokens

Google Gemini API pricing

GPT-4.1 mini

Not published

Documented inputs

Gemini 3 Flash

Not sourced

GPT-4.1 mini

Not sourced

Documented outputs

Gemini 3 Flash

Not sourced

GPT-4.1 mini

Not sourced

Provider availability

Gemini 3 Flash

Not sourced

GPT-4.1 mini

Not sourced

Reasoning profile

Gemini 3 Flash

Non-Reasoning

GPT-4.1 mini

Non-Reasoning

Weight access

Gemini 3 Flash

Proprietary

GPT-4.1 mini

Proprietary

License

Gemini 3 Flash

Proprietary

GPT-4.1 mini

Proprietary

Release date

Gemini 3 Flash

2025-12-01

GPT-4.1 mini

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 Flash has the higher public score, 55.5 versus 29.09, and the 90% score intervals do not overlap.
Workload cost
Repository review: $0.034 vs $0.0248. Cache-heavy agent loop: $0.05 vs $0.104.
Context tradeoff
Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Gemini 3 Flash or GPT-4.1 mini?

Gemini 3 Flash has the higher public score, 55.5 versus 29.09, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, Gemini 3 Flash or GPT-4.1 mini?

Gemini 3 Flash scores higher for coding on the public lane, 36.4 to 19.8. GPT-4.1 mini 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 Flash or GPT-4.1 mini?

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

Which costs less, Gemini 3 Flash or GPT-4.1 mini?

For the stated presets, chat costs $0.002 on Gemini 3 Flash and $0.0012 on GPT-4.1 mini; repository review costs $0.034 and $0.0248; the cache-heavy agent loop costs $0.05 and $0.104. GPT-4.1 mini has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Gemini 3 Flash or GPT-4.1 mini?

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 evidence15 rows

Agentic

  • Claw-Eval

    Gemini 3 Flash49.2%
    Source
    GPT-4.1 mini—

    Not directly comparable

  • Gert Labs

    Gemini 3 Flash56.63%
    Source
    GPT-4.1 mini—

    Not directly comparable

  • JobBench

    Gemini 3 Flash11.4%
    Source
    GPT-4.1 mini—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Gemini 3 Flash53.9%
    Source
    GPT-4.1 mini—

    Not directly comparable

Coding

  • Vibe Code Bench

    Gemini 3 Flash20.20%
    Source
    GPT-4.1 mini—

    Not directly comparable

  • LiveCodeBench (Vals)

    Gemini 3 Flash85.6%
    Source
    GPT-4.1 mini—

    Not directly comparable

  • SWE-bench (Vals)

    Gemini 3 Flash75.0%
    Source
    GPT-4.1 mini—

    Not directly comparable

  • SWE-bench Verified

    Gemini 3 Flash—
    GPT-4.1 mini23.6%
    Source

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    Gemini 3 Flash87.9%
    Source
    GPT-4.1 mini—

    Not directly comparable

  • MMLU-Pro (Vals)

    Gemini 3 Flash88.6%
    Source
    GPT-4.1 mini—

    Not directly comparable

  • MMLU

    Gemini 3 Flash—
    GPT-4.1 mini87.5%
    Source

    Not directly comparable

  • GPQA

    Gemini 3 Flash—
    GPT-4.1 mini64.2%
    Source

    Not directly comparable

Instruction following

  • IFEval

    Gemini 3 Flash—
    GPT-4.1 mini88.5%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    Gemini 3 Flash35.640%
    GPT-4.1 mini4.483%

    Gemini 3 Flash leads this result

  • FrontierMath v2 (Tier 4)

    Gemini 3 Flash4.167%
    Source
    GPT-4.1 mini—

    Not directly comparable

15 public results · 1 shared

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Last updated September 29, 2026