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Gemini 4 Argon vs GPT-5 mini

Updated November 13, 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

Gemini 4 Argon has the higher public score, 79.03 versus 47.98, 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
Gemini 4 Argon

Google

79.03/100

Supported · Public rank #8

90% interval 75.4–82.7

Model B
OpenAI logo
GPT-5 mini

OpenAI

47.98/100

Supported · Public rank #106

90% interval 36.4–59.5

API price per 1M tokens

Gemini 4 Argon: $2 input / $10 output

GPT-5 mini: $0.25 input / $2 output

Context window

Gemini 4 Argon: 1M

GPT-5 mini: 128K

Released

Gemini 4 Argon: September 30, 2026

GPT-5 mini: August 7, 2025

Shared results
1
Gemini 4 Argon only
38
GPT-5 mini only
0
Like-for-like categories
0 / 8
Supported: Gemini 4 Argon and GPT-5 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.

  • Long documents

    Prompts that approach the documented context limit

    Gemini 4 Argon

    Gemini 4 Argon has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    GPT-5 mini

    GPT-5 mini 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 mini

    GPT-5 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-5 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-5 mini is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Cache-heavy agent loop cost

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

    Not enough matched evidence

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GPT-5 mini does not fit this workload in one request. GPT-5 mini has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

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.

71.8Gemini 4 Argon28.1GPT-5 mini

Directional only · BenchAlign v5.8

Gemini 4 Argon has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

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

Open the coding leaderboardShortlist models for this job
Same basis rules as the category table below

Or describe your own job

The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.

Describe the task and constraints. This selector recommends models; it cannot carry out the task or access your files and accounts.

We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy

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.

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 v5.8 lane for agentic, coding, reasoning, multimodal, 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 4 Argon
74.9
Supported · #6/135
GPT-5 mini
30.3
Estimated · #103/135
Basis
BenchAlign v5.8 lane · 13 vs 0 public rows
Reading
Directional only

Coding

Directional only
Gemini 4 Argon
71.8
Supported · #9/162
GPT-5 mini
28.1
Estimated · #118/162
Basis
BenchAlign v5.8 lane · 9 vs 1 public rows
Reading
Directional only

Reasoning

Not comparable
Gemini 4 Argon
76.5
Supported · #7/160
GPT-5 mini
Not ranked
Basis
BenchAlign v5.8 lane · 7 vs 0 public rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 4 Argon
Not ranked
GPT-5 mini
Not ranked
Basis
BenchAlign v5.8 lane · 5 vs 0 public rows
Reading
Not comparable

Knowledge

Not comparable
Gemini 4 Argon
79.2
Supported · #8/180
GPT-5 mini
Not ranked
Basis
BenchAlign v5.8 lane · 2 vs 0 public rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 4 Argon
Not ranked
GPT-5 mini
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 4 Argon
Not ranked
GPT-5 mini
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Gemini 4 Argon
87.4
Unranked · 2 rankable rows
GPT-5 mini
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, reasoning, multimodal, 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 4 Argon
$0.007
Fits in one request
GPT-5 mini
$0.00125
Fits in one request

GPT-5 mini has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Gemini 4 Argon
$0.13
Fits in one request
GPT-5 mini
$0.0185
Fits in one request

GPT-5 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 4 Argon
$0.16
Fits in one request
GPT-5 mini
$0.075
Does not fit in one request
Cached input priced at the published list-input rate

GPT-5 mini does not fit this workload in one request. GPT-5 mini has no published cached-input rate, so cached tokens use its listed input rate.

Your workload

Enter a month of tokens to price it at both models’ listed API rates.

Gemini 4 Argon
$40.00
per month
GPT-5 mini
$6.50
per month

Standard listed rates, no batch or volume discounts.

Cached input falls back to the list input rate only where a cached rate is unpublished

Price against score, among all priced models

Overall score against output price per 1M tokens, one model per family. Pricing as of November 12, 2026.

Gemini 4 ArgonGPT-5 mini

20406080100$0.10$0.50$1$5$10$50$100Output price, $ per 1M tokens (log)Overall score
  • 2 models at the same price score higher than Gemini 4 Argon. The nearest is Gemini 4 Carbon (score 89.2, also $10.00 per 1M output tokens).
  • 9 cheaper models score at least as high as GPT-5 mini. The nearest is Quasar 438B (score 49.1, $1.80 per 1M output tokens).

Gray dots are the other priced models. The dark step line is the efficiency frontier: for a model under it, another model costs no more and scores at least as high.

See GPT-5 mini on the full price vs performance chart

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 4 Argon

$0.1 per 1M cached input tokens

Google Gemini API pricing

GPT-5 mini

Not published

Reasoning profile

Gemini 4 Argon

Reasoning

GPT-5 mini

Reasoning

Weight access

Gemini 4 Argon

Proprietary

GPT-5 mini

Proprietary

License

Gemini 4 Argon

Proprietary

GPT-5 mini

Proprietary

Release date

Gemini 4 Argon

2026-09-30

GPT-5 mini

2025-08-07

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 4 Argon has the higher public score, 79.03 versus 47.98, and the 90% score intervals do not overlap.
Workload cost
Repository review: $0.13 vs $0.0185. Cache-heavy agent loop: $0.16 vs $0.075.
Context tradeoff
Gemini 4 Argon has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Gemini 4 Argon or GPT-5 mini?

Gemini 4 Argon has the higher public score, 79.03 versus 47.98, 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 4 Argon or GPT-5 mini?

Gemini 4 Argon scores higher for coding on the public lane, 71.8 to 28.1. GPT-5 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 4 Argon or GPT-5 mini?

Gemini 4 Argon scores higher for agentic tasks on the public lane, 74.9 to 30.3. GPT-5 mini 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 4 Argon or GPT-5 mini?

For the stated presets, chat costs $0.007 on Gemini 4 Argon and $0.00125 on GPT-5 mini; repository review costs $0.13 and $0.0185; the cache-heavy agent loop costs $0.16 and $0.075. GPT-5 mini does not fit this workload in one request. GPT-5 mini has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Gemini 4 Argon or GPT-5 mini?

Gemini 4 Argon has the larger documented context window: 1M, compared with 128K.

Benchmark evidence

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

Browse raw public benchmark evidence39 rows

Agentic

  • AutomationBench

    Gemini 4 Argon51.3%
    Source
    GPT-5 mini—

    Not directly comparable

  • Finance Agent v2

    Gemini 4 Argon65.4%
    Source
    GPT-5 mini—

    Not directly comparable

  • Terminal-Bench 4.0

    Gemini 4 Argon57.40%
    Source
    GPT-5 mini—

    Not directly comparable

  • Agents' Last Exam

    Gemini 4 Argon39.5%
    Source
    GPT-5 mini—

    Not directly comparable

  • OSWorld 2.0

    Gemini 4 Argon69.2%
    Source
    GPT-5 mini—

    Not directly comparable

  • CWE-bench v1

    Gemini 4 Argon68.0%
    Source
    GPT-5 mini—

    Not directly comparable

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

    Gemini 4 Argon57.6%
    Source
    GPT-5 mini—

    Not directly comparable

  • MazeBench

    Gemini 4 Argon12%
    Source
    GPT-5 mini—

    Not directly comparable

  • OSWorld 2.1 Offline

    Gemini 4 Argon74.7%
    Source
    GPT-5 mini—

    Not directly comparable

  • ExploitGym

    Gemini 4 Argon39.5%
    Source
    GPT-5 mini—

    Not directly comparable

  • RoboDojo Sim

    Gemini 4 Argon34.2%
    Source
    GPT-5 mini—

    Not directly comparable

  • CWE-bench

    Gemini 4 Argon65.0%
    Source
    GPT-5 mini—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Gemini 4 Argon57.7%
    Source
    GPT-5 mini—

    Not directly comparable

Coding

  • ScarfBench

    Gemini 4 Argon75.2%
    Source
    GPT-5 mini—

    Not directly comparable

  • DeepSWE

    Gemini 4 Argon77.9%
    Source
    GPT-5 mini—

    Not directly comparable

  • FrontierSWE v2

    Gemini 4 Argon55.0%
    Source
    GPT-5 mini—

    Not directly comparable

  • Vibe Code Bench

    Gemini 4 Argon91.90%
    Source
    GPT-5 mini14.17%
    Source

    Gemini 4 Argon leads this result

  • PostTrainBench v1.1

    Gemini 4 Argon45.3%
    Source
    GPT-5 mini—

    Not directly comparable

  • Bug Hunt Bench

    Gemini 4 Argon40.7 fixes
    Source
    GPT-5 mini—

    Not directly comparable

  • FrontierCode 2.0

    Gemini 4 Argon26.3%
    Source
    GPT-5 mini—

    Not directly comparable

  • CursorBench 4.0

    Gemini 4 Argon52.9%
    Source
    GPT-5 mini—

    Not directly comparable

  • PostTrainBench

    Gemini 4 Argon40.4%
    Source
    GPT-5 mini—

    Not directly comparable

Reasoning

  • STEMBench

    Gemini 4 Argon2.44
    Source
    GPT-5 mini—

    Not directly comparable

  • Graphwalks BFS 128K

    Gemini 4 Argon99.7%
    Source
    GPT-5 mini—

    Not directly comparable

  • GraphWalks BFS 256K–1M

    Gemini 4 Argon84.2%
    Source
    GPT-5 mini—

    Not directly comparable

  • ARC-AGI-1

    Gemini 4 Argon98.50%
    Source
    GPT-5 mini—

    Not directly comparable

  • ARC-AGI-2

    Gemini 4 Argon91.3%
    Source
    GPT-5 mini—

    Not directly comparable

  • ARC-AGI-3

    Gemini 4 Argon46.3%
    Source
    GPT-5 mini—

    Not directly comparable

  • ARC-AGI-3 Provider Adapter

    Gemini 4 Argon97.30%
    Source
    GPT-5 mini—

    Not directly comparable

Multimodal

  • ParseBench-2

    Gemini 4 Argon84.20%
    Source
    GPT-5 mini—

    Not directly comparable

  • ExtractBench Pro

    Gemini 4 Argon30%
    Source
    GPT-5 mini—

    Not directly comparable

  • Chartography (no tools)

    Gemini 4 Argon71.6%
    Source
    GPT-5 mini—

    Not directly comparable

  • LVBench

    Gemini 4 Argon91.7%
    Source
    GPT-5 mini—

    Not directly comparable

  • Blueprint-Bench 2

    Gemini 4 Argon54.4%
    Source
    GPT-5 mini—

    Not directly comparable

Knowledge

  • LABBench2

    Gemini 4 Argon88.8%
    Source
    GPT-5 mini—

    Not directly comparable

  • Morphology-to-molecule matching

    Gemini 4 Argon25.2%
    Source
    GPT-5 mini—

    Not directly comparable

Instruction following

  • Gray Swan IPI (15 attempts)

    Gemini 4 Argon0.7%
    Source
    GPT-5 mini—

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Gemini 4 Argon85.965%
    Source
    GPT-5 mini—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Gemini 4 Argon78.049%
    Source
    GPT-5 mini—

    Not directly comparable

39 public results · 1 shared

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Last updated November 13, 2026