Gemini 4 Argon vs GPT-5.4

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.

Decision reading

Gemini 4 Argon has the higher public score, 79.03 versus 65.43, and the 90% score intervals do not overlap. 10 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Gemini 4 Argon

Google

79.03/100

Supported · Public rank #8

90% interval 75.4–82.7

Model B

GPT-5.4

OpenAI

65.43/100

Supported · Public rank #26

90% interval 60.4–70.5

API price per 1M tokens

Gemini 4 Argon: $2 input / $10 output

GPT-5.4: $2.50 input / $15 output

Context window

Gemini 4 Argon: 1M

GPT-5.4: 1.05M

Released

Gemini 4 Argon: September 30, 2026

GPT-5.4: March 5, 2026

Shared results: 10
Gemini 4 Argon only: 29
GPT-5.4 only: 41
Like-for-like categories: 4 / 8

Supported: Gemini 4 Argon and GPT-5.4How 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.

Coding work
Code generation, repair, and software-engineering tasks
Gemini 4 Argon
Gemini 4 Argon has the higher public coding point estimate, 71.8 to 48.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Gemini 4 Argon
Gemini 4 Argon has the higher public agentic point estimate, 74.9 to 51.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Confidence: limited
Long documents
Prompts that approach the documented context limit
GPT-5.4
GPT-5.4 has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Chat turn cost
1K fresh input + 500 output tokens
Gemini 4 Argon
Gemini 4 Argon 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 4 Argon
Gemini 4 Argon has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Gemini 4 Argon
Gemini 4 Argon has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates

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.

Pick the job you would use them for

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 Argon48.7GPT-5.4

Like-for-like · 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 leaderboard Shortlist 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.

What do you need a model for?

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

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.

ARC-AGI-3Reasoning
Normalized gap 46.1
Gemini 4 Argon:46.3%
GPT-5.4:0.2%
Gemini 4 Argon source GPT-5.4 source
FrontierMath v2 (Tier 4)Math
Normalized gap 29.0
Gemini 4 Argon:78.049%
GPT-5.4:49.000%
Shared source
ARC-AGI-2Reasoning
Normalized gap 17.3
Gemini 4 Argon:91.3%
GPT-5.4:74.0%
Gemini 4 Argon source GPT-5.4 source
FrontierMath v2 (Tiers 1-3)Math
Normalized gap 7.4
Gemini 4 Argon:85.965%
GPT-5.4:78.596%
Shared source

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, 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

Like-for-like

Gemini 4 Argon: 74.9; Supported · #6/135
GPT-5.4: 51.6; Supported · #52/135
Basis: BenchAlign v5.8 lane · 13 vs 16 public rows
Reading: Gemini 4 Argon leads

Coding

Like-for-like

Gemini 4 Argon: 71.8; Supported · #9/162
GPT-5.4: 48.7; Supported · #51/162
Basis: BenchAlign v5.8 lane · 9 vs 10 public rows
Reading: Gemini 4 Argon leads

Reasoning

Like-for-like

Gemini 4 Argon: 76.5; Supported · #7/160
GPT-5.4: 61.9; Supported · #27/160
Basis: BenchAlign v5.8 lane · 7 vs 4 public rows
Reading: Gemini 4 Argon leads · intervals overlap

Knowledge

Like-for-like

Gemini 4 Argon: 79.2; Supported · #8/180
GPT-5.4: 65.7; Supported · #25/180
Basis: BenchAlign v5.8 lane · 2 vs 9 public rows
Reading: Gemini 4 Argon leads · intervals overlap

Multimodal

Not comparable

Gemini 4 Argon: Not ranked
GPT-5.4: 64.0; Estimated · #29/67
Basis: BenchAlign v5.8 lane · 5 vs 10 public rows
Reading: Not comparable

Multilingual

Not comparable

Gemini 4 Argon: Not ranked
GPT-5.4: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Gemini 4 Argon: Not ranked
GPT-5.4: 90.7; #25/157
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Gemini 4 Argon: 87.4; Unranked · 2 rankable rows
GPT-5.4: 77.1; Unranked · 2 rankable rows
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Gemini 4 Argon and GPT-5.4
Category	Gemini 4 Argon	GPT-5.4	Basis	Reading
Agentic	74.9Supported · #6/135	51.6Supported · #52/135	Like-for-likeBenchAlign v5.8 lane · 13 vs 16 public rows	Gemini 4 Argon leads
Coding	71.8Supported · #9/162	48.7Supported · #51/162	Like-for-likeBenchAlign v5.8 lane · 9 vs 10 public rows	Gemini 4 Argon leads
Reasoning	76.5Supported · #7/160	61.9Supported · #27/160	Like-for-likeBenchAlign v5.8 lane · 7 vs 4 public rows	Gemini 4 Argon leads · intervals overlap
Knowledge	79.2Supported · #8/180	65.7Supported · #25/180	Like-for-likeBenchAlign v5.8 lane · 2 vs 9 public rows	Gemini 4 Argon leads · intervals overlap
Multimodal	Not ranked	64.0Estimated · #29/67	Not comparableBenchAlign v5.8 lane · 5 vs 10 public rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	90.7#25/157	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	87.4Unranked · 2 rankable rows	77.1Unranked · 2 rankable rows	Not comparableProvisional lane · 2 vs 2 weighted rows	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-5.4: $0.01; Fits in one request

Gemini 4 Argon 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-5.4: $0.17; Fits in one request

Gemini 4 Argon 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-5.4: $0.25; Fits in one request

Gemini 4 Argon has the lower modeled cost

Costs use the listed standard API rates.

Your workload

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

Input, millionsOutput, millionsCached input, %

Gemini 4 Argon: $40.00; per month
GPT-5.4: $55.00; 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-5.4

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).
8 models score at least as high as GPT-5.4 for the same price or less. The nearest is Kimi K3 (score 68.4, also $15.00 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 Gemini 4 Argon 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.

SpecificationGemini 4 ArgonGPT-5.4

Context window

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

Gemini 4 Argon

Google Gemini 4 Argon model documentation

GPT-5.4

1.05M

OpenAI pricing

API model ID

Gemini 4 Argon

gemini-4-argon

Google Gemini 4 Argon model documentation

GPT-5.4

gpt-5.4

OpenAI pricing

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-5.4

$0.25 per 1M cached input tokens

OpenAI pricing

Documented inputs

Gemini 4 Argon

text, image, video, audio, pdf

Google Gemini 4 Argon model documentation

GPT-5.4

text, image

OpenAI model page for gpt-5.4

Documented outputs

Gemini 4 Argon

text

Google Gemini 4 Argon model documentation

GPT-5.4

text

OpenAI model page for gpt-5.4

Provider availability

Gemini 4 Argon

Generally Available · Gemini API, Google AI Studio

Google Gemini 4 Argon model documentation

GPT-5.4

Not sourced

Reasoning profile

Gemini 4 Argon

Reasoning

GPT-5.4

Reasoning

Weight access

Gemini 4 Argon

Proprietary

GPT-5.4

Proprietary

License

Gemini 4 Argon

Proprietary

GPT-5.4

Proprietary

Release date

Gemini 4 Argon

2026-09-30

GPT-5.4

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: Gemini 4 Argon has the higher public score, 79.03 versus 65.43, and the 90% score intervals do not overlap.
Workload cost: Repository review: $0.13 vs $0.17. Cache-heavy agent loop: $0.16 vs $0.25.
Context tradeoff: GPT-5.4 has the larger documented window (1.05M).

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

Questions

Which is better, Gemini 4 Argon or GPT-5.4?

Gemini 4 Argon has the higher public score, 79.03 versus 65.43, 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-5.4?

Gemini 4 Argon has the higher public coding point estimate, 71.8 to 48.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which is better for agentic tasks, Gemini 4 Argon or GPT-5.4?

Gemini 4 Argon has the higher public agentic tasks point estimate, 74.9 to 51.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which costs less, Gemini 4 Argon or GPT-5.4?

For the stated presets, chat costs $0.007 on Gemini 4 Argon and $0.01 on GPT-5.4; repository review costs $0.13 and $0.17; the cache-heavy agent loop costs $0.16 and $0.25. Costs use the listed standard API rates.

Which has the larger context window, Gemini 4 Argon or GPT-5.4?

GPT-5.4 has the larger documented context window: 1.05M, compared with 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 evidence80 rows

Agentic

AutomationBench
Gemini 4 Argon51.3%
Source
GPT-5.4—
Not directly comparable
Finance Agent v2
Gemini 4 Argon65.4%
Source
GPT-5.4—
Not directly comparable
Terminal-Bench 4.0
Gemini 4 Argon57.40%
Source
GPT-5.4—
Not directly comparable
Agents' Last Exam
Gemini 4 Argon39.5%
Source
GPT-5.4—
Not directly comparable
OSWorld 2.0
Gemini 4 Argon69.2%
Source
GPT-5.4—
Not directly comparable
CWE-bench v1
Gemini 4 Argon68.0%
Source
GPT-5.4—
Not directly comparable
Terminal-Bench-Science 0.1 (6x verifier timeout)
Gemini 4 Argon57.6%
Source
GPT-5.4—
Not directly comparable
MazeBench
Gemini 4 Argon12%
Source
GPT-5.40%
Source
Gemini 4 Argon leads this result
OSWorld 2.1 Offline
Gemini 4 Argon74.7%
Source
GPT-5.4—
Not directly comparable
ExploitGym
Gemini 4 Argon39.5%
Source
GPT-5.46.0%
Source
Gemini 4 Argon leads this result
RoboDojo Sim
Gemini 4 Argon34.2%
Source
GPT-5.4—
Not directly comparable
CWE-bench
Gemini 4 Argon65.0%
Source
GPT-5.4—
Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 4 Argon57.7%
Source
GPT-5.4—
Not directly comparable
LifeVal
Gemini 4 Argon—
GPT-5.473.0%
Source
Not directly comparable
Terminal-Bench 2.0
Gemini 4 Argon—
GPT-5.475.1%
Source
Not directly comparable
CyberGym
Gemini 4 Argon—
GPT-5.479.0%
Source
Not directly comparable
BrowseComp
Gemini 4 Argon—
GPT-5.482.7%
Source
Not directly comparable
OSWorld-Verified
Gemini 4 Argon—
GPT-5.475%
Source
Not directly comparable
MCP Atlas
Gemini 4 Argon—
GPT-5.470.6%
Source
Not directly comparable
Toolathlon
Gemini 4 Argon—
GPT-5.454.6%
Source
Not directly comparable
τ²-bench results
Gemini 4 Argon—
GPT-5.498.9%
Source
Not directly comparable
Claw-Eval
Gemini 4 Argon—
GPT-5.460.3%
Source
Not directly comparable
DeepSearchQA
Gemini 4 Argon—
GPT-5.473.6%
Source
Not directly comparable
Gert Labs
Gemini 4 Argon—
GPT-5.464.89%
Source
Not directly comparable
ResearchClawBench
Gemini 4 Argon—
GPT-5.415.3%
Source
Not directly comparable
JobBench
Gemini 4 Argon—
GPT-5.438.9%
Source
Not directly comparable
ApprenticeBench
Gemini 4 Argon—
GPT-5.411%
Source
Not directly comparable

Coding

ScarfBench
Gemini 4 Argon75.2%
Source
GPT-5.4—
Not directly comparable
DeepSWE
Gemini 4 Argon77.9%
Source
GPT-5.4—
Not directly comparable
FrontierSWE v2
Gemini 4 Argon55.0%
Source
GPT-5.4—
Not directly comparable
Vibe Code Bench
Gemini 4 Argon91.90%
Source
GPT-5.467.42%
Source
Gemini 4 Argon leads this result
PostTrainBench v1.1
Gemini 4 Argon45.3%
Source
GPT-5.419.0%
Source
Gemini 4 Argon leads this result
Bug Hunt Bench
Gemini 4 Argon40.7 fixes
Source
GPT-5.4—
Not directly comparable
FrontierCode 2.0
Gemini 4 Argon26.3%
Source
GPT-5.4—
Not directly comparable
CursorBench 4.0
Gemini 4 Argon52.9%
Source
GPT-5.4—
Not directly comparable
PostTrainBench
Shared source
Gemini 4 Argon40.4%
GPT-5.417.8%
Gemini 4 Argon leads this result
LiveCodeBench Pro
Gemini 4 Argon—
GPT-5.487.5%
Source
Not directly comparable
SWE-bench Pro
Gemini 4 Argon—
GPT-5.457.7%
Source
Not directly comparable
React Native Evals
Gemini 4 Argon—
GPT-5.485.3%
Source
Not directly comparable
Terminal-Bench 2.0
Gemini 4 Argon—
GPT-5.475.1%
Source
Not directly comparable
LiveCodeBench (Vals)
Gemini 4 Argon—
GPT-5.484.1%
Source
Not directly comparable
SWE-bench (Vals)
Gemini 4 Argon—
GPT-5.478.2%
Source
Not directly comparable
PostTrainBench v1
Gemini 4 Argon—
GPT-5.420.2%
Source
Not directly comparable

Reasoning

STEMBench
Gemini 4 Argon2.44
Source
GPT-5.4—
Not directly comparable
Graphwalks BFS 128K
Gemini 4 Argon99.7%
Source
GPT-5.4—
Not directly comparable
GraphWalks BFS 256K–1M
Gemini 4 Argon84.2%
Source
GPT-5.4—
Not directly comparable
ARC-AGI-1
Gemini 4 Argon98.50%
Source
GPT-5.493.67%
Source
Gemini 4 Argon leads this result
ARC-AGI-2
Gemini 4 Argon91.3%
Source
GPT-5.474.0%
Source
Gemini 4 Argon leads this result
ARC-AGI-3
Gemini 4 Argon46.3%
Source
GPT-5.40.2%
Source
Gemini 4 Argon leads this result
ARC-AGI-3 Provider Adapter
Gemini 4 Argon97.30%
Source
GPT-5.4—
Not directly comparable
BioBasicsBench
Gemini 4 Argon—
GPT-5.433.2%
Source
Not directly comparable

Multimodal

ParseBench-2
Gemini 4 Argon84.20%
Source
GPT-5.4—
Not directly comparable
ExtractBench Pro
Gemini 4 Argon30%
Source
GPT-5.4—
Not directly comparable
Chartography (no tools)
Gemini 4 Argon71.6%
Source
GPT-5.4—
Not directly comparable
LVBench
Gemini 4 Argon91.7%
Source
GPT-5.4—
Not directly comparable
Blueprint-Bench 2
Gemini 4 Argon54.4%
Source
GPT-5.4—
Not directly comparable
ParseBench-1
Gemini 4 Argon—
GPT-5.461.72%
Source
Not directly comparable
MMMU-Pro
Gemini 4 Argon—
GPT-5.481.2%
Source
Not directly comparable
OfficeQA Pro
Gemini 4 Argon—
GPT-5.453.2%
Source
Not directly comparable
MMMU-Pro w/ Python
Gemini 4 Argon—
GPT-5.482.1%
Source
Not directly comparable
CharXiv
Gemini 4 Argon—
GPT-5.482.8%
Source
Not directly comparable
ERQA
Gemini 4 Argon—
GPT-5.465.4%
Source
Not directly comparable
SimpleVQA
Gemini 4 Argon—
GPT-5.461.1%
Source
Not directly comparable
ScreenSpot Pro
Gemini 4 Argon—
GPT-5.485.4%
Source
Not directly comparable
ZeroBench
Gemini 4 Argon—
GPT-5.441.0%
Source
Not directly comparable
MedXpertQA (MM)
Gemini 4 Argon—
GPT-5.477.1%
Source
Not directly comparable

Knowledge

LABBench2
Gemini 4 Argon88.8%
Source
GPT-5.4—
Not directly comparable
Morphology-to-molecule matching
Gemini 4 Argon25.2%
Source
GPT-5.4—
Not directly comparable
GPQA
Gemini 4 Argon—
GPT-5.492.8%
Source
Not directly comparable
HLE
Gemini 4 Argon—
GPT-5.452.1%
Source
Not directly comparable
HLE w/o tools
Gemini 4 Argon—
GPT-5.439.8%
Source
Not directly comparable
GPQA-D
Gemini 4 Argon—
GPT-5.492.8%
Source
Not directly comparable
HealthBench Hard
Gemini 4 Argon—
GPT-5.440.1%
Source
Not directly comparable
MedXpertQA (Text)
Gemini 4 Argon—
GPT-5.459.6%
Source
Not directly comparable
HealthBench Professional
Gemini 4 Argon—
GPT-5.448.1%
Source
Not directly comparable
GPQA Diamond (Vals)
Gemini 4 Argon—
GPT-5.491.7%
Source
Not directly comparable
MMLU-Pro (Vals)
Gemini 4 Argon—
GPT-5.487.5%
Source
Not directly comparable

Instruction following

Gray Swan IPI (15 attempts)
Gemini 4 Argon0.7%
Source
GPT-5.4—
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
Shared source
Gemini 4 Argon85.965%
GPT-5.478.596%
Gemini 4 Argon leads this result
FrontierMath v2 (Tier 4)
Shared source
Gemini 4 Argon78.049%
GPT-5.449.000%
Gemini 4 Argon leads this result

80 public results · 10 shared

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Last updated November 13, 2026

Gemini 4 Argon vs GPT-5.4

Decision reading

Which one for your work

Coding work

Agentic work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

Or describe your own job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Coding

Reasoning

Knowledge

Multimodal

Multilingual

Instruction following

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Your workload

Price against score, among all priced models

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, Gemini 4 Argon or GPT-5.4?

Which is better for coding, Gemini 4 Argon or GPT-5.4?

Which is better for agentic tasks, Gemini 4 Argon or GPT-5.4?

Which costs less, Gemini 4 Argon or GPT-5.4?

Which has the larger context window, Gemini 4 Argon or GPT-5.4?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Instruction following

Math

Related comparisons

Watch Gemini 4 Argon vs GPT-5.4