Gemini 4 Argon vs GPT-6 Astra

Updated September 30, 2026. Rank says GPT-6 Astra is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

GPT-6 Astra has the higher public score, 88.04 versus 64.59, 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

64.59/100

Estimated · Public rank #32

90% interval 53.1–76.1

Model B

GPT-6 Astra

OpenAI

88.04/100

Supported · Public rank #1

90% interval 83.3–92.8

Shared results: 10
Gemini 4 Argon only: 7
GPT-6 Astra only: 24
Like-for-like categories: 2 / 8

Estimated: Gemini 4 Argon · Supported: GPT-6 AstraHow 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
GPT-6 Astra
GPT-6 Astra leads on the public coding lane, 74.3 to 68.4, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
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

Show secondary and unsupported calls

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
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemini 4 Argon is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Not enough matched evidence
A complete context comparison is not sourced.
Confidence: limited

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.

68.4Gemini 4 Argon74.3GPT-6 Astra

Like-for-like · BenchAlign v5.7

GPT-6 Astra leads the like-for-like coding row, although the 90% intervals overlap.

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

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.

AutomationBenchAgentic
Normalized gap 9.9
Gemini 4 Argon:51.3%
GPT-6 Astra:41.4%
Gemini 4 Argon source GPT-6 Astra source
OSWorld 2.0Agentic
Normalized gap 3.4
Gemini 4 Argon:69.2%
GPT-6 Astra:72.6%
Gemini 4 Argon source GPT-6 Astra 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 v5.7 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.

Coding

Like-for-like

Gemini 4 Argon: 68.4; Supported · #8/144
GPT-6 Astra: 74.3; Supported · #4/144
Basis: BenchAlign v5.7 lane · 4 vs 5 public rows
Reading: GPT-6 Astra leads · intervals overlap

Knowledge

Like-for-like

Gemini 4 Argon: 72.9; Supported · #11/170
GPT-6 Astra: 86.6; Supported · #2/170
Basis: BenchAlign v5.7 lane · 1 vs 7 public rows
Reading: GPT-6 Astra leads · intervals overlap

Agentic

Directional only

Gemini 4 Argon: 63.7; Estimated · #14/119
GPT-6 Astra: 70.8; Supported · #5/119
Basis: BenchAlign v5.7 lane · 7 vs 11 public rows
Reading: Directional only

Reasoning

Not comparable

Gemini 4 Argon: 77.1; Unranked · 3 rankable rows
GPT-6 Astra: 89.6; #1/27
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Gemini 4 Argon: Not ranked
GPT-6 Astra: 83.9; Unranked · 3 rankable rows
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Gemini 4 Argon: Not ranked
GPT-6 Astra: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Gemini 4 Argon: Not ranked
GPT-6 Astra: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Gemini 4 Argon: Not ranked
GPT-6 Astra: 85.0; Unranked · 1 rankable row
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Gemini 4 Argon and GPT-6 Astra
Category	Gemini 4 Argon	GPT-6 Astra	Basis	Reading
Coding	68.4Supported · #8/144	74.3Supported · #4/144	Like-for-likeBenchAlign v5.7 lane · 4 vs 5 public rows	GPT-6 Astra leads · intervals overlap
Knowledge	72.9Supported · #11/170	86.6Supported · #2/170	Like-for-likeBenchAlign v5.7 lane · 1 vs 7 public rows	GPT-6 Astra leads · intervals overlap
Agentic	63.7Estimated · #14/119	70.8Supported · #5/119	Directional onlyBenchAlign v5.7 lane · 7 vs 11 public rows	Directional only
Reasoning	77.1Unranked · 3 rankable rows	89.6#1/27	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Multimodal	Not ranked	83.9Unranked · 3 rankable rows	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	Not ranked	85.0Unranked · 1 rankable row	Not comparableProvisional lane · 0 vs 1 weighted rows	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 4 Argon: $0.007; Fit state unavailable
GPT-6 Astra: $0.035; 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; Fit state unavailable
GPT-6 Astra: $0.65; 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; Fit state unavailable
GPT-6 Astra: $0.9; Fits in one request

Gemini 4 Argon has the lower modeled cost

Costs use the listed standard API rates.

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.

SpecificationGemini 4 ArgonGPT-6 Astra

Context window

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

Gemini 4 Argon

Not sourced

Google Gemini 4 Argon announcement

GPT-6 Astra

1.05M

OpenAI GPT-6 Astra model documentation

API model ID

Gemini 4 Argon

Not sourced

GPT-6 Astra

gpt-6-astra

OpenAI GPT-6 Astra model documentation

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 4 Argon announcement

GPT-6 Astra

$1 per 1M cached input tokens

OpenAI GPT-6 Astra model documentation

Documented inputs

Gemini 4 Argon

Not sourced

GPT-6 Astra

text, image

OpenAI GPT-6 Astra model documentation

Documented outputs

Gemini 4 Argon

Not sourced

GPT-6 Astra

text

OpenAI GPT-6 Astra model documentation

Provider availability

Gemini 4 Argon

Limited Access · Fairwind Program

Google Gemini 4 Argon announcement

GPT-6 Astra

Limited Availability · OpenAI Responses API, OpenAI Chat Completions API, ChatGPT Plus, Pro, Business, and Enterprise, Microsoft Azure, AWS Bedrock

OpenAI GPT-6 Astra model documentation

Reasoning profile

Gemini 4 Argon

Reasoning

GPT-6 Astra

Reasoning

Weight access

Gemini 4 Argon

Proprietary

GPT-6 Astra

Proprietary

License

Gemini 4 Argon

Proprietary

GPT-6 Astra

Proprietary

Release date

Gemini 4 Argon

2026-09-30

GPT-6 Astra

2026-09-03

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-6 Astra has the higher public score, 88.04 versus 64.59, and the 90% score intervals do not overlap.
Workload cost: Repository review: $0.13 vs $0.65. Cache-heavy agent loop: $0.16 vs $0.9.
Context tradeoff: A complete documented context comparison is not available.

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

Questions

Which is better, Gemini 4 Argon or GPT-6 Astra?

GPT-6 Astra has the higher public score, 88.04 versus 64.59, 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-6 Astra?

GPT-6 Astra leads the public coding lane, 74.3 to 68.4, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, Gemini 4 Argon or GPT-6 Astra?

GPT-6 Astra scores higher for agentic tasks on the public lane, 70.8 to 63.7. Gemini 4 Argon 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-6 Astra?

For the stated presets, chat costs $0.007 on Gemini 4 Argon and $0.035 on GPT-6 Astra; repository review costs $0.13 and $0.65; the cache-heavy agent loop costs $0.16 and $0.9. Costs use the listed standard API rates.

Which has the larger context window, Gemini 4 Argon or GPT-6 Astra?

A complete documented context-window comparison is not available.

Benchmark evidence

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

Browse raw public benchmark evidence41 rows

Agentic

AutomationBench
Gemini 4 Argon51.3%
Source
GPT-6 Astra41.4%
Source
Gemini 4 Argon leads this result
Finance Agent v2
Gemini 4 Argon65.4%
Source
GPT-6 Astra—
Not directly comparable
Terminal-Bench 4.0
Gemini 4 Argon57.40%
Source
GPT-6 Astra57.90%
Source
GPT-6 Astra leads this result
Agents' Last Exam
Gemini 4 Argon39.5%
Source
GPT-6 Astra59.3%
Source
GPT-6 Astra leads this result
OSWorld 2.0
Gemini 4 Argon69.2%
Source
GPT-6 Astra72.6%
Source
GPT-6 Astra leads this result
CWE-bench v1
Shared source
Gemini 4 Argon68.0%
GPT-6 Astra68.0%
Tie
Terminal-Bench-Science 0.1 (6x verifier timeout)
Gemini 4 Argon57.6%
Source
GPT-6 Astra—
Not directly comparable
BrowseComp
Gemini 4 Argon—
GPT-6 Astra91.5%
Source
Not directly comparable
Terminal-Bench-Science 0.1
Gemini 4 Argon—
GPT-6 Astra64.6%
Source
Not directly comparable
ExploitGym
Gemini 4 Argon—
GPT-6 Astra42.4%
Source
Not directly comparable
HLE w/ tools
Gemini 4 Argon—
GPT-6 Astra57.2%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 4 Argon—
GPT-6 Astra87.3%
Source
Not directly comparable
ApprenticeBench
Gemini 4 Argon—
GPT-6 Astra68%
Source
Not directly comparable

Coding

DeepSWE
Gemini 4 Argon77.9%
Source
GPT-6 Astra74.1%
Source
Gemini 4 Argon leads this result
FrontierSWE v2
Shared source
Gemini 4 Argon55.1%
GPT-6 Astra65.5%
GPT-6 Astra leads this result
Vibe Code Bench
Gemini 4 Argon91.90%
Source
GPT-6 Astra—
Not directly comparable
PostTrainBench v1.1
Gemini 4 Argon45.3%
Source
GPT-6 Astra44.3%
Source
Gemini 4 Argon leads this result
FrontierCode 1.1 Main
Gemini 4 Argon—
GPT-6 Astra53.3%
Source
Not directly comparable
FrontierCode 1.1 Extended
Gemini 4 Argon—
GPT-6 Astra64.5%
Source
Not directly comparable

Reasoning

Graphwalks BFS 128K
Gemini 4 Argon99.7%
Source
GPT-6 Astra—
Not directly comparable
GraphWalks BFS 256K–1M
Gemini 4 Argon84.2%
Source
GPT-6 Astra71.8%
Source
Gemini 4 Argon leads this result
ARC-AGI-1
Gemini 4 Argon—
GPT-6 Astra98.50%
Source
Not directly comparable
ARC-AGI-2
Gemini 4 Argon—
GPT-6 Astra95%
Source
Not directly comparable
ARC-AGI-3
Gemini 4 Argon—
GPT-6 Astra62.7%
Source
Not directly comparable
GeneBench-Pro
Gemini 4 Argon—
GPT-6 Astra37.8%
Source
Not directly comparable
MRCR v2 256K-512K
Gemini 4 Argon—
GPT-6 Astra100.0%
Source
Not directly comparable
MRCR v2 512K-1M
Gemini 4 Argon—
GPT-6 Astra96.3%
Source
Not directly comparable

Multimodal

Chartography (no tools)
Gemini 4 Argon71.6%
Source
GPT-6 Astra—
Not directly comparable
LVBench
Gemini 4 Argon91.7%
Source
GPT-6 Astra—
Not directly comparable
ScreenSpot Pro
Gemini 4 Argon—
GPT-6 Astra92.7%
Source
Not directly comparable
BenchCAD Vision2Code (tools)
Gemini 4 Argon—
GPT-6 Astra0.959
Source
Not directly comparable

Knowledge

LABBench2
Gemini 4 Argon88.8%
Source
GPT-6 Astra—
Not directly comparable
GPQA
Gemini 4 Argon—
GPT-6 Astra96%
Source
Not directly comparable
GPQA-D
Gemini 4 Argon—
GPT-6 Astra96.0%
Source
Not directly comparable
HealthBench (raw)
Gemini 4 Argon—
GPT-6 Astra56.9%
Source
Not directly comparable
HealthBench (length-adjusted)
Gemini 4 Argon—
GPT-6 Astra58.3%
Source
Not directly comparable
HealthBench Professional
Gemini 4 Argon—
GPT-6 Astra64.7%
Source
Not directly comparable
HealthBench Professional (raw)
Gemini 4 Argon—
GPT-6 Astra68.2%
Source
Not directly comparable
HealthBench Hard
Gemini 4 Argon—
GPT-6 Astra36.6%
Source
Not directly comparable

Instruction following

Gray Swan IPI (15 attempts)
Shared source
Gemini 4 Argon0.7%
GPT-6 Astra8.5%
Gemini 4 Argon leads this result

Math

FrontierMath v2 (Tier 4)
Gemini 4 Argon—
GPT-6 Astra97.600%
Source
Not directly comparable

41 public results · 10 shared

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Last updated September 30, 2026

Gemini 4 Argon vs GPT-6 Astra

Decision reading

Which one for your work

Coding work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Agentic work

Long documents

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Coding

Knowledge

Agentic

Reasoning

Multimodal

Multilingual

Instruction following

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

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-6 Astra?

Which is better for coding, Gemini 4 Argon or GPT-6 Astra?

Which is better for agentic tasks, Gemini 4 Argon or GPT-6 Astra?

Which costs less, Gemini 4 Argon or GPT-6 Astra?

Which has the larger context window, Gemini 4 Argon or GPT-6 Astra?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Instruction following

Math

Related comparisons

Watch Gemini 4 Argon vs GPT-6 Astra